T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E

Final Report
National Security Commission on Artificial Intelligencep2

COMMISSION MEMBERS
Eric Schmidt
Chair

Robert Work
Vice Chair

Safra Catz

Eric Horvitz

Steve Chien

Andrew Jassy

Mignon Clyburn

Gilman Louie

Chris Darby

William Mark

Kenneth Ford

Jason Matheny

José-Marie Griffiths

Katharina McFarland

Andrew Moore

LETTER FROM THE CHAIR AND VICE CHAIR

Letter from the Chair
and Vice Chair
Americans have not yet grappled with just how profoundly the artificial
intelligence (AI) revolution will impact our economy, national security,
and welfare. Much remains to be learned about the power and limitsof AI technologies. Nevertheless, big decisions need to be made now
to accelerate AI innovation to benefit the United States and to defend
against the malign uses of AI.
When considering these decisions, our leaders confront the classic dilemma of statecraft
identified by Henry Kissinger: “When your scope for action is greatest, the knowledge on
which you can base this action is always at a minimum. When your knowledge is greatest,
the scope for action has often disappeared.” The scope for action remains, but America’s
room for maneuver is shrinking.
As a bipartisan commission of 15 technologists, national security professionals, businessexecutives, and academic leaders, the National Security Commission on Artificial
Intelligence (NSCAI) is delivering an uncomfortable message: America is not prepared todefend or compete in the AI era. This is the tough reality we must face. And it is this realitythat demands comprehensive, whole-of-nation action. Our final report presents a strategy
to defend against AI threats, responsibly employ AI for national security, and win the
broader technology competition for the sake of our prosperity, security, and welfare. The
U.S. government cannot do this alone. It needs committed partners in industry, academia,
and civil society. And America needs to enlist its oldest allies and new partners to build asafer and freer world for the AI era.
AI is an inspiring technology. It will be the most powerful tool in generations for benefiting
humanity. Scientists have already made astonishing progress in fields ranging from
biology and medicine to astrophysics by leveraging AI. These advances are not science
fair experiments; they are improving life and unlocking mysteries of the natural world. They
are the kind of discoveries for which the label “game changing” is not a cliché.
AI systems will also be used in the pursuit of power. We fear AI tools will be weapons offirst resort in future conflicts. AI will not stay in the domain of superpowers or the realm ofscience fiction. AI is dual-use, often open-source, and diffusing rapidly. State adversariesp1

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are already using AI-enabled disinformation attacks to sow division in democracies and jar
our sense of reality. States, criminals, and terrorists will conduct AI-powered cyber attacks
and pair AI software with commercially available drones to create “smart weapons.” It
is no secret that America’s military rivals are integrating AI concepts and platforms tochallenge the United States’ decades-long technology advantage. We will not be able todefend against AI-enabled threats without ubiquitous AI capabilities and new warfighting
paradigms. We want the men and women in national security departments and agencies
to have access to the best technology in the world to defend themselves and us, and toprotect our interests and those of our allies and partners.
Despite exciting experimentation and a few small AI programs, the U.S. government is along way from being “AI-ready.” The Commission’s business leaders are most frustrated byslow government progress because they know it’s possible for large institutions to adopt
AI. AI integration is hard in any sector—and the national security arena poses some uniquechallenges. Nevertheless, committed leaders can drive change. We need those leadersin the Pentagon and across the Federal Government to build the technical infrastructure
and connect ideas and experimentation to new concepts and operations. By 2025, theDepartment of Defense and the Intelligence Community must be AI-ready.
We should embrace the AI competition. Competition already infuses the quests for data,
computing power, and the holy grail: the rare talent to make AI breakthroughs. The factthat AI courses through so many adjacent technologies and is leveraged across so many
fields explains its power and leads inexorably to another critical point: AI is part of abroader global technology competition. Competition will speed up innovation. We shouldrace together with partners when AI competition is directed at the moonshots that benefit
humanity like discovering vaccines. But we must win the AI competition that is intensifying
strategic competition with China. China’s plans, resources, and progress should concern
all Americans. It is an AI peer in many areas and an AI leader in some applications. We
take seriously China’s ambition to surpass the United States as the world’s AI leader withina decade.
The AI competition is also a values competition. China’s domestic use of AI is a chilling
precedent for anyone around the world who cherishes individual liberty. Its employmentof AI as a tool of repression and surveillance—at home and, increasingly, abroad—isa powerful counterpoint to how we believe AI should be used. The AI future can be
democratic, but we have learned enough about the power of technology to strengthen
authoritarianism abroad and fuel extremism at home to know that we must not take for
granted that future technology trends will reinforce rather than erode democracy. We must
work with fellow democracies and the private sector to build privacy-protecting standards
into AI technologies and advance democratic norms to guide AI uses so that democracies
can responsibly use AI tools for national security purposes.

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We would like to emphasize a few areas where action is necessary
because the stakes of the competition are so high:Leadership.
Ultimately, we have a duty to convince the leaders in the U.S. Government to make thehard decision and the down payment to win the AI era. In America, the buck stops with
the President, and AI strategy starts in the White House. We built a National Security
Council to confront the challenges of the post–World War II era. Now we need to createa Technology Competitiveness Council to build a strategy that accounts for the complex
security, economic, and scientific challenges of AI and its associated technologies. That
leadership imperative extends into all critical national security departments and agencies.
Talent.
The human talent deficit is the government’s most conspicuous AI deficit and the single
greatest inhibitor to buying, building, and fielding AI-enabled technologies for national
security purposes. This is not a time to add a few new positions in national security
departments and agencies for Silicon Valley technologists and call it a day. We need tobuild entirely new talent pipelines from scratch. We should establish a new Digital Service
Academy and civilian National Reserve to grow tech talent with the same seriousness of
purpose that we grow military officers. The digital age demands a digital corps. Just as
important, the United States needs to win the international talent competition by improving
both STEM education and our system for admitting and retaining highly skilled immigrants.
Hardware.
Microelectronics power all AI, and the United States no longer manufactures the world’s
most sophisticated chips. We do not want to overstate the precariousness of our position,
but given that the vast majority of cutting-edge chips are produced at a single plant
separated by just 110 miles of water from our principal strategic competitor, we mustreevaluate the meaning of supply chain resilience and security. A recent chip shortage forauto manufacturing cost an American car company an estimated $2.5 billion. A strategic
blockage would cost far more and put our security at risk. The federal investment andincentives needed to revitalize domestic microchip fabrication—perhaps $35 billion—
should be an easy decision when the alternative is relying on another country to produce
the engines that power the machines that will shape the future.

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Innovation Investment.
We worry that only a few big companies and powerful states will have the resources
to make the biggest AI breakthroughs. Despite the diffusion of open-source tools, theneeds for computing power and troves of data to improve algorithms are soaring at the
cutting edge of innovation. The federal government must partner with U.S. companies topreserve American leadership and to support development of diverse AI applications that
advance the national interest in the broadest sense. If anything, this report underplays theinvestments America will need to make. The $40 billion we recommend to expand anddemocratize federal AI research and development (R&D) is a modest down payment on
future breakthroughs. We will also need to build secure digital infrastructure across thenation, shared cloud computing access, and smart cities to truly leverage AI for the benefitof all Americans. We envision hundreds of billions in federal spending in the coming years.
This is not a time for abstract criticism of industrial policy or fears of deficit spending tostand in the way of progress. In 1956, President Dwight Eisenhower, a fiscally conservative
Republican, worked with a Democratic Congress to commit $10 billion to build theInterstate Highway System. That is $96 billion in today’s world. Surely we can make asimilar investment in the nation’s future.
We are proud of the NSCAI’s bipartisan work. We have debated together, learned together,and achieved consensus on critical points. It is our privilege to submit our recommendations
to Congress and the President. To paraphrase Winston Churchill, we are at the beginningof the beginning of the competition that will shape our prosperity, national security, and thewell-being of our citizens. Our report presents the first steps the United States should taketo defend, compete, and win in the AI era.

Eric Schmidt,
Chair

Bob Work,
Vice Chairp
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LETTER FROM THE EXECUTIVE DIRECTOR: THE BEGINNING OF THE BEGINNING

LETTER FROM THE EXECUTIVE DIRECTOR:

The Beginningof the Beginning
When we started our journey two years ago, little did we know what
was in front of us. What we encountered was willingness and hopeamong many friends and allies to get our mission from Congress rightto maintain the United States’ advantage in artificial intelligence (AI).
We enjoyed support from U.S. Departments and Agencies. Many of them loaned us
resources, including detailing both civilian and military personnel, and dedicated countlesshours to help us understand their missions and priorities. Members of Congress andcongressional staff worked closely with us to accelerate our government’s adoption of AI
for national security purposes.
Over the course of the Commission’s work, we engaged with hundreds of representatives
from the private sector, academia, civil society, and across the government. We received
countless briefings—classified and unclassified. We met with anyone who thinks about AI,
works with AI, and develops AI who was willing to make time for us.
We found consensus among nearly all of our partners on three points: the conviction that
AI is an enormously powerful technology, acknowledgement of the urgency to invest morein AI innovation, and responsibility to develop and use AI guided by democratic principles.

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We also talked to our allies—old and new. From New Delhi to Tel Aviv to London, there
was a willingness and desire to work with the United States to deepen cooperation on AI.
I am indebted to the many individuals who volunteered with us, interned with us, provided
expertise, and were friends of the Commission. I am particularly grateful to the dedicatedfull-time staff of the Commission, who in many cases stepped away from important jobs tojoin this essential mission.
In the last two years, we encountered widespread hope that AI could generate incredible
benefits for our nation’s economy, welfare, and security. We also heard concern that AI—
like any technology—could create new challenges and exacerbate existing problems. We
listened and took those concerns seriously.
We ultimately came away with a recognition that if America embraces and invests in AI
based on our values, it will transform our country and ensure that the United States and its
allies continue to shape the world for the good of all humankind.
Thank you!
Yll Bajraktarip6

EXECUTIVE SUMMARY

Executive Summary
No comfortable historical reference captures the impact of artificial
intelligence (AI) on national security. AI is not a single technology
breakthrough, like a bat-wing stealth bomber. The race for AI
supremacy is not like the space race to the moon. AI is not evencomparable to a general-purpose technology like electricity. However,what Thomas Edison said of electricity encapsulates the AI future: “Itis a field of fields … it holds the secrets which will reorganize the life of
the world.” Edison’s astounding assessment came from humility. All
that he discovered was “very little in comparison with the possibilities
that appear.”
The National Security Commission on Artificial Intelligence (NSCAI) humbly acknowledges
how much remains to be discovered about AI and its future applications. Nevertheless, weknow enough about AI today to begin with two convictions.
First, the rapidly improving ability of computer systems to solve problems and to performtasks that would otherwise require human intelligence—and in some instances exceed
human performance—is world altering. AI technologies are the most powerful tools ingenerations for expanding knowledge, increasing prosperity, and enriching the humanexperience. AI is also the quintessential “dual-use” technology. The ability of a machine
to perceive, evaluate, and act more quickly and accurately than a human represents acompetitive advantage in any field—civilian or military. AI technologies will be a source ofenormous power for the companies and countries that harness them.
Second, AI is expanding the window of vulnerability the United States has already entered.
For the first time since World War II, America’s technological predominance—the backboneof its economic and military power—is under threat. China possesses the might, talent,
and ambition to surpass the United States as the world’s leader in AI in the next decade if
current trends do not change. Simultaneously, AI is deepening the threat posed by cyberattacks and disinformation campaigns that Russia, China, and others are using to infiltrate
our society, steal our data, and interfere in our democracy. The limited uses of AI-enabled
attacks to date represent the tip of the iceberg. Meanwhile, global crises exemplified by
the COVID-19 pandemic and climate change highlight the need to expand our conceptionof national security and find innovative AI-enabled solutions.

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T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E“The NSCAI Final Reportpresents an integrated national
strategy to reorganize thegovernment, reorient the nation,and rally our closest allies andpartners to defend and compete
in the coming era of
AI-accelerated competitionand conflict.”

Given these convictions, the Commission concludes that the United States must act now
to field AI systems and invest substantially more resources in AI innovation to protect
its security, promote its prosperity, and safeguard the future of democracy. Today, thegovernment is not organizing or investing to win the technology competition against acommitted competitor, nor is it prepared to defend against AI-enabled threats and rapidly
adopt AI applications for national security purposes. This is not a time for incremental
toggles to federal research budgets or adding a few new positions in the Pentagon for
Silicon Valley technologists. This will be expensive and require a significant change inmindset. America needs White House leadership, Cabinet-member action, and bipartisan
Congressional support to win the AI era.
The NSCAI Final Report presents an integrated national strategy to reorganize thegovernment, reorient the nation, and rally our closest allies and partners to defend andcompete in the coming era of AI-accelerated competition and conflict. It is a two-pronged
approach. Part I, “Defending America in the AI Era,” outlines the stakes, explains what
the United States must do to defend against the spectrum of AI-related threats, andrecommends how the U.S. government can responsibly use AI technologies to protect
the American people and our interests. Part II, “Winning the Technology Competition,”addresses the critical elements of the AI competition and recommends actions thegovernment must take to promote AI innovation to improve national competitiveness andprotect critical U.S. advantages. The recommendations are designed as interlocking andmutually reinforcing actions that must be taken together.
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EXECUTIVE SUMMARYPart I: Defending America in the AI Era.
AI-enhanced capabilities will be the tools of first resort in a new era of conflict as strategiccompetitors develop AI concepts and technologies for military and other malign uses
and cheap and commercially available AI applications ranging from “deepfakes” to lethal
drones become available to rogue states, terrorists, and criminals. The United States must
prepare to defend against these threats by quickly and responsibly adopting AI for national
security and defense purposes. Defending against AI-capable adversaries operating
at machine speeds without employing AI is an invitation to disaster. Human operators
will not be able to keep up with or defend against AI-enabled cyber or disinformation
attacks, drone swarms, or missile attacks without the assistance of AI-enabled machines.
National security professionals must have access to the world’s best technology to protect
themselves, perform their missions, and defend us. The Commission recommends that the
government take the following actions:
Defend against emerging AI-enabled threats to America’s free and open society. Digitaldependence in all walks of life is transforming personal and commercial vulnerabilities
into potential national security weaknesses. Adversaries are using AI systems to enhancedisinformation campaigns and cyber attacks. They are harvesting data on Americans
to build profiles of their beliefs, behavior, and biological makeup for tailored attempts tomanipulate or coerce individuals. This gathering storm of foreign influence and interference
requires organizational and policy reforms to bolster our resilience. The government needs
to stand up a task force and 24/7 operations center to confront digital disinformation. It
needs to better secure its own databases and prioritize data security in foreign investment
screening, supply chain risk management, and national data protection legislation. The
government should leverage AI-enabled cyber defenses to protect against AI-enabledcyber attacks. And biosecurity must become a top-tier priority in national security policy.
Prepare for future warfare. Our armed forces’ competitive military-technical advantage
could be lost within the next decade if they do not accelerate the adoption of AI acrosstheir missions. This will require marrying top-down leadership with bottom-up innovation
to put operationally relevant AI applications into place. The Department of Defense (DoD)should:First, establish the foundations for widespread integration of AI by 2025. This includes
building a common digital infrastructure, developing a digitally-literate workforce, andinstituting more agile acquisition, budget, and oversight processes. It also requires
strategically divesting from military systems that are ill-equipped for AI-enabled warfare
and instead investing in next-generation capabilities.
Second, achieve a state of military AI readiness by 2025. Pentagon leadership must act
now to drive organizational reforms, design innovative warfighting concepts, establish
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architecture. DoD must also augment and focus its AI R&D portfolio. Readiness will also
require promoting AI interoperability with allies and partners.
Manage risks associated with AI-enabled and autonomous weapons. AI will enable newlevels of performance and autonomy for weapon systems. But it also raises important
legal, ethical, and strategic questions surrounding the use of lethal force. Provided theiruse is authorized by a human commander or operator, properly designed and tested AIenabled and autonomous weapon systems can be used in ways that are consistent withinternational humanitarian law. DoD’s rigorous, existing weapons review and targeting
procedures, including its dedicated protocols for autonomous weapon systems andcommitment to strong AI ethical principles, are capable of ensuring that the United States
will field safe and reliable AI-enabled and autonomous weapon systems and use them in a
lawful manner. While it is neither feasible nor currently in the interests of the United States
to pursue a global prohibition of AI-enabled and autonomous weapon systems, the global,
unchecked use of such systems could increase risks of unintended conflict escalation andcrisis instability. To reduce the risks, the United States should (1) clearly and publicly affirm
existing U.S. policy that only human beings can authorize employment of nuclear weaponsand seek similar commitments from Russia and China; (2) establish venues to discuss
AI’s impact on crisis stability with competitors; and (3) develop international standards ofpractice for the development, testing, and use of AI-enabled and autonomous weapon
systems.
Transform national intelligence. The Intelligence Community (IC) should adopt andintegrate AI-enabled capabilities across all aspects of its work, from collection to analysis.
Intelligence will benefit from AI more than any other national security mission. To capitalize
on AI, the Office of the Director of National Intelligence needs to empower and resource its
science and technology leaders. The entire IC should leverage open-source and publicly
available information in its analysis and prioritize collection of scientific and technicalintelligence. For better insights, intelligence agencies will need to develop innovative
approaches to human-machine teaming that use AI to augment human judgment.
Scale up digital talent in government. National security agencies need more digital experts
now or they will remain unprepared to buy, build, and use AI and associated technologies.
The talent deficit in DoD and the IC represents the greatest impediment to being AI-readyby 2025. The government needs new talent pipelines, including a U.S. Digital Service
Academy to train current and future employees. It needs a civilian National Digital Reserve
Corps to recruit people with the right skills—including industry experts, academics, andrecent college graduates. And it needs a Digital Corps, modeled on the Army Medical
Corps, to organize technologists already serving in government.
Establish justified confidence in AI systems. If AI systems routinely do not work as designed
or are unpredictable in ways that can have significant negative consequences, then leaders
will not adopt them, operators will not use them, Congress will not fund them, and thep
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American people will not support them. To establish justified confidence, the government
should focus on ensuring that its AI systems are robust and reliable, including throughresearch and development (R&D) investments in AI security and advancing human-AI
teaming through a sustained initiative led by the national research labs. It should alsoenhance DoD’s testing and evaluation capabilities as AI-enabled systems grow in number,scope, and complexity. Senior-level responsible AI leads should be appointed across thegovernment to improve executive leadership and policy oversight.
Present a democratic model of AI use for national security. AI tools are critical for U.S.
intelligence, homeland security, and law enforcement agencies. Public trust will hinge on
justified assurance that government use of AI will respect privacy, civil liberties, and civilrights. The government must earn that trust and ensure that its use of AI tools is effective,
legitimate, and lawful. This imperative calls for developing AI tools to enhance oversightand auditing, increasing public transparency about AI use, and building AI systems that
advance the goals of privacy preservation and fairness. It also requires ensuring that those
impacted by government actions involving AI can seek redress and have due process.
The government should strengthen oversight and governance mechanisms and establish
a task force to assess evolving concerns about AI and privacy, civil liberties, and civilrights.
Part II: Winning the Technology Competition.
The race to research, develop, and deploy AI and associated technologies is intensifying thetechnology competition that underpins a wider strategic competition. China is organized,
resourced, and determined to win this contest. The United States retains advantages
in critical areas, but current trends are concerning. While a competitive response iscomplicated by deep academic and commercial interconnections, the United States must
do what it takes to retain its innovation leadership and position in the world. The U.S.
government must embrace the AI competition and organize to win it by orchestrating andaligning U.S. strengths.
Organize with a White House–led strategy for technology competition. The UnitedStates must elevate AI considerations from the technical to the strategic level. Emerging
technologies led by AI now underpin our economic prosperity, security, and welfare. The
White House should establish a new Technology Competitiveness Council led by theVice President to integrate security, economic, and scientific considerations; develop acomprehensive technology strategy; and oversee its implementation.
Win the global talent competition. The United States risks losing the global competition for
scarce AI expertise if it does not cultivate more potential talent at home and recruit andretain more existing talent from abroad. The United States must move aggressively on both
fronts. Congress should pass a National Defense Education Act II to address deficiencies
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thousands of undergraduate- and graduate-level fellowships in fields critical to the AI
future. At the same time, Congress should pursue a comprehensive immigration strategy
for highly skilled immigrants to encourage more AI talent to study, work, and remain in the
United States through new incentives and visa, green card, and job-portability reforms.
Accelerate AI innovation at home. The government must make major new investments
in AI R&D and establish a national AI research infrastructure that democratizes accessto the resources that fuel AI development across the nation. The government should: (1)double non-defense funding for AI R&D annually to reach $32 billion per year by 2026,
establish a National Technology Foundation, and triple the number of National AI ResearchInstitutes; (2) establish a National AI Research Infrastructure composed of cloud computing
resources, test beds, large-scale open training data, and an open knowledge networkthat will broaden access to AI and support experimentation in new fields of science andengineering; and (3) strengthen commercial competitiveness by creating markets for AI
and by forming a network of regional innovation clusters.
Implement comprehensive intellectual property (IP) policies and regimes. The United States
must recognize IP policy as a national security priority critical for preserving America’s
leadership in AI and emerging technologies. This is especially important in light of China’s
efforts to leverage and exploit IP policies. The United States lacks the comprehensive
IP policies it needs for the AI era and is hindered by legal uncertainties in current U.S.
patent eligibility and patentability doctrine. The U.S. government needs a plan to reform IP
policies and regimes in ways that are designed to further national security priorities.
Build a resilient domestic base for designing and fabricating microelectronics. After
decades leading the microelectronics industry, the United States is now almost entirely
reliant on foreign sources for production of the cutting-edge semiconductors that power
all the AI algorithms critical for defense systems and everything else. Put simply: theU.S. supply chain for advanced chips is at risk without concerted government action.
Rebuilding domestic chip manufacturing will be expensive, but the time to act is now. The
United States should commit to a strategy to stay at least two generations ahead of China
in state-of-the-art microelectronics and commit the funding and incentives to maintainmultiple sources of cutting-edge microelectronics fabrication in the United States.
Protect America’s technology advantages. As the margin of U.S. technological advantage
narrows and foreign efforts to acquire American know-how and dual-use technologies
increase, the United States must reexamine how to best protect ideas, technology, andcompanies without unduly hindering innovation. The United States must:
First, modernize export controls and foreign investment screening to better protect
critical dual-use technologies—including by building regulatory capacity and fully
implementing recent legislative reforms, implementing coordinated export controls on
advanced semiconductor manufacturing equipment with allies, and expanding disclosure
requirements for investors from competitor nations.
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Second, protect the U.S. research enterprise as a national asset—by providing government
agencies, law enforcement, and research institutions with tools and resources to conductnuanced risk assessments and share information on specific threats and tactics,
coordinating research protection efforts with allies and partners, bolstering cybersecurity
support for research institutions, and strengthening visa vetting to limit problematic
research collaborations.
Build a favorable international technology order. The United States must work hand-inhand with allies and partners to promote the use of emerging technologies to strengthen
democratic norms and values, coordinate policies and investments to advance global
adoption of digital infrastructure and technologies, defend the integrity of international
technical standards, cooperate to advance AI innovation, and share practices and resources
to defend against malign uses of technology and the influence of authoritarian states indemocratic societies. The United States should lead an Emerging Technology Coalition
to achieve these goals and establish a Multilateral AI Research Institute to enhance theUnited States’ position as a global research hub for emerging technology. The Departmentof State should be reoriented, reorganized, and resourced to lead diplomacy in emerging
technologies.
Win the associated technologies competitions. Leadership in AI is necessary but not
sufficient for overall U.S. technological leadership. AI sits at the center of the constellationof emerging technologies, enabling some and enabled by others. The United States must
therefore develop a single, authoritative list of the technologies that will underpin national
competitiveness in the 21st century and take bold action to catalyze U.S. leadership in
AI, microelectronics, biotechnology, quantum computing, 5G, robotics and autonomous
systems, additive manufacturing, and energy storage technology. U.S. leadership acrossthese technologies requires investing in specific platforms that will enable transformational
breakthroughs and building vibrant domestic manufacturing ecosystems in each. At thesame time, the government will need to continuously identify and prioritize emerging
technologies farther over the horizon.

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Conclusion
This new era of competition promises to change the world we live inand how we live within it. We can either shape the change to come or
be swept along by it. We now know that the uses of AI in all aspectsof life will grow and the pace of innovation will continue to accelerate.
We know adversaries are determined to turn AI capabilities against
us. We know China is determined to surpass us in AI leadership. We
know advances in AI build on themselves and confer significant firstmover advantages. Now we must act. The principles we establish,
the federal investments we make, the national security applications
we field, the organizations we redesign, the partnerships we forge,
the coalitions we build, and the talent we cultivate will set America’s
strategic course. The United States should invest what it takes tomaintain its innovation leadership, to responsibly use AI to defend
free people and free societies, and to advance the frontiers of science
for the benefit of all humanity. AI is going to reorganize the world.
America must lead the charge.

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PR EFAC E

Preface
The National Security Commission on Artificial Intelligence’s (NSCAI)
task is to make recommendations to the President and Congress
to “advance the development of artificial intelligence [AI], machinelearning, and associated technologies to comprehensively address
the national security and defense needs of the United States.” Inestablishing the Commission, Section 1051 of the John S. McCain
National Defense Authorization Act for Fiscal Year 2019 instructs
NSCAI to examine AI through the lenses of national competitiveness,the means to sustain technological advantage, trends in international
cooperation and competitiveness, ways to foster investment in basic
and advanced research, workforce and training, potential risks ofmilitary use, ethical concerns, establishment of data standards andincentivization of data sharing, and the future evolution of AI.1
The 15 commissioners were nominated by Congress and the Executive Branch. They
represent a diverse group of technologists, business executives, academic leaders, andnational security professionals. They have approached all inquiries in bipartisan fashion
and reached consensus on the Final Report. The Commission’s operations have been
guided by two principles: the need for action and the importance of transparency.
Action.
The Commission’s work includes an initial report in July 2019, interim reports in November
2019 and October 2020, two additional quarterly memorandums, a series of special
papers in response to the COVID-19 pandemic, and now a final report. Waiting to deliver
recommendations in a final report was not an option when we began our work in the springof 2019. Assessing the broad national security implications of a dynamic technology like
AI at a single point in time is like trying to catch lightning in a bottle. Scientists continue
to deliver AI breakthroughs and the commercial sector is finding new ways to apply AI
at an accelerating pace. Competitors around the world are developing AI strategies andinvesting resources. The Commission delivered recommendations on a continuous basis,
aiming to match the speed of AI developments and the desires from the Executive Branchand Congress for help in deciding what to do. Congress has already adopted a number ofour recommendations in the William M. (Mac) Thornberry National Defense Authorization
Act for Fiscal Year 2021,2 and the Executive Branch has incorporated recommendationsp
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as well. And we have continuously sought to learn from and educate a wide range ofstakeholders to build a shared understanding about how AI will impact national security.
Transparency.
The NSCAI has been committed to transparency. As a Federal Advisory Committee, it has
held five public plenary sessions totaling approximately 15 hours of deliberations, streamed
live online, and archived meeting recordings on the NSCAI website. It has responded tomore than two dozen Freedom of Information Act requests and released more than 2,500
pages of material. NSCAI has posted more than 700 pages of draft materials for public
review and comment. With the exception of materials and issues classified for national
security reasons, the Commission has endeavored to offer full transparency. We haveproactively engaged with the media after every plenary session, quarterly report, andsubmission to Congress. In dozens of separate engagements, we have partnered with
non-governmental organizations, federal government organizations, and internationalorganizations to communicate our recommendations to the media and the public.
Most important, we have taken on the hardest issues with AI in public settings and maderecommendations only after consulting with a wide range of civil society, private sector,
and government groups. We have tried to listen and understand views across the spectrum
on deeply complicated aspects of AI. We have engaged ethicists, technologists, andnational security strategists. We have spoken with warriors and diplomats. We have talked
to academics and entrepreneurs. All told, commissioners and staff have participated inhundreds of discussions. As the commissioners built consensus on recommendations, weapproached issues with care and humility.
The Final Report.
The Final Report presents the NSCAI’s recommendations as a strategy for winning
the AI era. The 16 chapters in the Main Report provide topline recommendations. The
accompanying Blueprints for Action outline concrete steps that departments and agencies
can take to implement NSCAI recommendations. The Commission has provided as muchspecificity as possible—including by providing draft legislative text and executive orders—to help the President and Congress move rapidly from understanding AI to acting for the
benefit of the American people.
The Final Report represents an important step, but it is not the NSCAI’s final act. For
the remaining life of the Commission, our work will focus on implementation to help the
President and Congress make the investments and take the actions recommended to win
the AI era.

For full text, see Pub. L. 115-232, 132 Stat. 1636 (2018), https://www.congress.gov/115/bills/hr5515/
BILLS-115hr5515enr.pdf.

1

For full text, see Pub. L. 116-283, 134 Stat. 3388 (2021), https://www.congress.gov/bill/116th-congress/
house-bill/6395/text.

2

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TA B L E O F C O N T E N T S

Table of Contentsp17

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Introduction

19

Artificial Intelligence in Context

31

PART I: DEFENDING AMERICA IN THE AI ERA

41

Chapter 1: Emerging Threats in the AI Era

43

Chapter 2: Foundations of Future Defense

59

Chapter 3: AI and Warfare

75

Chapter 4: Autonomous Weapon Systems and Risks
Associated with AI-Enabled Warfare

89

Chapter 5: AI and the Future of National Intelligence107

Chapter 6: Technical Talent in Government

119

Chapter 7: Establishing Justified Confidence in AI Systems

131

Chapter 8: Upholding Democratic Values: Privacy, Civil Liberties,
and Civil Rights in Uses of AI for National Security

141

PART II: WINNING THE TECHNOLOGY COMPETITION

155

Chapter 9: A Strategy for Competition and Cooperation

157

Chapter 10: The Talent Competition

171

Chapter 11: Accelerating AI Innovation

183

Chapter 12: Intellectual Property

199

Chapter 13: Microelectronics

211

Chapter 14: Technology Protection

223

Chapter 15: A Favorable International Technology Order

241

Chapter 16: Associated Technologies

253

Blueprints for Action

271

Appendices

599
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Introduction
Artificial Intelligence (AI) technologies promise to be the most powerfultools in generations for expanding knowledge, increasing prosperity,and enriching the human experience. The technologies will be thefoundation of the innovation economy and a source of enormous
power for countries that harness them. AI will fuel competition
between governments and companies racing to field it. And it will be
employed by nation-states to pursue their strategic ambitions.
Americans have not yet seriously grappled with how profoundly the AI revolution will
impact society, the economy, and national security. Recent AI breakthroughs, such as acomputer defeating a human in the popular strategy game of Go1, shocked other nations
into action, but it did not inspire the same response in the United States. Despite our
private-sector and university leadership in AI, the United States remains unprepared forthe coming era. Americans must recognize the assertive role that the government will haveto play in ensuring the United States wins this innovation competition. Congress and thePresident will have to support the scale of public resources required to achieve it.
The magnitude of the technological opportunity coincides with a moment of strategic
vulnerability. China is a competitor possessing the might, talent, and ambition to challenge
America’s technological leadership, military superiority, and its broader position in theworld. AI is deepening the threat posed by cyber attacks and disinformation campaigns
that Russia, China, and other state and non-state actors are using to infiltrate our society,steal our data, and interfere in our democracy. The limited uses of AI-enabled attacks todate are the tip of the iceberg. Meanwhile, global crises exemplified in the global pandemic
and climate change are expanding the definition of national security and crying out for
innovative technological solutions. AI can help us navigate many of these new challenges.
We are fortunate. The AI revolution is not a strategic surprise. We are experiencing its
impact in our daily lives and can anticipate how research progress will translate into realworld applications before we have to confront the full national security ramifications. This
commission can warn of national security challenges and articulate the benefits, rather
than explain why previous warnings were ignored and opportunities were missed. We
still have a window to make the changes to build a safer and better future. The pace of AI
innovation is not flat; it is accelerating. If the United States does not act, it will likely lose
its leadership position in AI to China in the next decade and become more vulnerable to aspectrum of AI-enabled threats from a host of state and non-state actors.
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The Commission concludes that the United States needs to implement a strategy todefend and compete in the AI era. The White House must lead the effort to reorganize thegovernment and reorient the nation. This report presents the core elements of the strategy.
• Part I, “Defending America in the AI Era” (Chapters 1-8), outlines what the United States
must do to defend against the spectrum of AI-related threats from state and non-state
actors and recommends how the U.S. government can responsibly use AI technologies
to protect the American people and our interests.
• Part II, “Winning the Technology Competition” (Chapters 9-16), outlines AI’s rolein a broader technology competition. Each chapter addresses a critical element of
the competition and recommends actions the government must take to promote AI
innovation to improve national competitiveness and protect critical U.S. advantages.
Why Does AI Matter?
In 1901, Thomas Edison was asked to predict electricity’s impact on humanity. Two
decades after the development of the light bulb, he foresaw a general-purpose technology
of unlimited possibilities. “[Electricity] is the field of fields,” he said. “It holds the secrets
which will reorganize the life of the world.”2 AI is a very different kind of general-purpose
technology, but we are standing at a similar juncture and see a similarly wide-ranging
impact.3 The rapidly improving ability of computer systems to solve problems and to
perform tasks that would otherwise require human intelligence is transforming many
aspects of human life and every field of science. It will be incorporated into virtually all
future technology. The entire innovation base supporting our economy and security willleverage AI. How this “field of fields” is used—for good and for ill—will reorganize theworld.
The Commission’s assessment is rooted in a realistic understanding of AI’s current state ofdevelopment and a projection of how the technology will evolve.
AI is already ubiquitous in everyday life and the pace of innovation is accelerating. We takefor granted that AI already shapes our lives in ways small and big. A “smartphone” has multiple
AI-enabled features including voice assistants, photo tagging, facial recognition security,
search apps, recommendation and advertising engines, and less obvious AI enhancements
in its operating system. AI is helping predict the spread and escalation of a pandemic
outbreak, planning and optimizing the distribution of goods and services, monitoring traffic
flow and safety, speeding up drug and therapeutic discovery, and automating routine office
functions. Recognizing the pace of change is critical to understanding the power of AI. The
application of AI techniques to solve problems is compressing innovation timescales andturning once-fantastical ideas into realities across a range of disciplines.
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INTRODUCTION

Deploying and adopting AI remains a hard problem. AI cannot magically solve problems. As
AI moves from an elite niche science to a mainstream tool, engineering will be as importantas scientific breakthroughs. Early adopters across sectors have learned similar lessons:
Trying to employ AI is a slog even after the science is settled. Many of the most important
real-world impacts will come from figuring out how to employ existing AI algorithms andsystems, some more than a decade old. The integration challenge is immense. Harnessing
data, hardening and packaging laboratory algorithms so they are ready for use in the field,
and adapting AI software to legacy equipment and rigid organizations all require time, effort,
and patience. Integrating AI often necessitates overcoming substantial organizational andcultural barriers, and it demands top-down leadership.
AI tools are diffusing broadly and rapidly. Cutting-edge deep learning techniques are often
prohibitively expensive, requiring vast amounts of data, computing power, and specialized
knowledge. However, AI will not be the provenance of only big states and big tech. Manymachine learning tools that fuel AI applications are publicly available and usable even for
non-experts. Open-source applications and development tools combined with inexpensive
cloud computing and less data-intensive approaches are expanding AI opportunities
across the world to state and non-state actors.
AI is changing relationships between humans and machines. In modern society, we already
rely much more on machines and automation than we may be aware. The U.S. military, forinstance, has used autonomous systems for decades. However, as AI capabilities improve,the dynamics within human-machine “teams” will change. In the past, computers could
only perform tasks that fell within a clearly defined set of parameters or rules programmed
by a human. As AI becomes more capable, computers will be able to learn and performtasks based on parameters that humans do not explicitly program, creating choices andtaking actions at a volume and speed never before possible. Across many fields of humanactivity, AI innovations are raising important questions about what choices to delegate tointelligent machines, in what circumstances, and for what reasons. In the national security
sphere, these questions will take on greater significance as AI is integrated into defense
and intelligence systems. Across our entire society, we will need to address these newcomplexities with nuanced approaches, intellectual curiosity, and care that recognizes theincreasing ubiquity of AI.
Part I: Defending America in the AI Era.
Technology so ubiquitous in other facets of society will have an equivalent impact oninternational competition and conflict.4 We must adopt AI to change the way we defend
America, deter adversaries, use intelligence to make sense of the world, and fight and win
wars. The men and women who protect the United States must be able to leverage the AI
and associated technologies that can help them accomplish their missions as quickly andsafely as possible.

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AI is the quintessential “dual use” technology—it can be used for civilian and military
purposes. The AI promise—that a machine can perceive, decide, and act more quickly, ina more complex environment, with more accuracy than a human—represents a competitive
advantage in any field. It will be employed for military ends, by governments and non-stategroups.
We can expect the large-scale proliferation of AI-enabled capabilities. Many national
security applications of AI will require only modest resources and good, but not great,expertise to use. AI algorithms are often accessible. The hardware is “off-the-shelf” andin most cases generally available to consumers (as with graphics processing units, forexample). “Deepfake” capabilities can be easily downloaded and used by anyone.5 AIenabled tools and mutating malware are in the hands of hackers.6 Cheap, lethal drones will
be common. Azerbaijan’s use of Turkish drones and Israeli loitering munitions in combat
against Armenia in October 2020 confirmed that autonomous military capabilities are
spreading.7 Many states are watching and learning from these experiences. The likelihoodof reckless or unethical uses of AI-enabled technologies by rogue states, criminals, or
terrorists is increasing.
AI-enabled capabilities will be tools of first resort in a new era of conflict. State and non-state
actors determined to challenge the United States, but avoid direct military confrontation,
will use AI to amplify existing tools and develop new ones. Adversaries are exploiting ourdigital openness through AI-accelerated information operations and cyber attacks. Adtech will become natsec-tech as adversaries recognize what advertising and technology
firms have recognized for years: that machine learning is a powerful tool for harvesting
and analyzing data and targeting activities. Using espionage and publicly available data,adversaries will gather information and use AI to identify vulnerabilities in individuals,
society, and critical infrastructure. They will model how best to manipulate behavior, andthen act.
AI will transform all aspects of military affairs. AI applications will help militaries prepare,sense and understand, decide, and execute faster and more efficiently. Numerous weapon
systems will leverage one or more AI technologies. AI systems will generate options forcommanders and create battle networks connecting systems across all domains. It willtransform logistics, procurement, training, and the design and development of new hardware.
Adopting AI will demand the development of new tactics and operational concepts. In the
future, warfare will pit algorithm against algorithm. The sources of battlefield advantage will
shift from traditional factors like force size and levels of armaments to factors like superior
data collection and assimilation, connectivity, computing power, algorithms, and systemsecurity.
Competitors are actively developing AI concepts and technologies for military use. Russia
has plans to automate a substantial portion of its military systems.8 It has irresponsibly

deployed autonomous systems in Syria for testing on the battlefield.9 China sees AI as thep22

INTRODUCTION

path to offset U.S. conventional military superiority by “leapfrogging” to a new generationof technology. Its military has embraced “intelligentized war”––investing, for example, inswarming drones to contest U.S. naval supremacy.10 China’s military leaders talk openly
about using AI systems for “reconnaissance, electromagnetic countermeasures andcoordinated firepower strikes.”11 China is testing and training AI algorithms in militarygames designed around real-world scenarios. As these authoritarian states field new AIenabled military systems, we are concerned that they will not be constrained by the same
rigorous testing and ethical code that guide the U.S. military.
AI will revolutionize the practice of intelligence. There may be no national security function
better suited for AI adoption than intelligence tradecraft and analysis. Machines will sift
troves of data amassed from all sources, locate critical information, translate languages,
fuse data sets from different domains, identify correlations and connections, redirect assets,
and inform analysts and decision-makers. To protect the American people, perhaps themost urgent and compelling reason to accelerate the use of AI for national security is thepossibility that more advanced machine analysis could find and connect the dots before
the next attack, when human analysis alone may not see the full picture as clearly.
Defending against AI-capable adversaries without employing AI is an invitation to disaster.
AI will compress decision time frames from minutes to seconds, expand the scale of attacks,and demand responses that will tax the limits of human cognition. Human operators will not
be able to defend against AI-enabled cyber or disinformation attacks, drone swarms, or
missile attacks without the assistance of AI-enabled machines. The best human operator
cannot defend against multiple machines making thousands of maneuvers per second
potentially moving at hypersonic speeds and orchestrated by AI across domains. Humans
cannot be everywhere at once, but software can.
Compelling logic dictates quick, but careful and responsible, AI adoption. The government
should adopt AI following the principle of legendary basketball coach John Wooden: “Be
quick, but don’t hurry.”12 Like other “safety critical” applications of AI, military and intelligence
functions require deliberation and caution before they are developed and fielded. Somecurrent AI systems are narrow and brittle. All require rigorous testing, safeguards, and anunderstanding of how they might operate differently in the real world than in a testbed.
AI-enabled autonomous weapon systems could be more precise, and as a result, reduce
inadvertent civilian casualties. But they also raise important ethical questions about therole of human judgment in employing lethal force. If improperly designed or used, they
could also increase the risk of military escalation.
There is an emerging consensus on principles for using AI responsibly in the defense andintelligence communities.13 If an AI-powered machine does not work as designed withpredictability and guided by clear principles, then operators will not use it, organizations
will not embrace it, and the American people will not support it. Hurrying would becounterproductive and dangerous if it caused Americans to lose confidence in the

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“

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E“The best human operator
cannot defend against multiple
machines making thousandsof maneuvers per second
potentially moving at hypersonic
speeds and orchestrated by AI
across domains. Humans cannot
be everywhere at once, but
software can.”benefits AI could confer. Risk, however, is inescapable. Failing to use AI to solve real
national security challenges risks putting the United States at a disadvantage, leaving
American service members more vulnerable, and spending taxpayer money unwisely on
antiquated and inefficient equipment. Delaying AI adoption will push all of the risk onto thenext generation of Americans—who will have to defend against, and perhaps fight, a 21st
century adversary with 20th century tools.
The U.S. government still operates at human speed, not machine speed. Adopting AI
requires profound adjustments in national security business practices, organizational
cultures, and mindsets from the tactical to the strategic levels—from the battlefield to
the Pentagon. The government lags behind the commercial state of the art in most AI
categories, including basic business automation. It suffers from technical deficits that range
from digital workforce shortages to inadequate acquisition policies, insufficient networkarchitecture, and weak data practices. Bureaucracy is thwarting better partnerships with
the AI leaders in the private sector that could help. The government must become a better
customer and a better partner. National security innovation, in the absence of an impetus
like a major war or terrorist attack, will require strong leadership.
Part II: Winning the Technology Competition.
In addition to AI’s narrow national security and defense applications, AI is the fulcrum of abroader technology competition in the world. AI will be leveraged to advance all dimensionsp24

INTRODUCTION

of national power, from healthcare to food production to environmental sustainability. The
successful adoption of AI in adjacent fields and technologies will drive economies, shapesocieties, and determine which states exert influence and exercise power in the world.
Many countries have national AI strategies, but only the United States and China have theresources, commercial might, talent pool, and innovation ecosystem to lead the world in
AI. In some areas of research and applications, China is already an AI peer, and it is more
technically advanced in some applications.14 Within the next decade, China could surpass
the United States as the world’s AI superpower.15
On a level playing field, the United States is capable of out-innovating any competitor.
However, today, there is a fundamental difference in the U.S. and China’s approaches to
AI innovation that puts American AI leadership in peril. For decades, the U.S. innovation
model has been the envy of the world. The open exchange of ideas, free markets, andlimited government involvement to support basic research are pillars of the American way
of innovation and reflect American values. In America, tech firms compete for market share.
They are not instruments of state power. Researchers collaborate in an open research
environment in competition with their peers to make AI breakthroughs without regard forborders. The international flow of venture capital and AI-related commerce is encouraged
as firms compete for profits and the next big idea.
Most AI progress in the United States should remain with the private sector and universities.
We must not lose an innovation culture that is bottom-up and infused with a garage-startup
mentality. However, a fully distributed approach is not a winning strategy in this strategiccompetition. Even large tech firms cannot be expected to compete with the resourcesof China or make the big investments the U.S. will need to stay ahead. We will need ahybrid approach meshing government and private-sector efforts to win the technology
competition.
China is organized, resourced, and determined to win the technology competition. AI iscentral to China’s global expansion, economic and military power, and domestic stability.
It has a head start on executing a national AI plan as part of larger plans to lead the world
in several critical and emerging technology fields. Beginning in 2017, China established
AI goals, objectives, and strategies tied to specific timelines with resources backed bycommitted leadership to lead the world in AI by 2030.16 China is executing a centrally
directed systematic plan to extract AI knowledge from abroad through espionage, talent
recruitment, technology transfer, and investments. It has ambitious plans to build and train
a new generation of AI engineers in new AI hubs. It supports “national champion” firms(including Huawei, Baidu, Alibaba, Tencent, iFlytek, and SenseTime) to lead developmentof AI technologies at home, advance state-directed priorities that feed military and security
programs under the rubric of military-civil fusion, and capture markets abroad.17 It funds
massive digital infrastructure projects across several continents. China developed an
intellectual property (IP) strategy and is trying to set global technical standards for AI
development.18 And its laws make it all but impossible for a company in China to shield its
data from the authorities.19
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Advancements in AI are contributing to a broad platform technology competition in
e-commerce, search engines, social media, and much else. The countries, companies,
and researchers that win the AI competition—in computing, data, talent, andcommercialization—will be positioned to win a much larger game. In essence, more
and better data, fed by a larger consumer/participant base, produce better algorithms,
which produce better results, which in turn produces more users, more data, and better
performance—until, ultimately, fewer companies will become entrenched as the dominant
platforms. If China’s firms win these competitions, it will not only disadvantage U.S.
commercial firms, it will also create the digital foundation for a geopolitical challenge to theUnited States and its allies. Platform domination abroad allows China to harvest the data of
its users and permits China to extend aspects of its domestic system of control. Wherever
China controls the digital infrastructure, social media platforms, and e-commerce, it would
possess greater leverage and power to coerce, propagandize, and shape the world toconform to its goals.
The AI competition is complicated by deep interconnections. The United States and China
are not operating in parallel lanes like the Soviets and Americans did in the space race,with disconnected research and development (R&D) enterprises and minimal commercial
contacts. The research ecosystems in China and the United States are deeply connected
through shared research projects, talent circulation (particularly from China to the UnitedStates), and commercial linkages that include supply chains, markets, and joint research
ventures. It would be counterproductive to sever the technology ties to China that benefit
basic research and U.S. companies. However, the United States must protect the integrityof open research, prevent the theft of American IP, and employ targeted tools like export
controls and investment screening to protect technology industries critical to national
security.
The United States retains advantages in critical areas, but trends are concerning. The
world’s best scientific talent is more likely to stay home or migrate elsewhere today
than in our recent past.20 The U.S. lead in microelectronics—the hardware on which all
AI runs—has diminished, and for cutting-edge chips it is dependent on foreign supply
chains and manufacturers in Asia that are vulnerable to coercion or disruption.21 While

many machine learning tools are widely available and per-unit computing costs havedeclined, the computing power and data access needed for cutting-edge deep learning
research breakthroughs are making it harder for university-based researchers and smaller
companies to compete.22 The geography of innovation remains concentrated in only someparts of the country.23
The U.S. government must take a hands-on approach to national technology
competitiveness. Promoting a diverse and resilient R&D ecosystem and commercial sector
is a government responsibility. Expanding talent pipelines to attract the world’s best andredoubling efforts to educate AI-ready Americans are public policy choices. Judiciously,
but aggressively, protecting critical AI intellectual property and thwarting the systemicp26

INTRODUCTION

campaign of illicit knowledge transfer being conducted by competitors is a government
obligation. Protecting hardware advantages and building resiliency into supply chainsnecessitate legislation and federal incentives. Bringing together like-minded allies andpartners to build an international coalition that ensures a democratic vision for AI that will
shape the digital future requires U.S.-led diplomacy.
The AI competition will require White House leadership. The critical elements of the strategy
are too complicated for any one department or agency to lead because they cut acrossnational security, economic, and technology policy. Only strong executive leadership fromthe White House can drive policy, force tradeoffs, and mobilize the country to make thenecessary investments.
AI for What Ends? Technology and Values.
The widespread adoption of AI by governments around the world is impacting not only theinternational order among states, but also the political order within them. The stakes of the AI
future are intimately connected to the enduring contest between authoritarian and democratic
political systems and ideologies.
Technology itself does not possess an ideology, but how it is designed, where it is employed,
and which laws govern its use reflect the priorities and values of those who design and employ
it. More AI-enabled surveillance and analysis capabilities will soon be in the hands of most or all
governments. As the technology diffuses, the main difference between states will have less to
do with the quality or sophistication of the technology and more to do with the way it is used—
for what purpose, and under what rules.
Authoritarian regimes will continue to use AI-powered face recognition, biometrics, predictive
analytics, and data fusion as instruments of surveillance, influence, and political control. China’s
use of AI-powered surveillance technologies to repress its Uyghur minority and monitor all of its
citizens foreshadows how authoritarian regimes will use AI systems to facilitate censorship, track
the physical movements and digital activities of their citizens, and stifle dissent.24 The global
circulation of these digital systems creates the prospect of a wider adoption of authoritarian
governance. But liberal democracies also employ AI for internal security and public safety
purposes. More than half of the world’s advanced democracies use AI-enabled surveillancesystems.25 Such technologies have legitimate public purposes and are compatible with the ruleof law. Yet in states edging toward illiberal practices, utilizing digital tools in ways that undermine
the rule of law could tip the scales toward further democratic backsliding. The preservation ofindividual liberties calls for continued vigilance. A responsible democracy must ensure that theuse of AI by the government is limited by wise restraints to comport with the rights and liberties
that define a free and open society.
The U.S. government should develop and field AI-enabled technologies with adequatetransparency, strong oversight, and accountability to protect against misuse. Merely stating U.S.

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opposition to the authoritarian use of AI is not enough. The United States must also demonstrate
how a democracy should use AI to protect the security of its citizens in ways that uphold liberal
democratic values. There is an urgent need to field AI for national security purposes against, forinstance, foreign and domestic terrorists operating within our borders. There is also an enduringneed to ensure that security applications of AI conform to core values of individual liberty andequal protection under law.
The United States must lead a coalition of democracies. As we ensure that AI is developed
and used in ways that are safe for democracy at home, we must also promote global norms
to make its use safe for democracy abroad. While the U.S. government’s ability to influence
the governance practices of other states is limited, a strong plank of the U.S. foreign policy
agenda with respect to AI must be to promote human rights and counter techno-authoritarian
trends. The United States can use diplomacy and leverage its global partnerships to advocate
for establishing privacy-protecting technical standards and norms in international bodies,and it can work with like-minded nations to ensure that other nations have an alternative toembracing China’s technology and methods of social control and access to technologies
that protect democratic values like privacy. We do not seek a fragmented digital world. We
want the United States and its allies to exist in a world with a diverse set of choices in digital
infrastructure, e-commerce, and social media that will not be vulnerable to authoritarian coercion
and that support free speech, individual rights, privacy, and tolerance for differing views.

Conclusion
We are at the beginning of the beginning of this new era of competition.
We now know the uses of AI in all aspects of life will grow and the paceof innovation will accelerate. We know adversaries are determined toturn AI capabilities against us. We know a competitor is determined tosurpass us in AI leadership. We know AI is accelerating breakthroughs
in a wide array of fields. We know that whoever translates AI
developments into applications first will have the advantage. Now
we must act. The principles we establish, the federal investments we
make, the national security applications we field, the organizations
we redesign, the partnerships we forge, the coalitions we build, andthe talent we cultivate will set America’s strategic course. The UnitedStates should invest what it takes to maintain its innovation leadership,
to responsibly use AI to defend free people and free societies, and toadvance the frontiers of science for the benefit of all humanity. AI isgoing to reorganize the world. America must lead the charge.
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Introduction - Endnotes
The Google DeepMind Challenge Match, DeepMind (last accessed Jan. 7, 2021), https://deepmind.
com/alphago-korea.

1

Quoted in Orison Swett Marden, How They Succeeded: Life Stories of Successful Men Told by
Themselves, Lothrop Publishing Co. at 238 (1901).

2

Andrew Ng is widely credited with making this comparison. See e.g., Shana Lynch, Andrew Ng: Why
AI Is the New Electricity, Insights by Stanford Business (March 11, 2017), https://www.gsb.stanford.
edu/insights/andrew-ng-why-ai-new-electricity.

3

For an overview of AI and international relations see Michael Horowitz, Artificial Intelligence,
International Competition, and the Balance of Power, Texas National Security Review (May 2018),
https://doi.org/10.15781/ T2639KP49.

4

5
Karen Hao & Will Douglas Heaven, The Year Deepfakes Went Mainstream, MIT Technology Review(Dec. 24, 2020), https://www.technologyreview.com/2020/12/24/1015380/best-ai-deepfakes-of-2020/.

Nicholas Duran, et al., 2018 Webroot Threat Report, Webroot (2018), https://www-cdn.webroot.
com/9315/2354/6488/2018-Webroot-Threat-Report_US-ONLINE.pdf; Implications of Artificial
Intelligence for Cybersecurity: Proceedings of a Workshop, National Academies of Sciences,
Engineering, & Medicine (2019), https://www.nap.edu/catalog/25488/implications-of-artificialintelligence-for-cybersecurity-proceedings-of-a-workshop; Ben Buchanan, et al., Automating Cyber
Attacks: Hype and Reality, Center for Security and Emerging Technology (Nov. 2020), https://cset.
georgetown.edu/research/automating-cyber-attacks/; Deep Exploit: Fully Automatic Penetration
Test Tool Using Deep Reinforcement Learning, GitHub (last accessed Jan. 9, 2021), https://github.
com/13o-bbr-bbq/machine_learning_security/tree/master/DeepExploit.

6

Robyn Dixon, Azerbaijan’s Drones Owned the Battlefield in Nagorno-Karabakh—and Showed Futureof Warfare, Washington Post (Nov. 11, 2020), https://www.washingtonpost.com/world/europe/nagornokarabkah-drones-azerbaijan-aremenia/2020/11/11/441bcbd2-193d-11eb-8bda-814ca56e138b_story.
html.

7

Vadim Kozyulin, Militarization of AI, Stanley Center for Peace and Security (July 2019), https://
stanleycenter.org/wp-content/uploads/2020/05/MilitarizationofAI-Russia.pdf.

8Dylan Malyasov, Combat Tests in Syria Brought to Light Deficiencies of Russian Unmanned Minitank, Defence Blog (June 18, 2018), https://defence-blog.com/news/army/combat-tests-syria-broughtlight-deficiencies-russian-unmanned-mini-tank.html.

910
Testimony of Elsa Kania before the U.S.-China Economic and Security Review Commission, Hearing
on Technology, Trade, and Military-Civil Fusion (June 7, 2019), https://www.uscc.gov/sites/default/
files/June%207%20Hearing_Panel%201_Elsa%20Kania_Chinese%20Military%20Innovation%20in%20
Artificial%20Intelligence_0.pdf; Elsa Kania, “AI Weapons” in China’s Military Innovation, Brookingsat 1 (April 20, 2020), https://www.brookings.edu/wp-content/uploads/2020/04/FP_ 20200427_ai_
weapons_kania_v2.pdf.

Marcus Clay, The PLA’s AI Competitions, The Diplomat (Nov. 5, 2020), https://thediplomat.
com/2020/11/the-plas-ai-competitions/.
11

12
Andrew Hill & John Wooden, Be Quick— But Don’t Hurry: Finding Success in the Teachings of a
Lifetime, Simon & Schuster at 69 (2001).
13
Press Release, U.S. Department of Defense, DoD Adopts Ethical Principles for Artificial Intelligence(Feb. 24, 2020), https://www.defense.gov/Newsroom/Releases/Release/Article/2091996/dodadopts-ethical-principles-for-artificial-intelligence/; Principles of Artificial Intelligence Ethics for
the Intelligence Community, ODNI (last accessed Jan. 11, 2021), https://www.dni.gov/index.php/
features/2763-principles-of-artificial-intelligence-ethics-for-the-intelligence-community.
14
Graham Allison & Eric Schmidt, Is China Beating the U.S. to AI Supremacy?, Belfer Center for
Science and International Affairs (Aug. 2020), https://www.belfercenter.org/publication/china-beatingus-ai-supremacy.

See e.g., Alexandra Mousavizadeh, et al., The Global AI Index, Tortoise Media (Dec. 3, 2019),
https://www.tortoisemedia.com/2019/12/03/global-ai-index/.

15

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See Graham Webster, et al., Full Translation: China’s ‘New Generation Artificial Intelligence
Development Plan,’ New America (Aug. 1, 2017), https://www.newamerica.org/cybersecurity-initiative/
digichina/blog/full-translation-chinas-new-generation-artificial-intelligence-development-plan-2017/(translating China’s State Council Notice on the Issuance of the New Generation Artificial Intelligence
Development Plan, dated July 20, 2017).

16

Benjamin Larsen, Drafting China’s National AI Team for Governance, New America (Nov. 18, 2019),
https://www.newamerica.org/cybersecurity-initiative/digichina/blog/drafting-chinas-national-ai-teamgovernance/ (originally published in Graham Webster, ed., AI Policy and China: Realities of StateLed Development, Stanford-New America DigiChina Project (Oct. 29, 2019), https://d1y8sb8igg2f8e.
cloudfront.net/documents/DigiChina-AI-report-20191029.pdf); Meng Jing, China to Boost Its ‘National
Team’ to Meet Goal of Global AI Leadership by 2030, South China Morning Post (Nov. 15, 2018),
https://www.scmp.com/tech/innovation/article/2173345/china-boost-its-national-team-meet-goalglobal-ai-leadership-2030; Gregory C. Allen, Understanding China’s AI Strategy, Center for a New
American Security (Feb. 6, 2019), https://www.cnas.org/publications/reports/understanding-chinasai-strategy (“The price of SenseTime and the other AI Champions being allowed to dominate thesetechnologies is the Champions’ extensive cooperation with China’s national security community. Even
beyond direct cooperation, China’s success in commercial AI and semiconductor markets brings
funding, talent, and economies of scale that both reduce China’s vulnerability from losing access tointernational markets and offer useful technology for the development of weaponry and espionage
capabilities.”).

17

18
Emily de La Bruyère & Nathan Picarsic, China Standards 2035, Horizon Advisory (April 2020),
https://www.horizonadvisory.org/china-standards-2035-first-report.

Murray Scot Tanner, Beijing’s New National Intelligence Law: From Defense to Offense, Lawfare(July 20, 2017), https://www.lawfareblog.com/beijings-new-national-intelligence-law-defense-offense.

19

20
Remco Zwetsloot, China’s Approach to Tech Talent Competition: Policies, Results, and theDeveloping Global Response, Brookings Institution (April 2020), https://www.brookings.edu/wpcontent/uploads/2020/04/FP_ 20200427_china_talent_policy_zwetsloot.pdf.
21“Ninety percent of all high-volume, leading-edge [semiconductor] production will soon be based in
Taiwan, China, and South Korea.” The Department of Defense estimates that by 2022 only 8% of all
semiconductor fabrication will occur in the United States, “down from 40% in the 1990s.” Michaela D.
Platzer, et al., Semiconductors: U.S. Industry, Global Competition, and Federal Policy, Congressional
Research Service at 12 (Oct. 26, 2020), https://fas.org/sgp/crs/misc/R46581.pdf (quoting Rick
Switzer, U.S. National Security Implications of Microelectronics Supply Chain Concentration in Taiwan,
South Korea, and the People’s Republic of China, U.S. Air Force [Sept. 2019]).
22
For example, non-elite universities and AI startups have difficulty affording the cost of computeresources and data for training sophisticated machine learning (ML) models. Nur Ahmed & Muntasir
Wahed, The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence
Research, arXiv (Oct. 22, 2020), https://arxiv.org/abs/2010.15581. The need to bolster the nationwide
AI infrastructure is the first recommendation in the 20-Year Roadmap issued by the Association for the
Advancement of Artificial Intelligence. See A 20-Year Community Roadmap for Artificial Intelligence
Research in the US, Computing Community Consortium and AAAI, at 3 (August 2019), https://cra.org/
ccc/wp-content/uploads/sites/2/2019/08/Community-Roadmap-for-AI-Research.pdf.

More than 90% of U.S. innovation-sector job creation occurred in just five major coastal cities
between 2005 and 2017. Robert D. Atkinson, et al., The Case for Growth Centers: How to Spread Tech
Innovation Across America, Brookings (Dec. 9, 2019), https://www.brookings.edu/research/growthcenters-how-to-spread-tech-innovation-across-america/.

23

See, e.g., Patrice Taddonio, How China’s Government Is Using AI on Its Uighur Muslim Population,
PBS Frontline (Nov. 21, 2019), https://www.pbs.org/wgbh/frontline/article/how-chinas-governmentis-using-ai-on-its-uighur-muslim-population/; Bethany Allen-Ebrahimian, Exposed: China’s Operating
Manuals for Mass Internment and Arrest by Algorithm, International Consortium of Investigative
Journalists (Nov. 24, 2019), https://www.icij.org/investigations/china-cables/exposed-chinasoperating-manuals-for-mass-internment-and-arrest-by-algorithm/.

24

See Steven Feldstein, The Global Expansion of AI Surveillance, Carnegie Endowment forInternational Peace (Sept. 2019), https://carnegieendowment.org/2019/09/17/global-expansion-of-aisurveillance-pub-79847.

25

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ARTIFICIAL INTELLIGENCE IN CONTEXTArtificial Intelligencein Context

Graphic 0.4: AI in Context.

Artificial
Intelligence
Context
Artificial intelligence(AI) is not a insinglepiece of hardware or software, but rather aconstellation of technologies that depend on interrelated elements that can be envisioned

Artificial intelligence (AI) is not a single piece of hardware or software, but rather a constellation of technologies that depend on interrelated elementsthat can be
as a stack.
asenvisioned
a stack.

Integration
Applications
Algorithms

Computer
VisionPlanning &Optimization

Hardware
Data

Prediction

Uses for
Deployed AI
TodayModeling &Simulation

TalentRobotic
Process Automation

Novel
Learning

Natural Language
Understanding

Edge
ComputingResearch
Frontiers

Advanced
Reasoning

Education

Human-AI
Teaming
More
General AI

Science

AI-Enabled
FutureSmart Cities

Healthcare

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Space

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E

Artificial Intelligence (AI) is not a single piece of hardware or software,
but rather a constellation of technologies. To address such a broad
topic, the Commission’s legislative mandate provided guidanceon how to scope its work to include technologies that solve tasks
requiring human-like perception, cognition, planning, learning,communication, or physical action; and technologies that may learn
and act autonomously, whether in the form of software agents or
embodied robots.1
Successful development and fielding of AI technologies depends on a number ofinterrelated elements that can be envisioned as a stack.2 AI requires talent, data,
hardware, algorithms, applications, and integration. We regard talent as the most essential
requirement because it drives the creation and management of all the other elements.
Data is critical for most AI systems.3 Labeled and curated data enables much of current
machine learning (ML) used to create new applications and improve the performanceof existing AI applications. The underlying hardware provides the computing power toanalyze ever-growing data pools and run applications. This hardware layer includes cloudbased compute and storage, supported by a networking and communications backbone,
instrumental for connecting smart sensors and devices at the network edge. Algorithms
are the mathematical operations that tell the system how to navigate the data to provide
answers in response to specific questions. An application makes the answers useful for
specific tasks. Integration of these elements is critical to fielding a successful end-toend AI system. This requires significant engineering talent and investment to integrate
existing data flows, decision pipelines, legacy equipment, testing designs, etc. This taskof integration can be daunting and historically has been underestimated.4
AI technologies and applications such as pattern recognition, ML, computer vision, natural
language understanding, and speech recognition have evolved for many decades. In theearly years of AI, the period the Defense Advanced Research Projects Agency (DARPA)
describes as the “first wave,” researchers explored many approaches, including symbolic
logic, expert systems, and planning. Some of the most effective results were based on“handcrafted knowledge” defined by humans and then used by the machine for reasoning
and interacting.5
Within the past 10 years, we have witnessed a “second wave” of AI, propelled by largescale statistical ML that enables engineers to create models that can be trained to
specific problem domains if given exemplar data or simulated interactions. Learning from
data, these systems are designed to solve specific tasks and achieve particular goals
with competencies that, in some respects, parallel the cognitive processes of humans:
perceiving, reasoning, learning, communicating, deciding, and acting. Today most fielded
large-scale AI systems employ elements of both first- and second-wave AI approaches.

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ARTIFICIAL INTELLIGENCE IN CONTEXTAge of Deployed AI.
Today, we have reached an inflection point. Global digital transformation has led to an
overwhelming supply of data. Statistical ML algorithms, particularly deep neural networks,
have matured as problem solvers—albeit with limitations.6 The powerful and networked
computing that fuels ML capabilities has become widely available. The convergence of
these factors now places this capable technology in the hands of the technical and nontechnical alike. The fundamental “question is no longer how this technology works, butwhat it can do for you.”7
While the current technology still has significant limitations, it is well-suited for certain use
cases. We have entered the age of deployed AI. AI is now ubiquitous, embedded in devices
Graphic 0.1: Current Application of AI in Key Areas.
we use and interact with on a daily basis—for example, in our smartphones, wirelessrouters, and cars. We routinely rely on AI-enriched applications, whether searching for a
new restaurant, navigating traffic, selecting a movie, or getting customer service over thephone or online.
Current A pplicationof AI in Key A reas.

Prediction
Event forecasting and patternanalysis have impacted nearly allindustries (e.g., finance, farming,and transportation).

Natural Language Understanding (NLU)
Examples:Precision
Agriculture

Current
Application of
AI in Key Areas

Planning and Optimization
Determining
necessary steps tocomplete a series oftasks can save timeand money, andimprove safety.

Machines are able toprocess, analyze,
understand, and mimic
human language, either
spoken or written.

Preventative
VehicleMaintenance

Example:Transportation
Planning
in Cities

Example:RicherHuman-Computer
Interaction

Computer Vision
Perceiving andlearning visual tasks fromthe world throughcameras and sensors.

Example:Livestock
Monitoring

Robotic Process Automation (RPA)
Modeling and Simulation
Modeling our physical,
economical, and social world to
support the study, optimization,and testing of operations throughsimulation without interfering orinterrupting ongoing processes.

Examples:
COVID-19
Research

Software robotics to helporganizations automate tediousand repetitive tasks.

Example:
RPA Platforms

Tracking
Space Debris

Forecasting the future of AI is difficult. Five years ago, few would have predicted the recent
breakthroughs in natural language understanding that have resulted in systems that cangenerate full text almost indistinguishable from human prose.8 With a remarkable increase

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of investments in the global AI industry over the past five years9 and an unprecedented
amount of general R&D dollars being invested worldwide,10 there is no AI slowdown insight—only new horizons for deployed AI.

“With a remarkable increaseof investments in the global AI
industry over the past five years
and an unprecedented amountof general R&D dollars being
invested worldwide, there is no
AI slowdown in sight—only new
horizons for deployed AI.”

Frontiers of AI Technology.
The next decade of AI research will likely be defined by efforts to incorporate existingknowledge, push forward novel ways of learning, and make systems more robust,generalizable, and trustworthy.11 Research on advancing human-machine teaming will beat the forefront, as will improvements in hybrid AI techniques, enhanced training methods,
and explainable AI.
Human-AI Teaming. Mastering human-AI collaboration and teaming is a foundational
element for future application of AI. Synergy between humans and AI holds the promiseof a whole greater than the sum of its parts. Researchers are addressing this challenge bystudying issues of delegated authority, observability, predictability, directability, and trust.12
Gaining greater understanding of how humans will learn to work with AI will provide insights
for creating effective training programs for humans. Advances in language understanding
are being pursued to create systems that can summarize complex inputs and engage
through human-like conversation, a critical component of next-generation teaming. The
frontier of teaming includes the need for collaborative intelligence among cohorts of agents,
whether mixed groups of humans and machines or teams of coordinating machines.

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ARTIFICIAL INTELLIGENCE IN CONTEXTNovel Ways of Learning. New learning methods are allowing for greater efficiency in bothtraining and inference from data.13 This decreases dependence on vast data sets andwidens the aperture of systems to handle tasks beyond their original scope, building
pathways toward contextual learning and commonsense reasoning. Hybrid AI techniques
combine different AI approaches to capitalize on their complementary strengths.14 For
example, neuro-symbolic research is combining symbolic manipulation with neuralnetworks.15 Model-based and data-based approaches may also be combined; for example,
leveraging physics knowledge within statistical ML frameworks.16 Researchers are also
advancing supervised learning techniques with low supplies of labeled data,17 while others
have devised more efficient methods of labeling data.18 Synthetic data generation throughsimulation is one such promising approach.19 It allows a model to see conditions andscenarios it may not have encountered with a real data set, while preserving relationships
between important variables in the original data and privacy of sensitive data.20
Edge Computing. Breaking size, weight, and power barriers also increases the ubiquityof AI and aids privacy protection. Companies are working to pack more computational
power into tighter, specialized chips that use less energy to train and run the same models.
Such chips allow consumer devices to run complex models locally, rather than transmit
data externally and wait for models to run remotely. Retaining data entirely on the device
where a model is being trained or run is an advancement that could potentially enhance
individual privacy in AI-powered systems.21
Advances in Reasoning. In comparison to humans, even our most capable current AI
systems lack what one might think of as “commonsense reasoning.” Efforts are underway
to create systems that can generalize knowledge and translate learning across domains.
An AI system endowed with commonsense reasoning could effectively model thehuman ability to make and exploit presumptions about the physical properties, purpose,
intentions, and behavior of people and objects and thereby characterize the probableconsequences of an action or interaction. Advancements in categorization, in creating
generalized structured ontologies, and in language understanding will drive the ability formachines to learn while understanding context and content and allow people to discoverrapid solutions to problems that would historically take years to examine.22 This researchpromises to pave the way for more explainable AI along with greater ability to detect andmitigate bias, which will be essential to improving trustworthiness of these more generalAI technologies.23
Toward More General Artificial Intelligence. AI solutions to date have demonstrated
narrow and deep competencies, but with fundamental distinction from capabilities
demonstrated by humans. Humans perform tasks by learning without explicit supervised
signals; they generalize skills required for one task and apply them to other tasks; and theyaccrue, manipulate, and reason with large amounts of commonsense knowledge. Some
researchers have used the phrase “artificial general intelligence” (AGI) to refer to a goalof extending AI beyond narrow, vertical wedges of expertise. Debates have focused onp35

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whether there might be specific breakthroughs that would lead to more general, human-like
capabilities or whether the field will more likely continue to push more general AI along one
or more dimensions of skills. No matter what the perspective, significant progress acrossthe research areas mentioned in this section will be required to create more general AI
systems.24 If achieved, more general AI methods could have enormous benefits, but could
also introduce new risks if safety challenges are not addressed. While breakthroughs arein no way guaranteed, the United States should continue to research systems with morehuman-like capabilities, accompanied by commensurate investments to ensure that thosesystems are safe and controllable.
Advances in AI, including the mastery of more general AI capabilities along one or moredimensions, will likely provide new capabilities and applications. Some of these advances
could lead to inflection points or leaps in capabilities. Such advances may also introduce
new concerns and risks and the need for new policies, recommendations, and technicaladvances to assure that systems are aligned with goals and values,25 including safety,
robustness and trustworthiness.26 The US should monitor advances in AI and makenecessary investments in technology and give attention to policy so as to ensure that AI
systems and their uses align with our goals and values.27
Looking to an AI-Enabled Future.
Following the trajectories of the research threads outlined above sketches a future in which
AI empowers humanity in unprecedented ways, unlocking capabilities across science,education, space technology, healthcare, infrastructure, manufacturing, agriculture,entertainment, and countless other sectors. For example, advances in natural language
understanding could enable real-time, ubiquitous translation for more obscure languages
for which written and spoken training data is limited.28 This would transform the way wecommunicate across geographic and cultural barriers, enabling business, diplomacy, andfree exchange of ideas.
Breakthroughs in integration of multi-modal, multi-source data could enable real-time
AI-driven modeling and simulation for federal responses to crises including pandemics
and natural disasters.29 Drone feeds augmented with maps, building layouts, and othervisual data layers could empower first responders with lifesaving emergency-scene
understanding,30 and AI could help build response plans, expedite command and control,and optimize logistics for a range of disaster-response scenarios.31

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ARTIFICIAL INTELLIGENCE IN CONTEXTGraphic 0.2: AI is the Engine of Invention.

AI as the Engine

Healthcareof Invention.

Advancing AI-assisted disease prevention
Aiding in early disease detection and monitoring
Mitigating the effects of disabilities
Supporting health care providers in patient care

Examples:Averting cardiovascular disease and stroke
Biological sensors
Assisting the visually impaired
Telehealth robots

Education

Science
Examples:Expediting thediscovery andeffective utilizationof materials

Accelerating
materials
development
and resilience
Expediting drug andvaccine discovery

AI as theEngine ofInvention

Examples:Optimizing learningfor individuals

Enabling extended and flexible
space exploration

Smart Cities
Example:Autonomous
spacecraft andsmart habitats

Examples:Improving safety
Enabling intelligent
infrastructure
Optimizing complex
transportation hubs
Sustaining theenvironmentp37

Remote digitaltutors that use
virtual andaugmented reality

Increasing safety
in training

Space

Dynamic
curriculum
augmentation

Connected smart
sensors
Smart roads andbridges
Coordinating
movement of people
and goods
Supporting low-carbon
energy systems

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Artificial Intelligence in Context - Endnotes
The John S. McCain National Defense Authorization Act for Fiscal Year 2019 includes the following
definition to guide the Commission’s work: 1. Any artificial system that performs tasks under varying
and unpredictable circumstances without significant human oversight, or that can learn fromexperience and improve performance when exposed to data sets. 2. An artificial system developedin computer software, physical hardware, or other context that solves tasks requiring human-likeperception, cognition, planning, learning, communication, or physical action. 3. An artificial systemdesigned to think or act like a human, including cognitive architectures and neural networks. 4. A
set of techniques, including machine learning that is designed to approximate a cognitive task. 5.
An artificial system designed to act rationally, including an intelligent software agent or embodied
robot that achieves goals using perception, planning, reasoning, learning, communicating, decisionmaking, and acting. See Pub. L. 115-232, 132 Stat. 1636, 1965 (2018).

1

Andrew W. Moore, et al., The AI Stack: A Blueprint for Developing and Deploying Artificial
Intelligence, Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking
for Persistent ISR IX, 106350C (May 4, 2018), https://doi.org/10.1117/12.2309483; see also Dave
Martinez, et al., Artificial Intelligence: Short History, Present Developments, and Future Outlook, MIT
Lincoln Laboratory at 27 (Jan. 2019), https://www.ll.mit.edu/media/9526.

2

Note that model-based AI requires data for the manual construction of the model(s). Typically, thisinvolves less data than statistical machine learning, but more human effort.

3

Saleema Amershi, et al., Software Engineering for Machine Learning: A Case Study, ICSE-SEIP’19 Proceedings of the 41st International Conference on Software Engineering at 291-300 (2019),
https://2019.icse-conferences.org/details/icse-2019-Software-Engineering-in-Practice/30/SoftwareEngineering-for-Machine-Learning-A-Case-Study; D. Sculley, et al., Machine Learning: The HighInterest Credit Card of Technical Debt, SE4ML: Software Engineering for Machine Learning (NIPS
2014 Workshop), https://ai.google/research/pubs/pub43146.

4

5John Launchbury, A DARPA Perspective on Artificial Intelligence, DARPA, 4-7 (Feb. 2017), https://www.darpa.mil/attachments/AIFull.pdf.

The limitations of today’s statistical machine learning, as an example, include the vulnerability of
unknowingly learning and amplifying biases in the training data; the fact that they are often complex
models composed of a very large number of learned parameters, making them opaque and difficult
to interpret; the fact that they are trained to solve narrow tasks and lack generalization to other related
problems (such as when operationally encountered data fundamentally changes characteristic fromthe training data); and the fact that they require large amounts of labeled training data.

6

Andrew Moore, When AI Becomes an Everyday Technology, Harvard Business Review (June 7,2019), https://hbr.org/2019/06/when-ai-becomes-an-everyday-technology.

7

Tom B. Brown, et al., Language Models are Few-Shot Learners, arXiv (July 22, 2020), https://arxiv.
org/abs/2005.14165.

8

Zachary Arnold, et al., Tracking AI Investment: Initial Findings from the Private Markets, Center for
Security and Emerging Technology (Sept. 2020), https://cset.georgetown.edu/wp-content/uploads/
CSET-Tracking-AI-Investment.pdf.

910
According to UNESCO, global spending on R&D has reached a record high of almost US$1.7
trillion. See How Much Does Your Country Invest in R&D?, UNESCO Institute for Statistics (lastaccessed Jan. 7, 2021), http://uis.unesco.org/apps/visualisations/research-and-developmentspending/.
11
For a recent debate from AI experts see AI DEBATE 2: Moving AI Forward: An Interdisciplinary
Approach, Montreal Artificial Intelligence (Dec. 23, 2020), https://montrealartificialintelligence.com/
aidebate2.html.

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ARTIFICIAL INTELLIGENCE IN CONTEXTArtificial Intelligence in Context - Endnotes
See e.g., Bryan Wilder, et al., Learning to Complement Humans, International Joint Conferences
on Artificial Intelligence Organization (2020), https://doi.org/10.24963/ijcai.2020/212; Ece Kamar, etal., Combining Human and Machine Intelligence in Large-scale Crowdsourcing, Proceedings of the
11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012) (June
4-8, 2012), https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/galaxyZoo.pdf;
Ramya Ramakrishnan, et al., Overcoming Blind Spots in the Real World: Leveraging Complementary
Abilities for Joint Execution, Proceedings of the AAAI Conference on Artificial Intelligence (July17, 2019), https://doi.org/10.1609/aaai.v33i01.33016137; Saleema Amershi, et al., Guidelines for
Human-AI Interaction, CHI ’19: Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems (May 2019), https://doi.org/10.1145/3290605.3300233; Eric Horvitz, Reflections
on Challenges and Promises of Mixed-Initiative Interaction, AI Magazine (June 15, 2007), https://doi.
org/10.1609/aimag.v28i2.2036.
12

A goal of these new methods is to eliminate the need for many complex calculations that maketraditional training very slow. Vincent Dutordoir, Sparse Gaussian Processes with Spherical Harmonic
Features, arXiv (June 30, 2020), https://arxiv.org/abs/2006.16649.

13

For a discussion on hybrid intelligence architectures that combine symbolic manipulation with deeplearning see Gary Marcus, The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence,arXiv at 14-19 (Feb. 19, 2020), https://arxiv.org/abs/2002.06177.
14

See Neuro-symbolic AI, MIT-IBM Watson AI Lab (last accessed Jan. 16, 2021), https://
mitibmwatsonailab.mit.edu/category/neuro-symbolic-ai/.

15

Anuj Karpatne, et al., Physics-guided Neural Networks (PGNN): An Application in Lake Temperature
Modeling, Association for Computing Machinery’s Special Interest Group on Knowledge Discovery
and Data Mining (ACM SIGKDD) 2018 (Feb. 20, 2018), https://arxiv.org/pdf/1710.11431.pdf.

16

17
Rajat Raina, et al., Self-Taught Learning: Transfer Learning from Unlabeled Data, Proceedingsof the 24th International Conference of Machine Learning (June 2007), https://dl.acm.org/doi/
abs/10.1145/1273496.1273592; Dr. Bruce Draper, Learning with Less Labeling, DARPA (last accessed
Dec. 19, 2020), https://www.darpa.mil/program/learning-with-less-labeling.

Rahul Dixit, et al., Artificial Intelligence and Machine Learning in Sparse/Inaccurate Data Situations,
IEEE (Aug. 21, 2020), https://ieeexplore.ieee.org/document/9172612.

18

19
See Cem Dilmegani, The Ultimate Guide to Synthetic Data in 2021, AI Multiple (Jan. 12, 2021),
https://research.aimultiple.com/synthetic-data/.
20
The Real Promise of Synthetic Data, MIT News (Oct. 16, 2020), https://news.mit.edu/2020/realpromise-synthetic-data-1016.
21
10 Breakthrough Technologies 2020, MIT Technology Review (Feb. 26, 2020), https://www.
technologyreview.com/10-breakthrough-technologies/2020/#tiny-ai.

The World’s Largest and Most Complete Common-Sense Knowledge Base, Cycorp (last accessed
Dec. 19, 2020), https://www.cyc.com/the-cyc-platform/the-knowledge-base; John Pavlus, Common
Sense Comes Closer to Computers, Quanta Magazine (April 30, 2020), https://www.quantamagazine.
org/common-sense-comes-to-computers-20200430/; Antoine Bosselut, et al., COMET: Commonsense
Transformers for Automatic Knowledge Graph Construction, Proceedings of the 57th Annual Meetingof the Association for Computational Linguistics (2019), https://homes.cs.washington.edu/~msap/
pdfs/bosselut2019comet.pdf.
22

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Amy Blumenthal, How to Make AI Trustworthy, Science Daily (Aug. 31, 2020), https://www.
sciencedaily.com/releases/2020/08/200827105937.htm.

23

See Benedict Neo, Top 4 AI companies leading in the race towards Artificial General Intelligence,
Towards Data Science (April 13, 2020), https://towardsdatascience.com/four-ai-companies-onthe-bleeding-edge-of-artificial-general-intelligence-b17227a0b64a; Srishti Deoras, 9 Companies
Doing Exceptional Work In AGI, Just Like OpenAI, Analytics India Magazine (July 25, 2019), https://analyticsindiamag.com/9-companies-doing-exceptional-work-in-agi-just-like-openai/.

24

In the Key Considerations, the Commission describes practices, technologies and operational policies
to develop and field systems that align with key values. Importantly, agencies must consider valuesas (1) embodied in choices about engineering trade-offs and (2) explicitly represented in the goals
and utility functions of an AI system. See Key Considerations for Responsible Development & Fieldingof Artificial Intelligence, NSCAI (July 2020), https://www.nscai.gov/wp-content/uploads/2021/01/
Key-Considerations-for-Responsible-Development-Fielding-of-AI.pdf. In addition to actions andinvestments needed now (see the Blueprint for Action associated with Chapter 7 of this report), the Key
Considerations include policies and practices that should be updated to reflect new AI considerations
as the technology evolves.

25

26
This will require R&D as noted in the Key Considerations and Chapter 7 of this report. It will also
require continued investment in system architectures to limit the consequences of system failure, tomonitor AI performance as systems run to assess if they are performing as intended, and to overcome
S&T gaps for audit and oversight. For more information, see Chapters 7 and 8 of this report.

As noted in Chapter 8 and its Blueprint for Action, this will require, for instance, sustained assessment
and recurring guidance on privacy and civil liberties implications of AI applications and emerging
technologies. Disallowed outcomes and policy guidance will need to be updated over time as community
norms and technical capabilities change. Further, the Key Considerations note that engineering
practices will need to assess general feasibility and compliance with disallowed outcomes expressedin policy, as well as the demonstrated technical maturity of specific candidate AI technologies. See
Key Considerations for Responsible Development & Fielding of Artificial Intelligence, NSCAI (July2020),
https://www.nscai.gov/wp-content/uploads/2021/01/Key-Considerations-for-ResponsibleDevelopment-Fielding-of-AI.pdf.

27

Envisioned translation systems will leverage feedback to the system by actively correcting
translation and recognition errors the software makes, improving performance as the interaction
between translating parties goes on. A major goal is rapid deployment to new languages that havenot been seen before. For example, Carnegie Mellon’s DIPLOMAT Project makes interactive speechtranslation possible through a new architecture called Multi Engine Machine Translation (MEMT).
DIPLOMAT gives users the ability to provide translation corrections to support rapid development to
new languages that have not been seen before. Robert Frederking, Interactive Speech Translation in
the DIPLOMAT Project, Carnegie Mellon University Language Technologies Institute (last accessed
Dec. 19, 2020), http://www.cs.cmu.edu/~air/papers/acl97-workshop.pdf.

28

Modeling and simulation can also help prepare for effective pandemic supply chain
responses orchestrated by the government. Madhav Marathe, High Performance Simulations to
Support Real-time COVID19 Response, SIGSIM-PADS ’20 (June 2020), https://dl.acm.org/doi/
pdf/10.1145/3384441.3395993.

29

30

Edgybees (last accessed Dec. 19, 2020), https://edgybees.com/.

Department of Energy Announces the First Five Consortium, U.S. Department of Energy (Aug. 18,
2020), https://www.energy.gov/articles/department-energy-announces-first-five-consortium.

31

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PA R T O N EPART ONEp41

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C EPART I: DEFENDING AMERICA IN THE AI ERA

41

Chapter 1: Emerging Threats in the AI Era

43

Chapter 2: Foundations of Future Defense

59

Chapter 3: AI and Warfare

75

Chapter 4: Autonomous Weapon Systems and Risks
Associated with AI-Enabled Warfare

89

Chapter 5: AI and the Future of National Intelligence107

Chapter 6: Technical Talent in Government

119

Chapter 7: Establishing Justified Confidence in AI Systems

131

Chapter 8: Upholding Democratic Values: Privacy, Civil Liberties,
and Civil Rights in Uses of AI for National Security

141

p
42

CHAPTER 1

Chapter 1: Emerging Threatsin the AI Erap
43

E M E R G I N G T H R E AT S I N T H E A I E R ASocietal Level of Conflict

Data Harvesting
and Targeting ofIndividuals

AI-Enabled
Information
Operations

Adversarial AI
Accelerated
Cyber Attacks
AI-Enabled
Biotechnologyp44

CHAPTER 1

The U.S. government is not prepared to defend the United States in the
coming artificial intelligence (AI) era. AI applications are transforming
existing threats, creating new classes of threats, and further emboldening
state and non-state adversaries to exploit vulnerabilities in our open
society.1 AI systems will extend the range and reach of adversaries into
the United States just as the missile age and terrorism brought threats
closer to home. Because of AI, adversaries will be able to act withmicro-precision, but at macro-scale and with greater speed. They will
use AI to enhance cyber attacks and digital disinformation campaigns
andto target individuals in new ways. AI will also help create precisely
Graphic 1.1: How AI is Transforming the Threat Landscapeengineered biological agents. And adversaries will manipulate the AI
systems we will rely upon.

H ow AI isTransforming theT hreat L andscapeCurrent Threats
Advanced BY AI Systems

New Threats
FROM AI Systems

Threats TO AI Stacks
Themselves

Future Threats
VIA AI Systems

AI transforms existing
range and reach of threats

AI creates new
threat phenomena

AI itself is also a new
attack surface

Examples of potential
threats to keep in viewSelf-replicating
AI-generated
malwareDeepfakes andcomputational
propaganda

Improved
and autonomous
disinformation
campaigns

Micro-targeting:AI-fused data fortargeting or blackmail

AI-engineered andtargeted pathogens

AI swarms andnano-swarms

AI attack involves thewhole “AI stack”.
Examples include:

Rapid machine-tomachine escalation
via automated C2Model inversion
Training data
manipulation“Data lake”
poisoning

AI-enabled humanaugmentation bypeer competitors
Proliferation of
simple lethal
autonomous weapons
to terrorists

AI technologies exacerbate two existing national security challenges:• First, digital dependence in all walks of life increases vulnerabilities to cyber intrusion
across every segment of our society: corporations, universities, government, privateorganizations, and the homes of individual citizens. In parallel, new sensors haveflooded the modern world. The internet of things (IoT), cars, phones, homes, and social
media platforms collect streams of data, which can then be fed into AI systems that canidentify, target, and manipulate or coerce our citizens.2• Second, state and non-state adversaries are challenging the United States below
the threshold of direct military confrontation by using cyber attacks, espionage,
psychological and political warfare, and financial instruments. Adversaries do notneed AI to conduct widespread cyber attacks, exfiltrate troves of sensitive data about
American citizens, interfere in our elections, or bombard us with malign information onp
45

E M E R G I N G T H R E AT S I N T H E A I E R A

digital platforms. However, AI is starting to change these attacks in kind and in degree,
creating new threats to the U.S. economy, critical infrastructure, and societal cohesion.3
Moreover, these AI-enabled capabilities will be used across the spectrum of conflict.
They will be used as tools of first resort in non-military conflicts, as a prelude to military
actions, or in concert with military actions in war.
Americans are waking to some of the privacy implications of their digital dependence
and the potential threats from AI-powered malign information, like deep fakes. However,debate in the United States has not yet accounted for the full scope and danger of the AIenabled threats and the overall security risks to the AI systems all around us. The prospectof adversaries using machine learning (ML), planning, and optimization to create systems
to manipulate citizens’ beliefs and behavior in undetectable ways is a gathering storm.4
Most concerning is the prospect that adversaries will use AI to create weapons of mass
influence to use as leverage during future wars, in which every citizen and organization
becomes a potential target.

““The prospect of adversaries
using machine learning,planning, and optimization tocreate systems to manipulate
citizens’ beliefs and behavior inundetectable ways is a gatheringstorm. Most concerning is theprospect that adversaries will
use AI to create weapons of mass
influence to use as leverageduring future wars, in which
every citizen and organization
becomes a potential target.”p46

CHAPTER 1

S ocietal
Level Impact.

The rest of this chapter discusses five AI-related threats that already havebeen, or soon will be, developed and used against the United States.
1. AI-Enabled Information Operations.
AI and associated technologies will increase the magnitude, precision, and persistenceof adversarial information operations. AI exacerbates the problem of malign information inthree ways:• Message. AI can produce original text-based content and manipulate images, audio,
and video, including through generative adversarial network (GAN)-enabled andreinforcement learning (RL) deep fakes that will be very difficult to distinguish fromauthentic messages.

p47

E M E R G I N G T H R E AT S I N T H E A I E R A• Audience. AI can construct profiles of individuals’ preferences, behaviors, and beliefs totarget specific audiences with specific messages.
• Medium. AI can be embedded within platforms, such as through ranking algorithms, toproliferate malign information.
AI-enabled malign information campaigns will not just send one powerful message to 1
million people, like 20th century propaganda. They also will send a million individualized
messages—configured on the basis of a detailed understanding of the targets’ digital lives,
emotional states, and social networks.5 Rival states are already using AI-powered maligninformation. For example, according to Taiwan authorities, China’s government tested its
AI-powered malign information capacities during the 2020 Taiwan elections.6 A National
Basketball Association general manager was harassed on social media for supporting
protesters in Hong Kong, in an effort that may have involved autonomous bots.7 Other
techniques rely on AI-generated fake personas.8 The control and manipulation of digital
information has become central to the Kremlin’s strategy, including in efforts to undermine
the integrity of the democratic process in the United States and elsewhere.9
In the United States, the private sector has taken the leading role in combating foreignmalign information. Social media companies in particular have extensive operations to trackand manage information on their platforms. But coordination between the government andthe social media firms remains ad hoc. We need a more integrated public-private responseto the problem of foreign-generated disinformation. Moreover, the government needs todevote greater attention and resources to the technical challenges of detection, attribution,and media authentication. The government should:
Create a Joint Interagency Task Force and Operations Center. Congress has authorized
a Foreign Malign Influence Response Center to be established within the Office of the
Director of National Intelligence (ODNI).10 The government should use this authority tocreate a technologically advanced, 24-hour task force and operations center to lead andintegrate government efforts to counter foreign-sourced malign information. It would survey
the landscape of relevant public and private actors, coordinate among them, and act inreal time to counter foreign information campaigns. To expose, attribute, and respondeffectively, the center must be equipped with modern AI-enabled digital tools and staff
with specialized expertise.

Recommendation

Fund the Defense Advanced Research Projects Agency (DARPA) to coordinate multiple
research programs to detect, attribute, and disrupt AI-enabled malign information
campaigns and to authenticate the provenance of digital media. Additional funding would
amplify ongoing DARPA research programs to detect synthetic media and expand its
efforts into attributing and disrupting malign information campaigns.11 However promisingsome of these detection technologies may prove to be individually, funding to develop
alternative technologies to authenticate the provenance of the digital media will provide
a more technologically robust means to prevent the impersonation of trusted sourcesof information.12 DARPA should pursue these programs and help transition all of these

Recommendationp48

CHAPTER 1

technologies and applications to government departments and agencies, in order to assist
with detecting, attributing, and disrupting malign information campaigns in real time.
Recommendation

Create a task force to study the use of AI and complementary technologies, including
the development and deployment of standards and technologies, for certifying content
authenticity and provenance. The White House Office of Science and Technology
Policy should take the lead in creating this task force. In response to the challengesof misinformation, efforts are underway to develop standards and pipelines aimed atcertifying the authenticity and provenance of audiovisual content.13 These efforts makeuse of technologies, including encryption and fragile watermarking, to secure and trackthe expected transformations of content via production and transmission pipelines.
These efforts offer the opportunity to mitigate malign information campaigns that seek tocorrupt or spoof highly trusted sources of information across our digital ecosystem. This
technology area is ripe for public-private partnership. Several private organizations are
already forming to fight disinformation efforts in this realm.14“

2. Data Harvesting and Targeting of Individuals.

“Potential adversaries willrecognize what every advertiser
and social media company
knows: AI is a powerful targeting
tool.”
Data security is a national security problem. “Ad-tech” has become “natsec-tech.” Potential
adversaries will recognize what every advertiser and social media company knows: AI
is a powerful targeting tool. Just as AI-powered analytics transformed the relationshipbetween companies and consumers, now it is transforming the relationship between
governments and individuals. The broad circulation of personal data drives commercial
innovation but also creates vulnerabilities.15 We fear that adversaries’ systematic efforts
to harvest data on U.S. companies, individuals, and the government is about more than
traditional espionage.16 Adversaries will combine widely available commercial data withdata acquired illicitly—as in the 2015 Office of Personnel Management hack—to track,
manipulate, and coerce individuals.17 The reach of tools that China, for instance, usesp49

E M E R G I N G T H R E AT S I N T H E A I E R A

to monitor, control, and coerce its own citizens—big data analytics, surveillance, andpropaganda—can be extended beyond its borders and directed at foreigners.18 Without
adequate data protection, AI makes it harder for anyone to hide his or her financial situation,
patterns of daily life, relationships, health, and even emotions. Personal and commercial
vulnerabilities become national security weaknesses as adversaries map individuals,
networks, and social fissures in society; predict responses to different stimuli; and model
how best to manipulate behavior or cause harm. The rise and spread of these techniques
represent a major counterintelligence challenge.19
For the government to treat the data of its citizens and businesses as a national security asset,
substantial changes are required in the way we think about data security and in our policies
and laws to strengthen it. We need to identify categories and combinations of personaland commercial data that are most sensitive. Early efforts to limit foreign adversaries’ data
harvesting—such as the government’s decision to force a Chinese company to relinquish
ownership of a popular dating application for fear of what a hostile adversary could do withsensitive private data20 —represent important initial steps. However, the government lacks
a broad approach with clear policies, criteria, or authorities to confront this multifaceted
problem. The government should:
Develop policies that treat data security as national security, including in these areas:

Recommendation• First, from a technical standpoint, the government must ensure that a security
development lifecycle approach is in place for its own AI systems (includingcommercial systems it acquires), which should include a focus on potential privacy
attacks.21 Red teaming must include privacy expertise. Government databases should
be federated and anonymized whenever possible, and personal data retained no longer
than is necessary, in order to make it more difficult for adversaries to utilize information
for malicious purposes.
• Second, the government should ensure that data privacy and security are priority
considerations as part of larger efforts to strengthen foreign investment screening andsupply chain intelligence and risk management.22• Third, national efforts to legislate and regulate data protection and privacy must
integrate national security considerations, such as limiting the ability of hostile
foreign actors to acquire sensitive data on Americans on the commercial market.23
3. Accelerated Cyber Attacks.
Malware in the AI era will be able to mutate into thousands of different forms once it is lodged
on a computer system. Such mutating polymorphic malware already accounts for more than
90% of malicious executable files.24 Deep RL tools can already find vulnerabilities, conceal
malware, and attack selectively.25 While it is uncertain which methods will dominate, there
is a clear path for U.S. adversaries to transform the effectiveness of cyber attack andespionage campaigns with an ensemble of new and old algorithmic means to automate,
optimize, and inform attacks.26 This goes beyond AI-enhanced malware. Machine learning
has current and potential applications across all the phases of cyber attack campaignsp50

CHAPTER 1““Machine learning has current
and potential applications acrossall the phases of cyber attack
campaigns ...”

and will change the nature of cyber warfare and cyber crime.27 The expanding applicationof existing AI cyber capabilities will make cyber attacks more precise and tailored,
further accelerate and automate cyber warfare, enable stealthier and more persistentcyberweapons, and make cyber campaigns more effective on a larger scale.

U.S. defenses have proven incapable of handling even more elementary cyber challenges.
Vulnerabilities remain open in outdated infrastructure and medical devices, while newvulnerabilities are proliferating in 5G networks, billions of IoT devices, and in software supply
chains.28 The multibillion-dollar global damage caused by Russia’s 2017 NotPetya attack
concretely demonstrates the power of even basic automated malware, the risk toleranceof capable state actors, and the consequences of such capabilities proliferating.29 Thoughdefensive applications of AI bring the promise to improve our national cyber defenses,
AI can’t defend inherently vulnerable digital infrastructure. To address the present
threat, Congress must continue implementing the Cyberspace Solarium Commission’s
recommendations.30 With this foundation for cyber defense, the U.S. can prepare for
expanding threats via testing and building the instrumented infrastructure required for AIenabled cyber defenses, establishing better incentives for security, properly organizing tomeet the challenge, and keeping attackers off balance. Pervasive cyber-enabled espionage
and attacks on U.S. computer networks and critical infrastructure will continue—and will
become more damaging with AI—unless urgent federal action is taken. The government
should:Recommendationp51

Develop and deploy AI-enabled defenses against cyber attacks. National security
agencies need to acquire the sensors and instrumentation needed to train AI systems
to detect and respond to threats on their networks. AI-enabled cyber defenses will alsoneed large-scale, instrumented, and realistic testing, and they must be robust enoughto withstand adversarial attacks. The defenses should be employed to expand machine
speed information sharing, behavior-based anomaly detection, and malware mitigation
across government networks. To capitalize on these capabilities, the government should
accelerate the establishment of a Joint Cyber Planning and Operations Center, modeled
after the National Counterterrorism Center.31 The Center would serve as a centralized cyberintelligence sharing and collaboration unit with multi-agency jurisdiction and authorities toinvestigate threats, proactively support defensive mitigations, and coordinate responses.

E M E R G I N G T H R E AT S I N T H E A I E R A

4. Adversarial AI.
AI systems represent a new target for attack. While we are on the front edge of thisphenomenon, commercial firms and researchers have documented attacks that involve
evasion, data poisoning, model replication, and exploiting traditional software flaws to
deceive, manipulate, compromise, and render AI systems ineffective.32 This threat is related
to, but distinct from, traditional cyber activities, because AI systems will be vulnerable toadversarial attacks from any domain where AI augments action—civilian or military.33 Given
the reliance of AI systems on large data sets and algorithms, even small manipulations ofthese data sets or algorithms can lead to consequential changes for how AI systems operate.
The threat is not hypothetical: adversarial attacks are happening and already impacting
commercial ML systems.34 With rare exceptions, the idea of protecting AI systems has
been an afterthought in engineering and fielding AI systems, with inadequate investment
in research and development.35 Only three of 28 organizations recently surveyed have “theright tools in place to secure their ML systems.”36 There has not yet been a uniform effort
to integrate AI assurance across the entire U.S. national security enterprise. To improve AI“assurance,” the government should:
Create a National AI Assurance Framework. All government agencies will need to develop
and apply an adversarial ML threat framework to address how key AI systems could be
attacked and should be defended. An analytical framework can help to categorize threats togovernment AI systems and assist analysts with detecting, responding to, and remediating
threats and vulnerabilities.37

Recommendation

Create dedicated red teams for adversarial testing. Red teams should assume an offensive
posture, trying to break systems and make them violate rules for appropriate behavior.
Because of the scarcity of required expertise and experience for AI red teams, DoD andODNI should consider establishing government-wide communities of AI red-teaming
capabilities that could be applied to multiple AI developments.38

Recommendation

5. AI-Enabled Biotechnology.
Biology is now programmable. New technologies such as the gene editing tool CRISPR
ushered in an era where humans are able to edit DNA. Combined with massive computing
power and AI, innovations in biotechnology may provide novel solutions for mankind’s mostvexing challenges, including in health, food production, and environmental sustainability.
Like other powerful technologies, however, applications of biotechnology can have a darkside. The COVID-19 pandemic reminded the world of the dangers of a highly contagious
pathogen. AI may enable a pathogen to be specifically engineered for lethality or to target
a genetic profile—the ultimate range and reach weapon. Also, AI, when applied to biology,
could optimize for the physiological enhancement of human beings, including intelligenceand physical attributes. To the extent that brain waves can be represented as a machine
vision challenge for AI, the mysteries of the brain may be unlocked and programmed.

p52

CHAPTER 1

Individuals, societies, and states will have different moral and ethical views and accept
different degrees of risk in the name of progress, and U.S. competitors are comparatively
likely to take more risk-tolerant actions and conform less rigidly to bioethical norms andstandards. China understands the tremendous upside associated with leading the bio
revolution. Massive genomic data sets at places like BGI Group (formerly known as the
Beijing Genomics Institute), coupled with China’s now-global genetic data collection
platform and “all-of-nation” approach to AI, will make them a formidable competitor in the
bio realm.39 BGI may be serving, wittingly or unwittingly, as a global collection mechanism
for Chinese government genetic databases, providing China with greater raw numbers anddiversity of human genome samples as well as access to sensitive personal information
about key individuals around the world.40 The United States cannot afford to look back in 10
years and be “surprised” by the biotechnology equivalent of Huawei. Additionally, Russia’slong-standing disregard for scientific norms and bioethical principles, demonstrated by its
development and employment of novel nerve agents such as Novichok for assassination
attempts and U.S. government concerns over Russia’s compliance with the Biological
Weapons Convention, could presage a willingness to utilize advanced biotechnology
abilities for nefarious purposes.41 The government should:
Recommendationp53

Increase the profile of biosecurity and biotechnology issues within U.S. national security
agencies. Given how AI will substantially increase the rate of technical advancementin biotechnology, the government should update the National Biodefense Strategy toinclude a wider vision of biological threats, such as human enhancement, exploitation ofgenetic data for malicious ends, and ways U.S. competitors could utilize biotechnology
or biodata advantages for novel purposes. Additionally, U.S. officials should warn of thedangers associated with foreign actors obtaining personal genetic information, specificallyhighlighting concerns about the links between BGI and the Chinese government.42

E M E R G I N G T H R E AT S I N T H E A I E R A

Chapter 1 - Endnotes
A threat can be understood as an adversary capability paired with a vulnerability that can create a
harmful consequence. See Terry L. Deibel, Foreign Affairs Strategy: Logic for American Statecraft,
Cambridge University Press at 142-150 (2007). Threats can be graded further by their seriousness,
likelihood, imminence, and tractability.

1

Stuart A. Thompson & Charlie Warzel, Twelve Million Phones, One Dataset, Zero Privacy, New YorkTimes (Dec. 19, 2019), https://www.nytimes.com/interactive/2019/12/19/opinion/location-trackingcell-phone.html. For example, the internet of things (IoT) and AI-powered applications can turn yournew robotic vacuum into a listening device. See Sriram Sami, et al., Spying with Your Robot Vacuum
Cleaner: Eavesdropping via Lidar Sensors, Proceedings of the 18th Conference on Embedded
Networked Sensor Systems (Nov. 2020), https://dl.acm.org/doi/10.1145/3384419.3430781.

2This is in some ways analogous to what Cold War strategists called “counter-value targeting.” See
Lawrence Freedman, The Evolution of Nuclear Strategy, Palgrave Macmillan Vol. 20 at 119-122 (1989).
In the realm of nuclear strategy, this was also known as counter-city or counter-economy targeting.

3

Some observers have used the concept of “sharp power” to describe such efforts to wield influencein open societies. These uses of power are sharp “in the sense that [authoritarian states aim to]pierce, penetrate, or perforate the information environments in the targeted countries.” Sharp Power:
Rising Authoritarian Influence, National Endowment for Democracy at 13 (Dec. 5, 2017), https://www.ned.org/sharp-power-rising-authoritarian-influence-forum-report/. See also Testimony of Dr.
Eric Horvitz, Microsoft, before the U.S. Senate Committee on Commerce, Science, & Transportation,
Subcommittee on Space, Science, & Competitiveness, Hearing on the Dawn of Artificial Intelligence at
13 (Nov. 30, 2016), http://erichorvitz.com/Senate_Testimony_Eric_Horvitz.pdf.

4Some have characterized AI-driven information operations as “computational propaganda.” See Matt
Chessen, The MADCOM Future: How Artificial Intelligence Will Enhance Computational Propaganda,
Reprogram Human Culture, and Threaten Democracy… and What Can Be Done About It, Atlantic
Council (Sept. 2017), https://www.atlanticcouncil.org/wp-content/uploads/2017/09/ The_MADCOM_
Future_RW_0926.pdf.

5

Philip Sherwell, China Uses Taiwan for AI Target Practice to Influence Elections, The Australian (Jan.
5, 2020), https://www.theaustralian.com.au/world/the-times/china-uses-taiwan-for-ai-target-practiceto-influence-elections/news-story/57499d2650d4d359a3857688d416d1e5.

6

7
Ben Cohen, et al., How One Tweet Turned Pro-China Trolls Against the NBA, Wall Street Journal(Oct. 16, 2019), https://www.wsj.com/articles/how-one-tweet-turned-pro-china-trolls-against-thenba-11571238943. On automated bots, see, e.g., Sarah Kreps & Miles McCain, Not Your Father’sBots, Foreign Affairs (Aug. 2, 2019), https://www.foreignaffairs.com/articles/2019-08-02/not-yourfathers-bots.

James Vincent, An Online Propaganda Campaign Used AI-Generated Headshots to Create Fake
Journalists, The Verge (July 7, 2020), https://www.theverge.com/2020/7/7/21315861/ai-generatedheadshots-profile-pictures-fake-journalists-daily-beast-investigation.

8

For recent studies on technical aspects of Russia’s interference in the 2016 election, see Alexander
Spangher, et al., Characterizing Search-Engine Traffic to Internet Research Agency Web Properties,
Web Conference (2020), https://www.microsoft.com/en-us/research/publication/characterizingsearch-engine-traffic-to-internet-research-agency-web-properties/; Ryan Boyd, et al., Characterizing
the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election
Using Linguistic Analyses, PsyArXiv Preprints (2018), https://psyarxiv.com/ajh2q/. See also Alina
Polyakova, Weapons of the Weak: Russian and AI-driven Asymmetric Warfare, Brookings Institution(Nov. 15, 2018), https://www.brookings.edu/research/weapons-of-the-weak-russia-and-ai-drivenasymmetric-warfare/.

9

10
See Pub. L. 116-92, National Defense Authorization Act for Fiscal Year 2020, 133 Stat. 1198, 2129
(2019).

These include the Media Forensics (MediFor) and Semantic Forensics (SemaFor) programs. See Dr.
Matt Turek, Media Forensics, DARPA (last accessed Jan.10, 2021), https://www.darpa.mil/program/media-forensics; Dr. Matt Turek, Semantic Forensics, DARPA (last accessed Jan.10, 2021), https://www.darpa.mil/program/semantic-forensics.
11

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54

CHAPTER 1

Chapter 1 - Endnotes
See, e.g., Paul England, et al., AMP: Authentication of Media via Provenance, arXiv (June 20, 2020),
https://arxiv.org/abs/2001.07886.
12

13

See, e.g., Paul England, et al., AMP: Authentication of Media via Provenance, arXiv.

See Creating the Standard for Digital Content Attribution, Content Authenticity Initiative, https://contentauthenticity.org/; and Project Origin: Protecting Trusted Media, Project Origin, https://www.
originproject.info/about.
14

Robert Williams has described how policy makers face an “innovation-security conundrum,” one
aspect of which is “the worry that data privacy and national security are increasingly interconnected.
Data (and data networks) can be exploited in ways that threaten security, but they also form thelifeblood of technological innovation on which both economic growth and national security depend.”
Robert D. Williams, Crafting a Multilateral Technology and Cybersecurity Policy, Brookings at 1 (Nov.
2020), https://www.brookings.edu/wp-content/uploads/2020/11/Robert-D-Williams.pdf.

15

Ellen Nakashima, With a Series of Major Hacks, China Builds a Database on Americans, Washington
Post (June 5, 2015), https://www.washingtonpost.com/world/national-security/in-a-series-of-hackschina-appears-to-building-a-database-on-americans/2015/06/05/d2af51fa-0ba3-11e5-95fdd580f1c5d44e_story.html.
16

17
Another example of an adversary acquiring significant data on U.S. individuals is the hack of thecredit reporting agency Equifax. Press Release, Department of Justice, Chinese Military Personnel
Charged with Computer Fraud, Economic Espionage and Wire Fraud for Hacking into Credit Reporting
Agency Equifax (Feb. 10, 2020), https://www.justice.gov/opa/pr/chinese-military-personnel-chargedcomputer-fraud-economic-espionage-and-wire-fraud-hacking; Aruna Viswanatha, et al., FourMembers of China’s Military Indicted Over Massive Equifax Breach, Wall Street Journal (Feb. 11,2020), https://www.wsj.com/articles/four-members-of-china-s-military-indicted-for-massive-equifaxbreach-11581346824.
18
See, e.g., Drew Harwell & Eva Dou, Huawei Tested AI Software That Could Recognize
Uighur Minorities and Alert Police, Report Says, Washington Post (Dec. 8, 2020), https://www.
washingtonpost.com/technology/2020/12/08/huawei-tested-ai-software-that-could-recognize-uighurminorities-alert-police-report-says/; Hugh Harsono, China’s Surveillance Technology Is Keeping Tabs
on Populations Around the World, The Diplomat (June 18, 2020), https://thediplomat.com/2020/06/
chinas-surveillance-technology-is-keeping-tabs-on-populations-around-the-world/.

See James Baker, Counterintelligence Implications of Artificial Intelligence– – Part III, Lawfare (Oct.
10, 2018), https://www.lawfareblog.com/counterintelligence-implications-artificial-intelligence-part-iii.

19

Yuan Yang & James Fontanella-Khan, Grindr Sold by Chinese Owner After US National Security
Concerns, Financial Times (March 7, 2020), https://www.ft.com/content/a32a740a-5fb3-11ea-8033fa40a0d65a98.
20

21
On privacy attacks, see Maria Rigaki & Sebastian Garcia, A Survey of Privacy Attacks in Machine
Learning, arXiv (July 15, 2020), https://arxiv.org/abs/2007.07646.

The Committee on Foreign Investment in the United States (CFIUS) has the authority to review
transactions that include “sensitive personal data of United States citizens that may be exploited ina manner that threatens national security.” For background, see Laura Jehl, Spotlight on Sensitive
Personal Data As Foreign Investment Rules Take Force, The National Law Review (Feb. 18, 2020),
https://www.natlawreview.com/article/spotlight-sensitive-personal-data-foreign-investment-rules-takeforce. The National Counterintelligence and Security Center (NCSC) includes “sensitive government
data, and personally-identifiable information” in its conception of key supply chain risks. See Supply
Chain Risk Management: Reducing Threats to Key U.S. Supply Chains, NCSC at 3 (2020), https://www.dni.gov/files/NCSC/documents/supplychain/20200925-NCSC-Supply-Chain-Risk-Managementtri-fold.pdf.
22

See, e.g., Graham Webster, App Bans Won’t Make U.S. Security Risks Disappear, MIT Technology
Review (Sept. 21, 2020), https://www.technologyreview.com/2020/09/21/1008620/wechat-tiktok-banchina-us-security-policy-opinion/.

23

p
55

E M E R G I N G T H R E AT S I N T H E A I E R A

Nicholas Duran, et al., 2018 Webroot Threat Report, Webroot at 6 (2018), https://www-cdn.webroot.
com/9315/2354/6488/2018-Webroot-Threat-Report_US-ONLINE.pdf.

24

Gary J. Saavedra, et al., A Review of Machine Learning Applications in Fuzzing, arXiv (Oct. 9, 2019),
https://arxiv.org/pdf/1906.11133.pdf; Isao Takaesu, Machine Learning Security: DeepExploit, GitHub(Aug. 29, 2019), https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/DeepExploit;
Marc Ph. Stoecklin, et al., DeepLocker: How AI Can Power a Stealthy New Breed of Malware, Security
Intelligence (Aug. 8, 2018), https://securityintelligence.com/deeplocker-how-ai-can-power-a-stealthynew-breed-of-malware/.

25

26
Implications of Artificial Intelligence for Cybersecurity: Proceedings of a Workshop, National
Academies of Sciences, Engineering, and Medicine (2019), https://doi.org/10.17226/25488; Nektaria
Kaloudi & Jingyue Li, The AI-Based Cyber Threat Landscape, ACM Computing Surveys at 1-34 (Feb.
2020), https://dl.acm.org/doi/abs/10.1145/3372823; Ben Buchanan, et al., Automating Cyber Attacks,
Center for Security and Emerging Technology (Nov. 2020), https://cset.georgetown.edu/research/automating-cyber-attacks/; Dakota Cary & Daniel Cebul, Destructive Cyber Operations and Machine
Learning, Center for Security and Emerging Technology at 5-23 (Nov. 2020), https://cset.georgetown.
edu/research/destructive-cyber-operations-and-machine-learning/.
27
Implications of Artificial Intelligence for Cybersecurity: Proceedings of a Workshop, National
Academies of Sciences, Engineering, and Medicine (2019), https://doi.org/10.17226/25488.

The recent SolarWinds attack demonstrates deep vulnerabilities in our software supply chains.
See Joint Statement by the Federal Bureau of Investigation (FBI), the Cybersecurity and Infrastructure
Security Agency (CISA), and the Office of the Director of National Intelligence (ODNI), Office of the
Director of National Intelligence (Dec. 16, 2020), https://www.dni.gov/index.php/newsroom/pressreleases/item/2175-joint-statement-by-the-federal-bureau-of-investigation-fbi-the-cybersecurity-andinfrastructure-security-agency-cisa-and-the-office-of-the-director-of-national-intelligence-odni.

28

29
Implications of Artificial Intelligence for Cybersecurity: Proceedings of a Workshop, National
Academies of Sciences, Engineering, and Medicine (2019), https://doi.org/10.17226/25488; Ben
Buchanan, et al., Automating Cyber Attacks, Center for Security and Emerging Technology at 3 (Nov.
2020), https://cset.georgetown.edu/research/automating-cyber-attacks/.

Cyberspace Solarium Commission Report, U.S. Cyberspace Solarium Commission (March 2020),
https://www.solarium.gov/report.

30

See recommendation 5.4 in Cyberspace Solarium Commission Report, U.S. Cyberspace Solarium
Commission at 87 (March 2020), https://www.solarium.gov/report.

31

32
Adversarial AI Threat Matrix: Case Studies, GitHub (last accessed Jan. 10, 2021), https://github.
com/mitre/advmlthreatmatrix/blob/master/pages/case-studies-page.md. For more on applicationsof adversarial AI, see Naveed Akhtar & Ajmal Mian, Threat of Adversarial Attacks on Deep Learningin Computer Vision: A Survey, IEEE (March 28, 2018), https://ieeexplore.ieee.org/stamp/stamp.
jsp?arnumber=8294186.

Adversarial AI is about what can be done to AI systems. The science of protecting and defending
AI applications against attacks is called “AI Assurance.” The science of attacking each technologicalcomponent of AI is called “Counter-AI.”

33

34
Ram Shankar Siva Kumar & Ann Johnson, Cyberattacks Against Machine Learning Systems Are
More Common Than You Think, Microsoft Security (Oct. 22, 2020), https://www.microsoft.com/
security/blog/2020/10/22/cyberattacks-against-machine-learning-systems-are-more-common-thanyou-think/.

It has been estimated that less than 1% of AI R&D funding is directed toward the security of AI
systems. See Nathan Strout, The Three Major Security Threats to AI, Center for Security and Emerging
Technology (Sept. 10, 2019), https://cset.georgetown.edu/article/the-three-major-security-threats-to35

ai/.
36
Ram Shankar Siva Kumar, et al., Adversarial Machine Learning—Industry Perspectives, arXiv at 2
(May 21, 2020), https://arxiv.org/pdf/2002.05646.pdf.

p56

CHAPTER 1

Chapter 1 - Endnotes
There are various ongoing public and private efforts including, for instance, the MITRE-Microsoft
adversarial ML framework. See Ram Shankar Siva Kumar & Ann Johnson, Cyberattacks Against
Machine Learning Systems Are More Common Than You Think, Microsoft Security (Oct. 22, 2020),
https://www.microsoft.com/security/blog/2020/10/22/cyberattacks-against-machine-learning-systemsare-more-common-than-you-think/; Adversarial AI Threat Matrix: Case Studies, MITRE (last accessed
Jan. 10, 2021), https://github.com/mitre/advmlthreatmatrix/blob/master/pages/case-studies-page.md.

37

For a similar recommendation, see Michèle Flournoy, et al., Building Trust Through Testing,
WestExec Advisors at 27 (Oct. 2020), https://cset.georgetown.edu/wp-content/uploads/Building-TrustThrough-Testing.pdf. (Flournoy, et al., argue for “a national AI and ML red team as a central hub totest against adversarial attacks, pulling together DoD operators and analysts, AI researchers, T&E[Central Intelligence Agency (CIA), Defense Intelligence Agency (DIA), National Security Agency(NSA)], and other IC components, as appropriate. This would be an independent red-teamingorganization that would have both the technical and intelligence expertise to mimic realistic adversary
attacks in a simulated operational environment.”)

38

BGI built and operates China National GeneBank, the Chinese government’s national genetic
database. It also is a major global supplier of COVID-19 testing, which potentially provides accessto large international genetic data sets; by June 30, 2020, it had supplied more than 35 million test
kits to 180 countries, including the United States, and built 58 testing labs in 18 countries. See Kirsty
Needham, Special Report: COVID Opens New Doors for China’s Gene Giant, Reuters (Aug. 5, 2020),
https://www.reuters.com/article/us-health-coronavirus-bgi-specialreport/special-report-covid-opensnew-doors-for-chinas-gene-giant-idUSKCN2511CE.

39

p
57

E M E R G I N G T H R E AT S I N T H E A I E R AJohn Wertheim, China’s Push to Control Americans’ Health Care Future, 60 Minutes (Jan. 31,2021), https://www.cbsnews.com/news/china-us-biodata-60-minutes-2021-01-28/? ftag=CNM-0010aab7d&linkId=110169507; Kirsty Needham, Special Report: COVID Opens New Doors for China’s
Gene Giant, Reuters (Aug. 5, 2020), https://www.reuters.com/article/us-health-coronavirus-bgispecialreport/special-report-covid-opens-new-doors-for-chinas-gene-giant-idUSKCN2511CE.

40

See Richard Pérez-Peña, What Is Novichok, the Russian Nerve Agent Tied to Navalny Poisoning?,
New York Times (Sept. 2, 2020), https://www.nytimes.com/2020/09/02/world/europe/novichok-skripal.
html; 2020 Adherence to and Compliance with Arms Control, Nonproliferation, and Disarmament
Agreements and Commitments (Compliance Report), U.S. Department of State at Pt. V (2020),
https://2017-2021.state.gov/2020-adherence-to-and-compliance-with-arms-control-nonproliferationand-disarmament-agreements-and-commitments-compliance-report-2//index.html#_Toc43298166.

41

See Chapter 16 of this report for additional recommendations pertaining to the nexus of AI andbiotechnology.

42

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58

CHAPTER 2

Chapter 2: Foundationsof Future Defensep59

F O U N D AT I O N S O F F U T U R E D E F E N S E

AI-Enabled Future Defense

Build theTechnical
Backbone

Accelerate
Adoption of
Existing Digital
Technologies

Train and Educate
Warfighters

Invest in NextGeneration
Capabilities

Democratize AI
Development

p60

CHAPTER 2

The U.S. military has enjoyed military-technical superiority over all
potential adversaries since the end of the Cold War. Now, its technicalprowess is being challenged, especially by China and Russia. Senior
military leaders have warned that if current trend lines are not altered,
the U.S. military will lose its military-technical superiority in the coming
years.1 Artificial intelligence (AI) is a key aspect of this challenge, asboth of our great power competitors believe they will be able to offset
our military advantage using AI-enabled systems and AI-enabledautonomy. In the coming decades, the United States will win against
technically sophisticated adversaries only if it accelerates adoption of
AI-enabled sensors and systems for command and control, weapons,
and logistics.
The Department of Defense (DoD) must set an ambitious goal. By 2025, the foundations
for widespread integration of AI across DoD must be in place. Those foundations include acommon digital infrastructure that is accessible to internal AI development teams and criticalindustry partners alike, a digitally literate workforce, and modern AI-enabled business practices
that improve efficiency. All are prerequisites to achieving a state of military AI readiness, which
is discussed in Chapter 3 of this report.

““By 2025, the foundations forwidespread integration of AI
across DoD must be in place.”
DoD lags far behind the commercial sector in integrating new and disruptive technologies suchas AI into its operations. Pockets of excellence started to emerge in 2017 when Project Maven
was launched with the aim to simplify work for intelligence analysts by recognizing objects
in video footage captured by drones and other platforms.2 Other promising initiatives are
occurring in defense labs and agencies, and proof-of-concept demonstrations are ongoing inservice-level tests.3 However, visionary technologists and warfighters largely remain stymied
by antiquated technology, cumbersome processes, and incentive structures that are designed
for outdated or competing aims.4 Successes are usually based on workarounds––in spite of
the system.

p
61

“

F O U N D AT I O N S O F F U T U R E D E F E N S E“... visionary technologists
and warfighters largely
remain stymied by antiquatedtechnology, cumbersome
processes, and incentive
structures that are designed foroutdated or competing aims.”

The obstacles to integrating AI are many. DoD has long been hardware-oriented toward ships,
planes, and tanks. It is now trying to make the leap to a software-intensive enterprise. Spending
remains concentrated on legacy systems designed for the industrial age and Cold War.5 Many
Departmental processes still rely too much on PowerPoint and manually driven work streams.
The data that is needed to fuel machine learning (ML) is currently stovepiped, messy, or often
discarded. Platforms are disconnected. Acquisition, development, and fielding practices
largely follow rigid, sequential processes, inhibiting early and continuous experimentation andtesting critical for AI. Even promising AI programs have not yet delivered as hoped and often
remain bound to proprietary software and data storage of commercial vendors. Steps suchas building the cloud infrastructure necessary to scale AI applications proceed slowly. Datasharing agreements and software updates that take hours or days in industry turn into monthslong delays. Service members at every level lack the technical education and experience toemploy AI.

Meanwhile, bureaucracy hinders partnerships with technology firms and critical efforts to
expand the National Security Innovation Base.6 The prospect of bureaucratic snarls deterscompanies from working with DoD; it is economically irrational for many startups to even try.
Traditional defense companies will continue to play a central role in building and integrating large
systems for AI-enabled warfare.7 However, even these contractors, who have the resources andexpertise to navigate the system, face process and technical roadblocks that slow efforts tobuild and integrate AI systems.
As a result, change will not be easy. It will require a Secretary of Defense who focuses the
Department on speeding the adoption of new technologies, and a dedicated Steering
Committee on Emerging Technology to drive implementation and align priorities between the
DoD and the Intelligence Community. The Secretary should direct action in five areas:p62

CHAPTER 2

1. Build the technical backbone. DoD should make foundational investments to support a
Department-wide technical infrastructure for ubiquitous development and fielding of AI. It took apromising first step in 2020 with the issuance of a DoD Data Strategy.8 However, the Department
lacks the modern digital ecosystem, collaborative tools and environments, and broad ondemand access to shared AI resources that it needs to integrate AI across the organization.9
The Department should avoid reinventing core infrastructure for each new AI-driven program
or capability,
phic 2.2.1: AI Digital Ecosystem
Summary.and it should look to leverage and interoperate with proven solutions from theIntelligence Community (IC) wherever possible. A broader platform that could be used acrossthe Department would enable more dynamic development and employment of AI and would
more AI
efficiently
utilize
scarce technical expertise.10
DigitalEcosystem
Recommendation

CommonInterfaces
AI Digital
Ecosystem.

USERS
APPLICATIONSPLATFORM ENVIRONMENTS
SOFTWAREDATA
HARDWARE

The Secretary of Defense should direct
the establishment of a DoD-wide digital
ecosystem. The Secretary should requirethat all new joint and service programs
adhere to the design of this ecosystem andthat, wherever possible, existing programs
become interoperable with it by 2025.11 Key
elements should include:• Data architecture composed of a secure,federated system of distributed repositories
linked by a data catalog and appropriate
access controls12 that facilitates finding,accessing, and moving desired data acrossthe DoD.13• Packaged AI environments14 thatenable agile and iterative AI capabilities
development,15 testing, fielding, and updatingin support of a diverse set of stakeholders.16• A marketplace of shared AI resources17 that builds upon federated repositories of data,
software, and trained models,18 along with pre-negotiated computing and storage
services from a pool of vetted cloud providers.
• A bolstered network and communications backbone to provide bandwidth to support
transport and data fusion, secure processing, continuous development and fielding of AI
applications, and software system integration at all levels.
• Common interfaces that allow swift integration of mission-oriented investments.

p
63

“

F O U N D AT I O N S O F F U T U R E D E F E N S E“Warfighters cannot change
the way they fight without alsochanging the way they think.”

Graphic 2.3: Train and Educate the Warfighter.

Data-informed
decision making

Train and Educate

Pillars

Computational
thinking

Human-machineteaming

War fighters.

Organizationaltransformation

Maker
cultureRecommendations
Integrate digital skill sets andcomputational thinking into junior militaryleader education

Integrate emerging and disruptive
technologies into service-level professional
military education

Create emerging and disruptive technology
coded billets in the DoD

2. Train and educate warfighters. Warfighters cannot change the way they fight without alsochanging the way they think. Most service members only use the powerful computers they
have to create PowerPoint presentations, build spreadsheets, or send emails. Our service
members need to develop core competencies in building, using, and responsibly teamingwith machine systems to recognize AI’s potential for building a faster and more effective
force. In particular, they need to know:

Recommendation• How to use data in decision-making in ways that complement intuition and experience.
• How to use information processing agents and how to get a computer to perform
calculations and analytics that could not be done efficiently by a human.
• How to develop and thrive in a “maker” culture that encourages continuous contact andregular experimentation with and development of new tools.
• How to move toward a “teammate model” for interacting with autonomous systems andnavigate issues of delegated authority, observability, predictability, directability, andtrust.
• How to bring organizations into the AI era—including when and how to integrate AIrelated tasks into priority missions, allocate resources to build and maintain the AI stack,oversee new systems, and support the careers of technical experts.

p64

CHAPTER 2

To improve training and education along these lines, DoD should:• Identify service members who excel at computational thinking during the accession
process;• Invest in upskilling its workforce through self-guided education courses and coding
language incentives;• Teach junior leaders about problem curation, the AI lifecycle, data collection andmanagement, probabilistic reasoning and data visualization, and data-informed
decision-making as part of their pre-commissioning requirements and initial training;• Integrate emerging and disruptive technology training into professional militaryeducation courses; and• Create emerging technology coded billets and an emerging technology certification
program comparable to the joint billet and qualification system.
Recommendation

3. Accelerate the adoption of existing digital technologies. DoD has largely relied on
workarounds to adopt new technologies, while the core acquisition processes remain sclerotic.
There are some bright spots, including the release of the Department’s tailorable acquisition
framework, contracting resources,19 and approaches taken by certain programs within the Air
Force.20 The Department must scale these innovative practices and take further steps to alignacquisition workforce training, program incentives, budget, and organizational structures tobetter support the delivery of digitally enabled capabilities.
A number of the Department’s digital innovation initiatives have delivered results,21 but they
are uncoordinated and under-resourced. DoD signaling of technology priorities is ad hoc andis not supported by a track record of significant DoD investments in digital technology with
non-traditional vendors. As a result, national security AI applications attract less private-market
investment. The Department should focus on four actions:• Integrate commercial AI to optimize core business processes. DoD should embrace
proven commercial AI applications and incentivize their use to generate labor andcost savings, speed administrative actions, and inform decision-making.22 As a criticalfirst step, DoD should prioritize construction of enterprise data sets across coreadministration areas.
• Network digital innovation initiatives to scale impact. Pockets of bottom-up
innovation need to be married with top-down leadership. The Department should
harmonize its innovation initiatives to carry out a coordinated go-to-market strategy forcommercial technology solutions. The Under Secretary of Defense for Research andEngineering, working closely with the Under Secretary of Defense for Acquisition andSustainment, the military services and other headquarters counterparts, should provide
strategic direction for this effort.
• Expand use of specialized acquisition pathways and contracting approaches. DoD
should accelerate efforts to train acquisition professionals on the full range of available
options for acquisition and contracting and incentivize their use for AI and digitaltechnologies.23p65

F O U N D AT I O N S O F F U T U R E D E F E N S E• Update the budget and oversight processes. DoD’s resource allocation process isnearly identical to what was put in place in 1961. It is incompatible with AI and otherdigital technologies. DoD and Congress should institute reforms that enable theadvancement of software and digital technologies by accounting for speed, uncertainty,
experimentation, and continuous upgrades.

An integrated and strategic approach to technology that aligns the process, incentives, and organizational
culture of the DoD and the National Security Innovation Base as a pipeline to resource, prioritize, acquire anditerate capabilities critical to sustain the competitive advantage

D elivering AI at
Speed and S cale.

IDENTIFY
SCOUT

TECH
ANNEX

RESOURCE
CREATE SUSTAINABLE
FUNDINGANALYZEDIVEST$

BUILD THE SUPPLYBASE

SIGNALDERA
UPG

UPDATE

SCALE

DIG

ITAL

CQ
UI
RE

FUND

A

N
TI O
OVA
VES
IATI
I N IT

INN

p66

CHAPTER 2““At every level, technologists,
operators, and domain
experts should function as
integrated teams.”

Recommendation

4. Democratize AI development. The Department must promote bottom-up AI
development.24 At every level, technologists, operators, and domain experts should
function as integrated teams.25 This would facilitate user feedback and improve trust andconfidence in AI systems. DoD should:• Designate the Joint Artificial Intelligence Center (JAIC) as the Department’s
AI Accelerator. The JAIC cannot identify every potential use for AI in the
Department, but it can and should serve as a central hub of AI expertise. In this“accelerator” model, JAIC would coordinate with relevant acquisition, technology,
Graphic
2.7: Budget Analysis
Chart.
and governanceoffices toinform strategy; develop AI applications that address
Graphic 2.7: Budget Analysis Chart.
shared challenges at the Combatant Commands; and provide resources that enabledistributed AI development across the Department and the military services.26
Enhanced AI R&D Investment, FY 2015-2030
Enhanced
AI R&D Investment, FY 2015-2030
Source: Govini and NSCAI
Source: Govini and NSCAIEnhanced
AI R&D Investment,
FY 2015-2030
Current AI_status quoCore AI_enhanced
Source:and
NSCAI Core AI_enhanced
Current Govini
AI_statusquo

AI adoption_status quo
AI adoption_status quo

AI adoption_enhanced
AI adoption_enhanced

$80
$80

Enhanced AI R& D

$70
$70

Investment, F Y

Dollar
Dollarvalue
value(billions(billions
US$)
US$)

2 015 -2 0 3 0.

Projection of spending on AI adoption withrecommended
core AI spendingof $8B/yr.
Projection of spending
on AI adoption
withrecommended core AI spending of $8B/yr.

$60
$60
$50
$50

Projection of spending on AI adoption withcurrent
core
spending
Projectionof AI
spending
onof
AI$1.5B/yr.
adoption withcurrent core AI spending of $1.5B/yr.

$40
$40
$30
$30
$20
$20
$10
$10
$0
$020152015

Projection of Core AI spendingunder
the status
Projectionof Corequo.
AI spending
under the status quo.
2018
2018

2021
2021

NSCAI recommendation: increase Core AI
spendingfrom $1.5B to $8B
per yearbyAI
2025.
NSCAI recommendation:
increaseCore
spending from $1.5B to $8B per year by 2025.

2024
2024

2027
2027

2030
2030

Core AI spending is comprised of RDT&E programs to develop AI applications such as machine learning/deep learning, collaborative behavior,computer vision; human-machine teaming, automated reasoning, robotic autonomy, automated data fusion, and self-healing networks. Together with

Core AI spending is comprised of RDT&E programs to develop AI applications such as machine learning/deep learning, collaborative behavior,This
figure illustrates
the correlation
between
R&D
investment
in CoreasAItechnologies
and AI adoptioncommerciallysourcedAI applications,coreautomatedAI spending
enables AI
adoption.
We define
AI adoption
the andsumself-healingof AI-enabledand AI-enabling
computer vision;
human-machineteaming,reasoning,
robotic
autonomy,
automateddata fusion,
networks.
Together withprojected
the below).
year
2030. core
TwoAIscenarios
areAI represented
inAIthisfigure.
first, themaintains
investments(definedcommerciallyto
sourcedAI applications,spending enables
adoption. We define
adoptionas theInsumtheof AI-enabled
andDoD
AI-enabling
itsinvestments
current (definedlevel below).
of investment in core AI (~1.5B/year). In the second scenario, the DoD increases itsp67

AI-enabled programs develop (in the case of RDT&E programs) and field (in the case of procurement programs) the gamut of DoD warfighting andbusiness
systems,incorporating
AI applications
for analyzing,
maneuvering,and many other
AI-enabled
programs
develop (inCore
the caseof RDT&E programs)
andautomating,field (in the communicating,case of procurement
programs)monitoring,
the gamutsensing,of DoD warfighting
andtasks.
While
AI spendingis usuallya small
percentageforof analyzing,these programs,
their system’s
performance
may be criticallydependent
business
systems,incorporatingCore
AI applications
automating,communicating,maneuvering,monitoring,sensing,upon
and themany otherincorporationcore AI. is usually a small percentage of these programs, their system’s performance may be critically dependent upon thetasks. While AIofspending
incorporation of core AI.
AI-enabling programs include technologies such as cloud computing and advanced microelectronics required to support the deployment of effectiveAI
capabilities
at scale.include technologies such as cloud computing and advanced microelectronics required to support the deployment of effective
AI-enabling
programs
AI capabilities at scale.

F O U N D AT I O N S O F F U T U R E D E F E N S E

investments in core AI to $8B/year. A significant increase in core AI spending is required to drive therate of AI adoption higher.
NSCAI staff teamed with two external partners to analyze historical and planned DoD investments in
AI RDT&E. The source data for the analysis is DoD’s annual RDT&E budget expenditures (for FY2015– FY 2020) and annual RDT&E budget requests (for FY2021-FY2025). For the methodology employed
and lessons learned from this work, see Analysis of DoD RDT&E Investments in AI, NSCAI (on file with
the Commission). Disclaimer: We believe this analysis yields important insights into general trends inAI spending and solutions for better future analyses, but caution that quality issues in the source datadetailed in our on file report mean that the spending level estimates presented contain significant,difficult to estimate margins of error.
AI-enabled programs develop (in the case of RDT&E programs) and field (in the case of procurement
programs) the gamut of DoD warfighting and business systems, incorporating Core AI applications
for analyzing, automating, communicating, maneuvering, monitoring, sensing, and many other tasks.
While AI spending is usually a small percentage of these programs, their system’s performance may be
critically dependent upon the incorporation of core AI.
AI-enabling programs include technologies such as cloud computing and advanced microelectronics
required to support the deployment of effective AI capabilities at scale.

• Establish integrated AI delivery teams at each Combatant Command. These
commands have specific operational needs that routinely outpace centralized
development. AI delivery teams should be embedded at each Combatant Commandand capable of supporting the full lifecycle of AI development and fielding, including
data science, engineering, testing, and production—leveraging common resources
through the digital ecosystem.27 Teams should include forward-deployable
components to act as the local interface with operational units.28
5. Invest in next-generation capabilities. DoD leaders anticipate flat or declining defense
budgets for the coming years.29 Despite potential budgetary pressures, DoD must continue
accelerating its modernization programs by prioritizing emerging and disruptive technologies
such as AI.30

Recommendation• Fund AI research and development. The Department should commit to spendingat least 3.4% of its budget on science and technology and allocate at least $8 billion
toward AI R&D annually.31 Additional resources should be focused on organizations
with significant AI expertise, such as the Defense Advanced Research Projects Agency(DARPA), the Office of Naval Research (ONR), the Air Force Office of Scientific Research(AFOSR), the Army Research Office (ARO), and the service laboratories.

p68

CHAPTER 2““To make AI ubiquitousthroughout its business processes
and military systems, DoD must
make tough budget tradeoffs andprioritize modernization.”• Retire legacy systems ill-equipped to compete in AI-enabled warfare. To make AI
ubiquitous throughout its business processes and military systems, DoD must maketough budget tradeoffs and prioritize modernization.32 DoD should pursue a balanced
approach to update existing systems with leading-edge technologies to buy time forinvestments in longer-term bets. Further, to guard against bias in favor of defending thestatus quo, DoD should require an evaluation of AI alternatives prior to funding Major
Defense Acquisition Programs (MDAP).33• Produce a technology annex to the National Defense Strategy. To link DoD’stechnology investment strategy to future operational needs, the annex should include
roadmaps for designing, developing, fielding, and sustaining critical technologies thatare needed to address the operational challenges identified in the strategy.

p
69

F O U N D AT I O N S O F F U T U R E D E F E N S E

Chapter 2 - Endnotes
General Joseph Dunford, then Chairman of the Joint Chiefs of Staff, testified in 2017 that “The U.S.
military’s competitive advantage against potential adversaries is eroding […] I assess that within five
years we will lose our ability to project power; the basis of how we defend the homeland, advance
U.S. interests, and meet our alliance commitments.” Posture Statement of General Joseph Dunford,
Chairman of the Joint Chiefs of Staff before the Senate Armed Services Committee, Senate Armed
Services Budget Hearing at 2 (June 13, 2017), https://www.armed-services.senate.gov/imo/media/
doc/Dunford_06-13-17.pdf.

1

Big Data at War: Special Operations Forces, Project Maven, and Twenty-First-Century Warfare,
Modern War Institute (Aug. 25, 2020), https://mwi.usma.edu/big-data-at-war-special-operationsforces-project-maven-and-twenty-first-century-warfare/; Cheryl Pellerin, Project Maven to Deploy
Computer Algorithms to War Zone by Year’s End, DoD (July 21, 2017), https://www.defense.gov/
Explore/News/Article/Article/1254719/project-maven-to-deploy-computer-algorithms-to-war-zone-byyears-end/. Project Maven now includes detecting, classifying, and tracking objects within full motion
video images (e.g., person, vehicle, and weapon) and other AI algorithms for text-based projects. PE
0305245D8Z: Intelligence Capabilities and Innovation, Office of the Secretary of Defense (Feb. 2019),
https://www.dacis.com/budget/budget_pdf/FY20/RDTE/D/0305245D8Z_187.pdf.

2

For example, the Army’s Project Convergence exercise in September 2020 demonstrated use of
AI at multiple stages of the targeting process. Jen Judson & Nathan Strout, At Project Convergence,
the US Army Experienced Success and Failure—and It’s Happy About Both, Defense News (Oct. 12,2020), https://www.defensenews.com/digital-show-dailies/ausa/2020/10/12/at-project-convergencethe-us-army-experienced-success-and-failure-and-its-happy-about-both/. The Air Force has held
similar exercises, most notably as part of its efforts associated with the Advanced Battle Management
System—the technical infrastructure which will support the DoD’s Joint All-Domain Command andControl concept. Theresa Hitchens, ABMS Demo Proves AI Chops For C2, Breaking Defense (Sept. 3,2020), https://breakingdefense.com/2020/09/abms-demo-proves-ai-chops-for-c2/.

3This includes the traditional process by which concepts of operation interact with technology
development. Chapter 3 of this report offers recommendations to adapt this approach and ensure thattechnological advancements inform concepts as much as concepts drive technology development.

4

As one observer has noted: “While DoD’s investment accounts have grown substantially inthe last three years, this growth has been highly concentrated in buying systems from existingproduction lines and doing prototypes of military systems.” Testimony of Andrew Hunter, Director,
Defense-Industrial Initiatives Group, CSIS, before the U.S. House of Representatives Armed
Services Committee, Hearing on DoD’s Role in Competing with China at 6 (Jan. 15, 2020), https://
armedservices.house.gov/_cache/files/5/8/5818cc1f-b86f-4dca-8aee-10ca788e6f43/9F4A03ABF1DE
AB747AF2D1302087A426.20200115-hasc-andrew-hunter-statement-vfinal.pdf.

5

The National Defense Strategy highlights the importance of the National Security Innovation Basein maintaining the Department’s technological advantage. Summary of the 2018 National Defense
Strategy, U.S. Department of Defense at 3 (2018), https://dod.defense.gov/Portals/1/Documents/
pubs/2018-National-Defense-Strategy-Summary.pdf. The Center for Strategic and International
Studies offers a useful definition of the term, noting that the “[National Security Innovation Base] is asignificant expansion in scope […] compared to the traditional concept of the defense industrial base”
and includes tech firms out of innovation hubs such as Silicon Valley, Boston, and Austin. See Andrew
Hunter, A Strategic Approach to Defense Investment, CSIS (March 26, 2018), https://www.csis.org/
analysis/strategic-approach-defense-investment.

6“The largest six prime defense suppliers (Lockheed Martin, Boeing, Northrop Grumman,
Raytheon, General Dynamics, and BAE Systems) […] represented 32 percent of all DoD prime
obligations in 2019.” Fiscal Year 2020: Industrial Capabilities, U.S. Department of Defense at 40(Dec. 23, 2020), https://www.businessdefense.gov/Portals/51/USA002573-20%20ICR_ 2020_Web.
pdf? ver=o3D76uGwxcg0n0Yxvd5k-Q%3d%3d.

7

The strategy lays the foundation for the Department to treat data as a strategic asset and details thegoals to make DoD data visible, accessible, understandable, linked, trustworthy, interoperable, andsecure. Executive Summary: DoD Data Strategy, U.S. Department of Defense (Sept. 30, 2020), https://
media.defense.gov/2020/Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

8

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Chapter 2 - Endnotes
In recent years the Department has made promising initial steps to establish managed services
constructs for platforms, cloud infrastructure, and software development. For example, the Air Force’s
CloudOne and PlatformOne offerings (https://software.af.mil/dsop/services/); the Navy’s Black Pearl(https://blackpearl.us/); and the Army’s Coding Repository and Transformation Environment (CReATE).
Further, the Office of the Secretary of Defense has built a data management platform, ADVANA, withthe goal to establish it as the single authoritative source for audit and business data analytics. See
Written Statement for the Record of David L. Norquist, Deputy Secretary of Defense before the U.S.
Senate Armed Services Committee Subcommittee on Readiness at 6 (Nov. 20, 2019), https://www.
armed-services.senate.gov/imo/media/doc/Norquist_11-20-19.pdf.

910Components of this platform are underway as a result of the Joint Artificial Intelligence Center(JAIC)’s Joint Common Foundation initiative—particularly the marketplace of shared AI resources
including data, algorithms, and trained AI models.
11
Use of a common technical infrastructure will vastly improve DoD’s ability to ensure interoperability
and increase the effectiveness of the joint force. However, it is important to note that even without
such critical technical infrastructure, the Department is taking important policy steps to driveinteroperability and AI readiness for programs designed to meet joint capability needs. See Aaron
Mehta, Hyten to Issue New Joint Requirements on Handling Data, Defense News (Sept. 23, 2020),
https://www.defensenews.com/pentagon/2020/09/23/hyten-to-issue-new-joint-requirements-onhandling-data/. Chapter 3 of this report outlines additional recommendations for achieving a state ofmilitary AI readiness by 2025.
12
Secured access to data sets as well as other shared resources should be managed by user- androle-based authentication facilitated by an end-to-end identity, credential, and access management
infrastructure.

This hinges on implementation of the DoD’s new data strategy. Executive Summary: DoD
Data Strategy, U.S. Department of Defense (Sept. 30, 2020), https://media.defense.gov/2020/
Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

13

14
These are platform environments with ready-made workflows that can be tailored and launcheddepending on user type (e.g., researcher, industry partner, operator) and use case (e.g.,development, TEVV [test, evaluation, validation, and verification], fielding).

In other words, the DevSecOps application lifecycle. “DevSecOps improves the lead time andfrequency of delivery outcomes through enhanced engineering practices, promoting a more cohesive
collaboration between Development, Security, and Operations teams as they work towards continuous
integration and delivery.” Understanding the Differences Between Agile & DevSecOps— From a
Business Perspective, GSA (last accessed Jan. 1, 2021), https://tech.gsa.gov/guides/understanding_differences_agile_devsecops/.

15

Stakeholders could include embedded development teams working at the tactical edge; privatesector partners contributing pre-trained models; academic researchers working on open, relevant
challenge problems; government science and technology (S&T) researchers working within a service
lab; or international partners co-developing interoperable AI capabilities.

16

17
Shared AI resources should be managed with continuous Authorization to Operate (ATO)
frameworks and with mandated default ATO reciprocity across the Department.
18
Similar to or relying upon the platform delivery and features of Git (https://git-scm.com), GitHub(https://github.com), and GitLab (https://about.gitlab.com).

The Pentagon acquisition office’s Adaptive Acquisition Framework and Contracting Cone markimportant steps by the Department to promote the use of alternate authorities for acquisitions andcontracting. These include, for example, other transaction authorities, middle-tier acquisitions, rapid
prototyping and rapid fielding, and specialized pathways for software acquisition.
19

20
For example, the Air Force’s Advanced Battle Management System (ABMS), which is managing
systems intended to support the new Joint All-Domain Command and Control concept as a portfolio
and based heavily on experimentation to drive innovation and an iterative approach to requirements.
Notably, the Department of Defense Appropriations Bill for Fiscal Year 2021 expresses concern withvarious aspects of the Air Force’s approach, including the “absence of firm requirements, acquisitionp71

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strategy, or cost estimate” and system of systems integration. See H. Rept. 116-453, at 294-295 (July16, 2020), https://www.congress.gov/116/crpt/hrpt453/CRPT-116hrpt453.pdf.
21
The term “digital innovation initiatives” is used here to describe the various entities across the
Office of the Secretary of Defense and the military services– –such as the Defense Innovation Unit
(DIU), AFWERX, NavalX, and Army Applications Laboratory (AAL)– –that are focused on bridging
the gap with the commercial technology sector, especially startups and non-traditional vendors, andaccelerating the delivery of best-of-breed technology solutions.
22
The Defense Innovation Unit (DIU) is currently pursuing a number of AI projects to optimize
business processes in the DoD, ranging from using AI-driven Robotic Process Automation to reducelabor costs for the Army Comptroller to improving Air Force readiness with AI-driven predictive
maintenance and leveraging AI-constructed knowledge graphs to rapidly identify supply chain
risks for the Defense Intelligence Agency. See JAIC Partners with DIU on AI/ML Models to Resolve
Complex Financial Errors, JAIC (Oct. 1, 2020), https://www.ai.mil/blog_10_01_ 20-jaic_partners_with_
diu_on_aiml_models_to_resolve_complex_finanical_errors.html; U.S. Defense Department Awards
C3.ai $95M Contract Vehicle to Improve Aircraft Readiness Using AI, Business Wire (Jan. 15, 2020),
https://www.businesswire.com/news/home/20200115005413/en/US-Defense-Department-Awards-C3.
ai-95M-Contract-Vehicle-to-Improve-Aircraft-Readiness-Using-AI; Accelerate.AI Accelerates Growthand Product Adoption with Defense Innovation Unit Contract, Accrete.ai (April 23, 2020), https://blog.
accrete.ai/newsroom/accrete.ai-wins-million-dollar-contract-with-the-defense-innovation-unit.

As an example, DIU uses several acquisition pathways and contracting strategies that could help
improve both the adoption and operational relevance of AI solutions and also expand the National
Security Innovation Base. DIU pioneered the Commercial Solutions Opening with Army Contracting
Command–New Jersey, which leverages section 2371b of title 10 U.S.C. Other Transaction authority
to create a “fast, flexible, and collaborative” contract vehicle to prototype capabilities for the
Department. DIU has also used Section 2374a of title 10 U.S.C. Prize Challenge authority to advancevarious AI-related priorities for DoD and the broader AI research community.

23

The Department-wide digital infrastructure described above is critical to enabling this approach,but structural changes are also required to maximize its utility.

24

25
There are notable examples of warfighter-technologist pairings within DoD, such as the Air Force’s
software factories and the forward-deployed tactical data teams used by Special Operations andArmy Futures Command. They found that partnering technologists (such as data scientists) with
operators or analysts at the tactical edge: 1) significantly reduces the time it typically takes acontractor to understand the problem set and deploy a solution; 2) incentivizes iterative development
techniques and fast-fielding of minimum viable products that yield higher-impact solutions on an
accelerated timeline; and 3) generates increased buy-in to data and AI technologies as criticalmission enablers. NSCAI Engagements (Nov. 2020). To ensure U.S. forces maintain overmatch in thelong-term, DoD must scale this user-centered development.
26
Important offices for coordination with the JAIC include but are not limited to USD(R&E), USD
Acquisition & Sustainment (USD(A&S)), Director Operational Test & Evaluation (DOT&E), and the
DoD Chief Information Officer (CIO) and Chief Data Officer (CDO). Within USD(R&E), DIU is a key
enabler of the JAIC that pursues a project-based approach by transitioning commercial prototypes for
specific applications. The JAIC currently serves the Combatant Commands through its Component
Mission Initiatives (CMIs), including a Mission Initiative for Joint Warfighting Operations. See Mission
Initiatives, JAIC (last accessed Dec. 28, 2020), https://www.ai.mil/mi_joint_warfighting_operations.
html.

Such applications could be developed by other Combatant Commands, Service software factories,or the JAIC and discoverable via the recommended digital ecosystem. Each Combatant Commandshould ensure that the AI delivery teams are staffed with the appropriate talent to manage the fulllifecycle of AI solutions, including in disciplines such as data science, AI testing and model training,
software engineering, product management, and full stack development.

27

As an example, both Army Futures Command (AFC) and Army Special Operations Command(USASOC) use a model known as “tactical data teams.” This model brings AI/ML expertise forward
to the field in the form of three- to six-person teams to build AI solutions for real-time operational
problems. Executed by a small business, Striveworks, under contract with AFC and USASOC, they are
currently supporting efforts in Central Command and Indo-Pacific Command Areas of Responsibility.

28

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Chapter 2 - Endnotes
Jim Garamone, Chairman Discusses Future Defense Budgets, U.S. Department of Defense (Dec.
3, 2020), https://www.defense.gov/Explore/News/Article/Article/2433856/chairman-discusses-futuredefense-budgets/.

29

30
Summary of the 2018 National Defense Strategy, U.S. Department of Defense at 6 (2018), https://
dod.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf.

The Defense Science Board has proposed the level of 3.4% in the past to mirror typical practices
in the private sector. Department of Defense Research, Development, Test, and Evaluation (RDT&E):
Appropriations Structure, Congressional Research Service at 12 (Oct. 7, 2020), https://fas.org/sgp/
crs/natsec/R44711.pdf.

31

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32
The Future of Defense Task Force report similarly stated that “policy makers, industry, and thePentagon must work together to identify trade-offs within the defense apparatus to include legacy
systems and operations, which will allow for investment in technology and operational concepts
to address future challenges.” Future of Defense Task Force Report 2020, House Armed Services
Committee at 18 (Sept. 23, 2020), https://armedservices.house.gov/_cache/files/2/6/26129500-d20847ba-a9f7-25a8f82828b0/424EB2008281A3C79BA8C7EA71890AE9.future-of-defense-task-forcereport.pdf.

This should utilize wargaming, experimentation, and live-virtual-constructive environments whereverfeasible, and should mandate interoperability with the digital ecosystem. This point echoes the Futureof Defense Task Force, which recommended that every Major Defense Acquisition Program (MDAP)
should be required “to evaluate at least one AI or autonomous alternative prior to funding.” Id. at 7.

33

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Chapter 3: AI and Warfarep75

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AI Ready by 2025

Innovative
Concepts

Top-Down
Leadership

AI-Readiness
Performance
Goals
Advanced
Technologies
and R&D
AI-Enabled Allies
and Partnersp76

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Even with the right artificial intelligence (AI)-ready technology
foundations in place, the U.S. military will still be at a battlefielddisadvantage if it fails to adopt the right concepts and operations
to integrate AI technologies. Throughout history, the best adoptersand integrators, rather than the best technologists, have reaped themilitary rewards of new technology.1 The Department of Defense(DoD) should not be a witness to the AI revolution in military affairs,
but should deliver it with leadership from the top, new operating
concepts, relentless experimentation, and a system that rewards
agility and risk.
A new warfighting paradigm is emerging because of AI. Our competitors are making
substantial investments to take advantage of it. This idea has been called “algorithmic”
or “mosaic” warfare2; China’s theorists have called it “intelligentized” war.3 All of theseterms capture, in various ways, how a new era of conflict will be dominated by AI and pit
algorithms against algorithms. Advantage will be determined by the amount and quality ofa military’s data, the algorithms it develops, the AI-enabled networks it connects, the AIenabled weapons it fields, and the AI-enabled operating concepts it embraces to create
new ways of war.
Today’s DoD is trying to execute an AI pivot, but without urgency. Despite pockets ofimaginative reform and a few farsighted leaders, DoD remains locked in an Industrial
Age mentality in which great-power conflict is seen as a contest of massed forces andmonolithic platforms and systems. The emerging ubiquity of AI in the commercial realm
and the speed of digital transformation punctuate the risk of not pivoting fast enough. The
Department must act now to integrate AI into critical functions, existing systems, exercises,
and wargames to become an AI-ready force by 2025. Simultaneously, DoD must develop
more creative warfighting concepts that are paired with investments in future AI-enabledtechnologies to continuously out-innovate potential adversaries. If our forces are notequipped with AI-enabled systems guided by new concepts that exceed those of theiradversaries, they will be outmatched and paralyzed by the complexity of battle.

An AI-Ready DoD by 2025:
Warfighters enabled with baseline digital literacy and accessto the digital infrastructure and software required for ubiquitous
AI integration in training, exercises, and operations.

p
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“

A I A N D WA R FA R E“The Department must act
now to integrate AI into critical
functions, existing systems,
exercises and wargames to become
an AI-ready force by 2025.”

AI and Warfare

To compete, deter, and, if necessary, fight and win in future conflicts requires wholesale
adjustments to operational concepts, technologies, organizational structures, and how we
integrate allies and partners into operations. It will also require risk-based assessments ofboth the benefits and drawbacks of widespread integration of AI-enabled capabilities, toinclude future autonomous weapon systems. Lastly, it will require a willingness to engage
in bilateral and multilateral dialogues with our allies and partners to urge them to makesimilar AI pivots to ensure future interoperability.

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How AI Will Change Warfare.
AI-enabled warfare will not hinge on a single new weapon, technology, or operational concept;
rather, it will center on the application and integration of AI-enabled technologies into every
facet of warfighting. AI will transform the way war is conducted in every domain from undersea
to outer space, as well as in cyberspace and along the electromagnetic spectrum. It will
impact strategic decision-making, operational concepts and planning, tactical maneuvers
in the field, and back-office support. In this new kind of warfare, traditional confines of thebattlefield will be expanded through AI-enabled micro-targeting, disinformation, and cyber
operations, as described in Chapter 1 of this report. AI will reshape many attributes of war,such as its speed, tempo, and scale; the relationships service members have with machines;
the persistence with which the battlefield can be monitored; and the discrimination andprecision with which targets can be attacked. There will be a premium on speed and accuracy
in developing knowledge, acting, and reacting as the conflict unfolds.

““AI-enabled warfare will not
hinge on a single new weapon,
technology, or operational
concept; rather, it will center on theapplication and integration of AIenabled technologies into every
facet of warfighting.”p
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A I A N D WA R FA R E

DoD AI RDT&E Investments by Warfighting Domains, FY 2015-2025
Source: Govini

D o D AI R DT& E
Investments.

DoD’s AI investments are well distributed across the various warfighting domains (land, naval, air,space, cyber, electromagnetic spectrum, information), with over 25% of investments in multi-domain
applications of AI, signaling AI’s potential for integrating multi-domain operations. Investments in AI
applications for Space operations more than quadrupled from FY2019 to FY2025, from $500M to $2.2B,
increasing from 3% to almost 9% of AI-enabled investment.
Note the spending levels presented in figure represent estimates based on an analysis of DoD RDT&E
budget documents for FY2021-FY2025. See Analysis of DoD RDT&E Investments in AI, NSCAI (on final
with the Commission). Due to inherent quality issues in the source data, estimates presented containsignificant, difficult to estimate margins of error.

AI will make the process of finding and hitting targets of military value faster and more efficient.
It will also increase accuracy of target identification and minimize collateral damage. Currently,
this process generally involves passing data in a serial fashion from a sensor, through aseries of humans, to a platform that can shoot at the target. AI will help automate some of
the intermediate stages of the decision process. AI will also create opportunities for moreadvanced processes that would operate more akin to a web, fusing multiple sensors andplatforms to manage complex data flows and transmitting actionable information to humanoperators and machines across all domains.4
In war, many of the military uses of AI will complement, rather than supplant, the role of humans.
AI tools will improve the way service members perceive, understand, decide, adapt, and act
in the course of their missions. However, new concepts for military operations will also need toaccount for the changing ways in which humans will be able to delegate increasingly complex
tasks to AI-enabled systems. In the near term, this will be managed through the military’s
principle of “mission command,” which stresses decentralized execution and disciplined
initiative by subordinates who follow a commander’s intent. This human-centric approach tofighting should remain the standard for the foreseeable future. But as AI continues to advanceinto the cognitive and neuromorphic domain, and human-machine teaming becomes moresophisticated, the military will need to develop more imaginative concepts and organizational
constructs that take full advantage of AI technologies without relinquishing the principles that
undergird mission command.
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CHAPTER 3

Ways toOperationalize AI.

PrepareBusiness processes. Robotic Process Automation and AI-enabled analysis can generate significant
savings, speed administrative actions, and provide decision-makers with superior insights into corebusiness processes such as finance, budget, contracting, travel, and human resources.
Design. AI will support a holistic system-of-systems approach to developmental force design via digitalengineering, digital twins, and modeling and simulation to enable a more comprehensive understanding ofsystem vulnerabilities and adjacent capabilities, concepts, and technologies.
Readiness. AI will enhance training by relieving the cognitive burden of doing repetitive tasks that can be
performed better by machines. AI will be prevalent in all exercises and wargames and will enhance themilitary’s ability to train in live, virtual, and constructive environments.
Plan and task collection. Through automation, AI-enabled systems will optimize tasking and collection
for platforms, sensors, and assets in near-real time in response to dynamic intelligence requirements orchanges in the environment.

Sense and UnderstandCollect. At the tactical edge, “smart” sensors will be capable of pre-processing raw intelligence andprioritizing what data to transmit and store, which will be especially helpful in degraded or low-bandwidthenvironments.
Process. AI-enabled natural language processing, computer vision, and audiovisual analysis can vastly
reduce manual data processing. AI can also be used to automate data conversion such as translations
and decryptions, accelerating the ability to derive actionable insights.
Exploit and analyze. AI-enabled tools have the potential to augment filtering, flagging, and triage acrossmultiple data sets. Such tools can identify connections and correlations more efficiently and at a greater
scale than human analysts and can flag those findings and the most important content for human analysis.
AI will improve indications and warnings for military leaders.
• AI can fuse data from multiple sources, types of intelligence, and classification levels to produce
accurate predictive analysis in a way that is not currently possible.
• Advances in speech-to-text transcription and language analytics now enable reading comprehension,question answering, and automated summarization of large quantities of text.
Disseminate. AI will be able to automatically generate machine-readable versions of intelligence products
and disseminate them at machine speed so that computer systems across the IC and the military caningest and use them in real time without manual intervention.

DecidePlanning. AI decision-support applications will utilize modeling and simulation algorithms and real-timedata sets to optimize planning options.
Deciding. AI will integrate command-and-control networks and compress the speed of finding andattacking targets of military value.
Tasking, delegation, and distribution. Edge processing enhanced by delegated authorities will allow
frontline units to operate in a coordinated manner with minimal to no communications. AI techniques likemachine learning, and rule-based models will support network resiliency.

ExecuteLogistics and sustainment. AI-enabled predictive analytics, optimization, and tracking will improve
efficiency and effectiveness across all facets of logistics. Intelligent systems will aid in the developmentof courses of action for routine and contingency logistics and sustainment operations. Robotic process
automation will streamline human-centric maintenance and supply chain workflows.
Movement. AI will enhance the ability of commanders to maneuver, position, and protect units and forces.
AI will help network and coordinate movements of autonomous swarms via human-machine and machinemachine teaming.
Targeting. AI-enabled systems will expand a single targeting chain into a complex targeting web thatconsiders numerous variables across units and domains.
Precision and accuracy. Through AI-enabled smart weapons and autonomous platforms, AI will enable
the military to be more precise and discern friendly forces, non-combatants, and adversary targets withgreater accuracy.

p
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A I A N D WA R FA R EThis list of how AI might transform warfighting principles and capabilities—as well asothers like it—is by no means exhaustive. Innovation will lead to future capabilities that are
unknowable at present and will only become clearer in time.

“Innovation will lead to futurecapabilities that are unknowableat present and will only become
clearer in time.”

Stronger Together.

If the United States wants to fight with AI, it will need allies and partners with AI-enabled
militaries and intelligence agencies. Uneven adoption of AI will threaten interoperability
and the political cohesion and resiliency of U.S. alliances.5 As it deepens and expands
conventional defense arrangements across the globe––especially in Europe and the IndoPacific––the United States should incorporate AI and emerging technology into coordinateddefense and intelligence activities. Given the dual-use nature of many software-based
capabilities, DoD will need more flexibility to work with civilian agencies, companies, andresearch institutions in partner nations.
Promote AI interoperability and the adoption of critical emerging technologies among allies
and partners, including the Five Eyes, the North Atlantic Treaty Organization (NATO), andacross the Indo-Pacific. This should include:

Recommendation• Enhancing existing Five Eyes AI-related defense and intelligence efforts.
• Supporting NATO efforts to accelerate agreements on architectures and standards,
develop allied technical expertise, and pursue coalition AI use cases for exercises andwargames.
• Fostering the Joint Artificial Intelligence Center (JAIC)’s International AI Partnership for
Defense as a critical vehicle to further AI defense and security cooperation.6• Creating an Atlantic-Pacific Security Technology Partnership to improve military andintelligence capabilities and interoperability across European and Indo-Pacific allies
and partners.

p82

CHAPTER 3““Uneven adoption of AI will
threaten military interoperability,
and the political cohesion andresiliency of U.S. alliances.”

AI - enabled
A lliances andPar tnerships.

Achieve a State of Military AI Readiness by 2025.
To reach this goal, the DoD should:
Recommendation

Drive organizational reform through top-down leadership. Senior civilian and military officials
should set clear priorities and direction, empower subordinates, and accept higher
uncertainty and risk in pursuing new technologies. Specifically, DoD should:• Establish a high-level Steering Committee on Emerging Technology, tri-chaired by the
Deputy Secretary of Defense, the Vice Chairman of the Joint Chiefs of Staff, and thePrincipal Deputy Director of National Intelligence7;• Ensure that the JAIC Director remains a three-star general or flag officer with significant
operational experience who reports directly to the Secretary of Defense or Deputy
Secretary of Defense;• Appoint the Under Secretary of Defense for Research and Engineering as the co-chairand chief science advisor to the Joint Requirements Oversight Council; andp
83

A I A N D WA R FA R E• Assign an AI Operational Advocate on the staff of every Combatant Command. This
officer would perform a similar role to that played by the Staff Judge Advocate. He or she
would be an expert in AI systems, advise the commander and staff on the capabilities
and limitations of AI systems, and identify when AI-enabled systems are being used
inappropriately.
Develop innovative operational concepts that integrate new warfighting capabilities withemerging technologies.8 These concepts should strive for seamless interoperability acrossthe military services and across operational domains. The concept developers should
work closely with technologists to articulate how the military could fight most effectively infuture scenarios, and they should assume that AI-enabled capabilities will be ubiquitouson future battlefields. These concepts can also drive future investments.

Recommendation

By the end of 2021, establish AI and digital readiness performance goals.9 To achieve more
substantial integration of AI across DoD, the Deputy Secretary of Defense should:

Recommendation• Direct DoD components to assess military AI and digital readiness through existingreadiness management forums and processes. The Tri-Chaired Steering Committee
on Emerging Technology should work closely with the Under Secretary of Defense for
Personnel and Readiness and the Joint Staff to ensure the identified AI readiness criteria
are incorporated into the military services’ readiness reporting and resourcing strategies.
• Direct the military services to accelerate review of specific skill gaps in AI to inform
recruitment and talent-management strategies.10• Direct the military services—in coordination with the Under Secretary of Defense for
Acquisition and Sustainment, the Joint Staff, the Defense Logistics Agency, and the
JAIC—to prioritize integration of AI into logistics and sustainment systems wherever
possible.
• Integrate AI into major wargames and exercises to promote field-to-learn approaches
to technology adoption. Operators need persistent interaction with AI-enabledcapabilities early in the development cycle to generate critical feedback on how theyfunction and how they impact the mission. Widespread experimentation will advanceboth concept development and the performance of the technology.11• Incentivize experimentation with AI-enabled applications through the Warfighting
Lab Incentive Fund, which could be overseen by the proposed Tri-Chaired Steering
Committee.12
Define a joint warfighting network architecture by the end of 2021. The key objective of thisjoint warfighting network should be a secure, open-standards systems network that
supports the integration of AI applications at operational levels and across domains.13
It should be accessible by all of the military services and encompass several elements,
including command and control networks; data transport, storage, and secure processing;
and weapon system integration. The technical infrastructure for the network should be
supported by best practices in digital engineering.14 It should also be interoperable withthe digital ecosystem described in Chapter 2 of this report.15

Recommendation

Invest in priority AI R&D areas that could support future military capabilities, including thefollowing:

Recommendationp84

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A I A N D WA R FA R E

Chapter 3 - Endnotes
On military adoption, see, e.g., Michael C. Horowitz, The Diffusion of Military Power: Causes andConsequences for International Politics, Princeton University Press (2010).

1

The Defense Advanced Research Projects Agency (DARPA) Mosaic warfare central concept isbuilt around the “adaptability for U.S. forces and complexity or uncertainty for the enemy throughthe rapid composition and recomposition of a more disaggregated U.S. military force using humancommand and machine control.” Bryan Clark, et al., Mosaic Warfare: Exploiting Artificial Intelligenceand Autonomous Systems to Implement Decision-Centric Operations, CSBA at vi (Feb. 11, 2020),
https://csbaonline.org/research/publications/mosaic-warfare-exploiting-artificial-intelligence-andautonomous-systems-to-implement-decision-centric-operations/publication/1.

2

The People’s Liberation Army has developed a warfighting concept for what it calls “intelligentized
operations” with AI at its core. Within this construct, China theorizes that in future conflict, the central
contest will be between adversarial battle networks rather than traditional weapons platforms, andthat information advantage and algorithmic superiority will be a determinant of victory. See Elsa
Kania, Chinese Military Innovation in Artificial Intelligence, CNAS at 1 (June 7, 2019), https://www.
cnas.org/publications/congressional-testimony/chinese-military-innovation-in-artificial-intelligence(testimony before the U.S.-China Economic and Security Review Commission).

3

4
See Creating Cross-Domain Kill Webs in Real Time, DARPA (Sept. 18, 2020), https://www.darpa.mil/
news-events/2020-09-18a. See also AI Fusion: Enabling Distributed Artificial Intelligence to Enhance
Multi-Domain Operations & Real-Time Situational Awareness, Carnegie Mellon University (2020),
http://www.cs.cmu.edu/~ai-fusion/overview.

On military interoperability challenges related to AI, see Erik Lin-Greenberg, Allies and Artificial
Intelligence: Obstacles to Operations and Decision-Making, Texas National Security Review (Spring2020), https://tnsr.org/2020/03/allies-and-artificial-intelligence-obstacles-to-operations-and-decisionmaking/.

5

The AI Partnership for Defense, launched in September 2020, includes the United States and 12
partner nations: Australia, Canada, Denmark, Estonia, Finland, France, Israel, Japan, Norway, South
Korea, Sweden, and the United Kingdom. It seeks to “provide values-based global leadership” onadoption of AI in the defense and security context and align “like-minded nations to promote theresponsible use of AI, advance shared interests and best practices on AI ethics implementation,
establish frameworks to facilitate cooperation, and coordinate strategic messaging on AI policy.” Joint
Statement, AI Partnership for Defense (Sept. 15-16, 2020), https://www.ai.mil/docs/AI_PfD_ Joint_
Statement_09_16_ 20.pdf. The Partnership held its second formal dialogue in January 2021. DoD Joint
AI Center Facilitates Second International AI Dialogue for Defense, JAIC (Jan. 27, 2021), https://www.
ai.mil/news_01_ 27_ 21-dod_joint_ai_center_facilitates_second_international_ai_dialogue_for_defense.
html.

6

The Commission acknowledges Section 236 of the Fiscal Year 2021 National Defense Authorization
Act, which permits the Secretary of Defense to establish a steering committee on emerging
technology and national security threats composed of the Deputy Secretary of Defense, the Vice
Chairman of the Joint Chiefs of Staff, the Under Secretary of Defense for Intelligence and Security,
the Under Secretary of Defense for Research and Engineering, the Under Secretary of Defense for
Personnel and Readiness, the Under Secretary of Defense for Acquisition and Sustainment, the Chief
Information Officer, and such other officials of the Department of Defense as the Secretary determines
appropriate. However, the structure described in Sec. 236 does not include leadership from theIntelligence Community and will thus not drive the intended action. See Pub. L. 116-283, William M.
(Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

7

Notably, the National Defense Strategy emphasizes the need to “evolve innovative operational
concepts” and “foster a culture of experimentation and calculated risk-taking.” Tighter coordination
between concept writers and technologists would create a more dynamic cycle of technology
development and integration. Summary of the 2018 National Defense Strategy, U.S. Department of
Defense at 7 (2018), https://dod.defense.gov/Portals/1/Documents/pubs/2018-National-DefenseStrategy-Summary.pdf.

8

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Chapter 3 - Endnotes“Readiness” is a key measure of military effectiveness and remains at the heart of budget,
policy, and oversight debates on defense preparedness. In this context, DoD should establish key
AI and digital readiness performance objectives to measure and drive Department and service
accountability. See G. James Herrera, The Fundamentals of Military Readiness, Congressional
Research Service at 2 (Oct. 2, 2020), https://fas.org/sgp/crs/natsec/R46559.pdf.

910
As noted in Chapter 6 of this report, there is already an identified need for the creation of digitalcorps, civilian and military AI and AI-related career fields, an expansion of recruiting pathways, andthe creation of recruiting offices. The military services need to assess the number of personnel inthose fields and structures, not the need to establish them.

Although AI will be ubiquitous across all domains, the high-data volumes associated with the space,cyber, and information operations domains make use cases in those domains particularly well-suitedfor prioritized integration of AI-enabled applications in wargames, exercises, and experimentation.
11

12
The Warfighting Lab Incentive Fund is intended to spur field experiments and demonstrations to“evaluate, analyze and provide insight into more effective ways of using current capabilities, andto identify new ways to incorporate technologies into future operations and organizations.” See
Memorandum from the Deputy Secretary of Defense, Warfighting Lab Incentive Fund and Governance
Structure, U.S. Department of Defense (May 6, 2016), https://defenseinnovationmarketplace.dtic.mil/
wp-content/uploads/2018/02/DSD_memo.pdf.

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A I A N D WA R FA R E

The network envisioned is well-aligned with ongoing DoD efforts to embrace standards-driven
interoperability, system adaptability, and data-sharing. See Memorandum from the Secretary of theNavy, Secretary of the Army, and Secretary of the Air Force for Service Acquisition Executives andProgram Executive Officers, U.S. Department of Defense (Jan. 7, 2019), https://www.dsp.dla.mil/
Portals/26/Documents/PolicyAndGuidance/Memo-Modular_Open_Systems_ Approach.pdf.

13

14
Such as the goals and focus areas outlined in the DoD Digital Engineering Strategy; terms, any
knowledge, and guidelines shared as part of the Digital Engineering Body of Knowledge; andincorporating Section 231 of the National Defense Authorization Act for Fiscal Year 2020, which
requires the creation of a digital engineering capability to automate testing and evaluation. See
Department of Defense Digital Engineering Strategy, Office of the Deputy Assistant Secretary of
Defense for Systems Engineering (June 2018); see also Pub. L. 116-92, The National Defense
Authorization Act for Fiscal Year 2020, 133 Stat. 1198 (2019). For a description of the DigitalEngineering Body of Knowledge, see Andrew Monje, Future Direction of Model-Based Engineering
Across the Department of Defense, U.S. Department of Defense (Jan. 27, 2020), https://ac.cto.mil/wpcontent/uploads/2020/05/RAMS-Monje-27Jan2020-Future.pdf.

See the Chapter 2 Blueprint for Action for details on how this architecture should interact with thedigital ecosystem.

15

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CHAPTER 4

Chapter 4:
Autonomous Weapon
Systems and Risks Associatedwith AI-Enabled Warfarep89

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Mitigate Strategic Risks Associatedwith AI-Enabled Weapon Systems

Continue
Rigorous TEVV
Procedures

DevelopInternational
Standards ofPractice

Discuss
Risks with
Competitors

Limit Specific
Applicationsp90

CHAPTER 4World military powers both large and small are pursuing artificial
intelligence (AI)-enabled and autonomous weapon systems. Suchsystems have the potential to help commanders make faster, better,and more relevant decisions. They will enable weapon systems to
be capable of levels of performance, speed, and discrimination thatexceed human capabilities. And they will enable hitherto impossible
complex tasks. If properly designed, tested, and used, they could
improve compliance with International Humanitarian Law (IHL)1 byreducing the risk of accidental engagements, decreasing civilian
casualties, minimizing collateral infrastructure damage, and allowing
for detailed auditing of the decisions and actions of operators andtheir command chains. Although U.S. weapons platforms have
utilized autonomous functionalities for more than eight decades,2AI technologies have the potential to enable novel, sophisticatedoffensive and defensive autonomous capabilities.
The increasing use of AI technologies in weapon systems has generated importantquestions regarding whether such systems are lawful, safe, and ethical. Those critical ofusing AI technologies in weapons argue that states should negotiate limits or restrictions
on such systems and their use. There is also concern that autonomous weapon systems
may make conflict escalation more likely, and debate continues over what steps areneeded to ensure that such systems minimize the risk of unintended military engagements
or inadvertent and uncontrollable conflict escalation. Since 2014, the United Nations
Convention on Certain Conventional Weapons (CCW) has held meetings among states
parties to discuss the technological, military, legal, and ethical dimensions of “emerging
technologies in the area of lethal autonomous weapon systems (LAWS).”3 Specifically, it isexamining whether autonomous technologies will be capable of complying with IHL andwhether additional measures are necessary to ensure that humans maintain an appropriate
degree of control over the use of force.
The Commission has consulted with civil society, academic organizations, and government
agencies in studying the legal, ethical, and strategic questions that surround AI-enabled
and autonomous weapon systems, including their potential military benefits and risks,possible ethical issues coming to the fore, international efforts to regulate them, and theircompliance with IHL. The Commission offers the following four judgments to reflect its
conclusions on these discussions.

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Judgment 1: Provided their use isauthorized by a human commander or
operator, properly designed and tested
AI-enabled and autonomous weapon
systems have been and can continue
to be used in ways which are consistent
with IHL.
This judgment is grounded in several
elements of IHL:
• Distinction: The principle of distinction
holds that parties to an armed conflictmust distinguish between civilians
and combatants.4 Weapons withincreasingly accurate AI-enabled
target recognition systems have thepotential to reduce cases of target
misidentification, the leading cause
of inadvertent engagements during
combat operations, and thus reduce
civilian casualties and collateral
infrastructure damage.5

NSCAI Judgments Regarding
AI-Enabled and Autonomous
Weapon Systems• Provided their use is authorized by ahuman commander or operator, properly
designed and tested AI-enabled andautonomous weapon systems have been
and can continue to be used in ways
which are consistent with IHL.
• Existing DoD procedures are capableof ensuring that the United States will
field safe and reliable AI-enabled andautonomous weapon systems and use
them in a manner that is consistent with
IHL.
• There is little evidence that U.S.
competitors have equivalent rigorous
procedures to ensure their AI-enabled
and autonomous weapon systems will be
responsibly designed and lawfully used.

• The Commission does not support a
global prohibition of AI-enabled and• Proportionality: The principle ofautonomous weapon systems.
proportionality prohibits attacks
which would cause incidental loss ofcivilian life excessive to the anticipatedmilitary advantage.6 AI-enabled andautonomous weapon systems can andshould also be designed to carry out operations in accordance with human judgments
and directions regarding the proportionality of an attack. The moral reasoning involvedin this calculus—weighing anticipated military advantage against potential civilian
harm—remains the responsibility of a human commander.7• Accountability: Ensuring accountability and command responsibility is essential
to compliance with IHL. A human can and should be held accountable for thedevelopment, testing, use, and behavior of any autonomous weapon system, AIenabled or otherwise. Autonomous weapon systems operate within the same generalparameters as those used for human command and control systems, which are
specifically designed to ensure accountability for actions and compliance with IHL. This
is no different than for any other weapon system.8
The Commission endorses DoD’s body of policy that states that human judgment must
be involved in decisions to take human life in armed conflict. The kind of involvement
necessary for humans to remain accountable for the use of autonomous weapon systems
will vary depending on the time criticality of the situation as well as the operational context,
circumstance, and type of weapon systems involved.9 It is incumbent upon states toestablish processes which ensure that appropriate levels of human judgment are relied

p
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“

CHAPTER 4“... human judgment must be
involved in decisions to takehuman life in armed conflict.”
upon in the use of AI-enabled and autonomous weapon systems and that human operatorsof such systems remain accountable for the results of their employment.

Human accountability for the results of lethal engagements does not necessarily require
human oversight of every step of an engagement process. Once a human authorizes anengagement against a target or group of targets, subsequent steps in the attack sequencecan be completed autonomously without relinquishing human accountability. The exact
number of steps in this sequence is dependent on the system’s technical capabilities andthe context and must consider factors such as the uncertainty associated with the system’s
behavior and potential outcomes, the magnitude of the threat, and the time available
for action. For instance, an autonomous weapon system located in a rapidly changing
environment, such as an urban setting, for an extended period, may require more frequent
human authorization to ensure sufficient human accountability over autonomous actions
than an equivalent system, operated for a similar amount of time, in a highly predictable
and less populated environment—such as underwater or in space. This logic can andshould be incorporated into the system’s design, testing, and operational planning. Taking
these factors into consideration, when feasible and deemed necessary operation designs
should include points of required human guidance amid a sequence of automated actions.
At such points, a human must review the system’s status and authorize its next actions
before the system’s mission can continue. A blanket decision to compel every discrete stepin an engagement involving lethal force to be subject to explicit authorization by a humanis neither realistic nor desirable. Indeed, such a policy could instead spur commanders to
use less precise, unguided weapon systems that might result in greater levels of collateral
damage.

Judgment 2: Existing DoD procedures are capable of ensuring that the United States will
field safe and reliable AI-enabled and autonomous weapon systems and use them in amanner that is consistent with IHL.
DoD’s commitment to rigorous procedures for the development and use of autonomous
weapon systems—as well as its commitment to strong AI ethical principles10 —instills
confidence that it will be able to field AI-enabled and autonomous weapon systems that areused lawfully. DoD has comprehensive processes for ensuring that the use of any weapon
it fields is compliant with IHL and has a demonstrated commitment to operating within
IHL, minimizing civilian casualties, and learning from its mistakes.11 DoD has establishedp
93

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a cross-department legal group, the DoD Law of War Working Group, to “develop andcoordinate law of war initiatives and issues, such as analysis regarding the legality of
new means or methods of warfare under consideration by DoD components.”12 This
standing body is well positioned to examine implications for IHL as technology evolves
over time. The International Committee on the Red Cross (ICRC) has lauded the strengthand transparency of this system, listing the United States as one of eight countries thathave “national mechanisms to review the legality of weapons and that have made theinstruments setting up these mechanisms available to the ICRC.”13
In addition to baseline legal review, the Department has taken special precautions forautonomous weapon systems to ensure these systems undergo sufficient test and evaluation,verification and validation (TEVV). In 2012, DoD added to an extensive list of guiding directives
and instructions regarding weapons development within the Department by publishing DoD
Directive (DoDD) 3000.09, Autonomy in Weapon systems, which establishes DoD policy
for the development and use of autonomous weapon systems. It requires that all systems
be designed “to allow commanders and operators to exercise appropriate levels of humanjudgment over the use of force” and requires senior DoD leaders to approve any autonomous
weapon with lethal capabilities first when development begins, and again before fielding.14
It also mandates any autonomous or semi-autonomous weapon that undergoes a revision
to its operating state to undergo additional testing and evaluation. DoDD 3000.09 provides
important definitions and baseline requirements for such systems and must be reviewed
annually as technology evolves.15 Chapter 7 of this report provides specific recommendations
on how the United States should adapt its TEVV policies and capabilities to ensure it retains
justified confidence in AI-enabled systems.16““The U.S. commitment to
IHL is longstanding, and AIenabled and autonomous
weapon systems will not change
this commitment.”

In addition, DoD’s command and control procedures to authorize target selection andemployment of munitions are rigorous and designed to ensure compliance with IHL.
Operational commanders in the field are directly supported by lawyers embedded at
multiple levels to advise on decisions about the use of force. The U.S. commitment to IHLp94

CHAPTER 4

is long-standing, and AI-enabled and autonomous weapon systems will not change thiscommitment.17 These same principles will be ingrained into the design of those weapons,demonstrated in TEVV, and maintained by commanders overseeing their deployment. DoD’s
policy for autonomy in weapon systems and its adoption of ethical principles for AI in 2020
further highlight and reinforce this commitment.18
Judgment 3: There is little evidence that U.S. competitors have equivalent rigorous
procedures to ensure their AI-enabled and autonomous weapon systems will be
responsibly designed and lawfully used.
Battlefield success may become increasingly dependent on AI performance, and AIenabled weapons are likely to proliferate given the open-source and dual-use nature of AI.
This could cause pressure to mount on states to rapidly field new and untested systems
and algorithms. Such pressures could also tilt designs toward systems that react more
quickly, limiting the amount of time available for effective human oversight on engagement
decisions. U.S. competitors, particularly Russia and China, likely do not have equivalent
operational and targeting procedures to ensure the use of such systems is compliant
with IHL and to preserve human accountability over the use of lethal force. Russia andChina also have not published anything equivalent to DoDD 3000.09, outlining theirpolicies and processes governing the acquisition, development, testing, and deployment
of autonomous weapon systems. Unlike in the United States, in Russia and China theseprocesses are secret, if they exist at all.

“

U.S. competitors have demonstrated that they are unlikely to adhere to the same ethicaland legal standards in developing and utilizing AI-enabled weapon systems. Russia inparticular has historically demonstrated a willingness to deploy risky and under-tested
weapon systems, and it has deployed poorly performing unmanned ground vehicles“A global treaty prohibiting
the development, deployment,
or use of AI-enabled andautonomous weapon systems is
not currently in the interest of
U.S. or international security ...”p95

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with limited autonomous functionalities in combat in Syria.19 China is not only actively
pursuing increased autonomous functionality across a range of military systems, but it
is also currently exporting armed drones with autonomous functionalities to other nations.
This includes systems such as the Blowfish A3, which Ziyan, the system’s manufacturer,
advertises as capable of conducting autonomous, lethal, targeted strikes.20
Judgment 4: The Commission does not support a global prohibition of AI-enabled andautonomous weapon systems.
A global treaty prohibiting the development, deployment, or use of AI-enabled andautonomous weapon systems is not currently in the interest of U.S. or international
security and would be inadvisable to pursue for several reasons:• First is the basic definitional problem. With respect to autonomous weapon systems,
although the UN discussions about LAWS date back to 2014, states have yet to agree
on a definition for them. This makes any treaty negotiation problematic, as it may be
impossible to define the category of systems to be restricted in such a way that provides
adequate clarity while not overly constraining existing U.S. military capabilities.
• Even if the definitional problem could be overcome, we judge that, at present,
implementation of such an agreement would be impractical because compliance could
not be verified. There is no feasible technical manner in which states could demonstrate
to one another that specific weapon systems are or are not autonomous, or that they
possess or lack certain capabilities. Doing so would require foreign inspectors to haveshort-notice access to the underlying code in weapon systems of concern. States
are unlikely to agree to such an intrusive verification regime because revealing that
information would create unacceptable risks to the security of their systems.
• Additionally, the effects of a prohibition agreement likely would run counter to U.S.
strategic interests. Commitments from states such as Russia or China likely would be
empty ones. Such an agreement would not serve the goal of putting political pressure
on the states that are most likely to deploy autonomous weapon systems in unsafe
and ethically concerning ways. Rather, the primary impact of an agreement would be
to increase pressure on those countries that abide by international law, including theUnited States and its democratic allies and partners. Moreover, differing views on aprohibition among U.S. allies could deepen divisions among them on the employmentof AI-enabled autonomous weapon systems. If U.S. allies joined an agreement
while the United States did not, that divergence would likely hinder allied militaryinteroperability.21
For these reasons, we believe the practical and strategic problems with a prohibition
treaty outweigh potential benefits for the United States and its allies and partners,
and therefore we support the current U.S. policy in opposition to such an agreement.
However, this does not preclude other agreements or policies to address strategicrisks associated with AI-enabled and autonomous weapon systems, or the
future possibility of regulating specific types of technologies in AI-enabled andautonomous weapons technologies when such an agreement could be verifiable.

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Recommendations to Mitigate Strategic Risks of AI.
While the Commission believes that properly designed, tested, and utilized AI-enabled andautonomous weapon systems will bring substantial military and even humanitarian benefit, theunchecked global use of such systems potentially risks unintended conflict escalation andcrisis instability. The United States cannot assume that AI-enabled and autonomous weapon
systems fielded by other countries will be developed, acquired, and fielded with the appropriate
testing and verification to enable them to act as intended. Unintended escalations may occur for
numerous reasons, including when systems fail to perform as intended, because of challenging
and untested complexities of interaction between AI-enabled and autonomous weapon systems
on the battlefield, and, more generally, as the result of machines or humans misperceiving
signals or actions. AI-enabled systems will likely increase the pace and automation of warfare
across the board, reducing the time and space available for de-escalatory measures. Beyond
testing and robustness, we cannot assume that AI-enabled and autonomous weapons
developed by other nations will be designed to behave in accordance with IHL.
Therefore, countries must take actions which focus on reducing risks associated with AIenabled and autonomous weapon systems and encourage safety and compliance with IHL
when discussing their development, deployment, and use. Such efforts should and mustbe led by the United States, which is uniquely situated to lead them given its technicalexpertise, military prowess, and clear and transparent policies and ethical principles
governing the deployment and use of AI-enabled and autonomous weapon systems. The
Commission presents the following five recommendations regarding actions the UnitedStates should take to mitigate risks associated with AI-enabled and autonomous weapon
systems.
Strategic Risks
Associated with
AI-Enabled Weapons

NSCAI Recommended Actions

Nation states could
allow AI to authorize
employment of key
strategic weapon
systems.

Clearly and publicly affirm existing
U.S. policy that only human beings
can authorize employment of
nuclear weapons, and seek similarcommitments from Russia and China.

Prevent unintended nuclear
conflict due to AI-enabled
launch authorization.

Pursue technical means to verify
compliance with future arms control
agreements pertaining to AI-enabled
and autonomous weapon systems.

Enable effective verificationof potential futureagreements, which provides
confidence systems areworking as intended
without revealing sensitive
operational details.

States cannot verify
compliance withpotential international
agreements pertaining
to AI-enabled weapons.

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Objectives

U.S. Actions

A U T O N O M O U S W E A P O N S Y S T E M S A N D R I S K S A S S O C I AT E D W I T H A I - E N A B L E D WA R FA R E

Strategic Risks
Associated with
AI-Enabled Weapons

Global, unregulated
proliferation of
AI-enabled andautonomous weapons.

NSCAI Recommended Actions

Fund research on technical means
to prevent proliferation of AI-enabled
and autonomous weapon systems.

U.S. Actions with Allies
Poorly designed or
improperly utilized AIenabled weapons could
behave unpredictably.

Develop international standards ofpractice for the development anduse of AI-enabled and autonomous
weapon systems.

AI-enabled systems
could cause inadvertentconflict escalation.

Discuss AI’s impact on crisis stability
in the existing U.S.-Russia Strategic
Security Dialogue and create anequivalent meaningful dialogue with
China.

U.S. Actions with Russia and China

Objectives
Design and incorporate
proliferation-resistantfeatures into sophisticatedAI-enabled and autonomous
weapons, and potentiallyshare them with Russia andChina.

Set international norms
guiding responsible
development and use of
AI-enabled and autonomous
weapon systems.

Improve understandingof doctrine and develop
confidence-building
measures regarding use of
AI-enabled and autonomous
weapon systems.

Clearly and publicly affirm existing U.S. policy that only human beings can authorize employmentof nuclear weapons, and seek similar commitments from Russia and China. The United Statesshould make a clear, public statement that decisions to authorize nuclear weapons
employment must only be made by humans, not by an AI-enabled or autonomous system,and should include such an affirmation in the DoD’s next Nuclear Posture Review.22 This
would cement and highlight existing U.S. policy, which states that “[t]he decision to employ
nuclear weapons requires the explicit authorization of the President of the United States.”23
It would also demonstrate a practical U.S. commitment to employing AI and autonomous
functions in a responsible manner, limiting irresponsible capabilities, and preventing AI
systems from escalating conflicts in dangerous ways. It could also have a stabilizing effect,
as it would reduce competitors’ fears of an AI-enabled, bolt-from-the-blue strike from theUnited States and could incentivize other countries to make equivalent pledges.

Recommendation

The United States should also actively press Russia and China, as well as other statesthat possess nuclear weapons, to issue similar statements. Although joint politicalcommitments that only humans will authorize employment of nuclear weapons would not
be verifiable, they could still be stabilizing, responding to a classic prisoner’s dilemma: aslong as countries have confidence that others are not building risky command and control
structures that have the potential to inadvertently trigger massive nuclear escalation, theywould have less incentive to develop such systems themselves.24 While this norm is widely
accepted in the United States, it is unclear if Russia and China share the same strategic

p
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“

CHAPTER 4“... countries must take actions
which focus on reducing risks
associated with AI-enabled andautonomous weapon systems,
and encourage safety andcompliance with IHL when
discussing their development,
deployment, and use. Suchefforts should and must be ledby the United States ...”
concerns. Public reports indicate that Russia previously installed a “dead hand” system toautomate nuclear launch authorization,25 and China’s representatives in Track II dialogues
with the United States have been hesitant to state that China would make an equivalentcommitment. If neither Russia nor China is willing to agree to such a proposal, the UnitedStates should mount a strong international pressure campaign to condemn this decisionand highlight how Russia and China refuse to commit to responsible military uses of AI.

Recommendation

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99

Discuss AI’s impact on crisis stability in the existing U.S.-Russia Strategic Security Dialogue(SSD) and create an equivalent meaningful dialogue with China. The Departments of Stateand Defense should discuss AI’s impact on crisis stability within the existing U.S.-Russia
SSD and create an equivalent meaningful dialogue with China. The SSD is an interagency
bilateral dialogue focused on reducing misunderstandings and misperceptions on key
strategic issues and threats, as well as reducing the likelihood of inadvertent escalation.
Although the dialogue has traditionally focused on nuclear arms control and doctrine, it
has recently been used to also discuss emerging technologies and space security.26
The United States has no equivalent dialogue with China, as China has resisted U.S.
attempts to establish one for nearly a decade. However, within the last year there has
been increasing evidence that China is interested in formal talks with the United Statesconcerning AI-enabled military systems.27 This interest should be cultivated and leveraged
into establishing a U.S.-China SSD that includes the relevant military, diplomatic, andsecurity officials from both sides.

A U T O N O M O U S W E A P O N S Y S T E M S A N D R I S K S A S S O C I AT E D W I T H A I - E N A B L E D WA R FA R E

Given that the United States, Russia, and China are all aggressively pursuing AI-enabledcapabilities, and that Russia and China are likely to field AI-enabled systems that haveundergone less rigorous TEVV than comparable U.S. systems and may be unsafe or unreliable,
it is crucial to improve mutual understanding of each other’s military doctrines, including withrespect to AI-enabled and autonomous systems. The United States should use this channel tohighlight how deploying unsafe systems could risk inadvertent conflict escalation, emphasize
the need to conduct rigorous TEVV, and discuss where each side sees risks of a conventional
conflict rapidly escalating in order to better anticipate future responses in a crisis.

““... it is crucial to improve mutual
understanding of each other’s
military doctrines, including
with respect to AI-enabled andautonomous systems.”

These dialogues could also plant the seeds for a future, standing dialogue exclusively
focused on establishing practical and concrete confidence building measures surrounding
AI-enabled and autonomous weapon systems. For instance, the United States, Russia, andChina could work to develop an “international autonomous incidents agreement,” modeled
after the 1972 Incidents at Sea Agreement, which would seek to define the “rules of theroad” for behavior of autonomous military systems to create a more predictable operating
environment and avoid accidents and miscalculations.28 They could also agree to integrate“automated escalation tripwires” into systems that would prevent the automated escalationof conflict in specific scenarios without human intervention, to include nuclear weapons
employment as noted above.
Work with allies to develop international standards of practice for the development, testing, anduse of AI-enabled and autonomous weapon systems. The United States must work closelywith its allies to develop standards of practice regarding how states should responsibly
develop, test, and employ AI-enabled and autonomous weapon systems. This could build
off of existing work, to include the 11 Guiding Principles agreed to by the LAWS Group ofGovernmental Experts (GGE) in 2019,29 DoDD 3000.09, the DoD Ethical Principles for AI,
and the NSCAI Key Considerations for Responsible Development and Fielding of AI.30 Aspart of this effort, the DoD Law of War Working Group should meet regularly to review any
future technical developments that pertain to autonomous weapon systems and IHL, andthe tri-chaired Steering Committee on Emerging Technology (separately recommended

Recommendationp100

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by the Commission in Chapter 3 of this report) should advise on how such future technicaldevelopments impact policy and national defense.
The outputs of both groups should inform future DoD engagements with both allies andcompetitors on AI-enabled and autonomous weapon systems. Obtaining allied consensus
regarding standards for the development, testing, and use of such systems will set
important norms regarding these systems, help to ensure they are developed and usedsafely, and further highlight the commitment of the United States and its allies to ethicaland responsible uses of AI. The United States should also use these consultations tohighlight the ways in which AI will become a crucial part of future military operations anddevelop common frameworks guiding the appropriate and responsible use of AI-enabled
and autonomous weapon systems on the battlefield. This should seek to incentivize allies
to invest in the digital modernization of their own forces while also highlighting the risks tomilitary interoperability should any ally agree to join a treaty prohibiting LAWS.
Recommendation

Pursue technical means to verify compliance with future arms control agreements pertaining to
AI-enabled weapon systems. The United States should actively pursue the development oftechnologies and strategies that could enable effective and secure verification of futurearms control agreements involving uses of AI technologies. Although arms control of AIenabled weapon systems is currently technically unverifiable, effective verification will
likely be necessary to achieve future legally binding restrictions on AI capabilities. DoD
and the Department of Energy (DoE) should spearhead efforts to design and implement
technologies which could provide other countries confidence that an AI-enabled andautonomous weapon system is working as intended without revealing sensitive operational
details. For instance, it could examine ways for AI-enabled weapons platforms to produce
authenticatable records of operation, which could be spot-checked via international
challenge inspections if noncompliant activity is suspected. Technical creativity will benecessary to enable any future international restrictions on AI capabilities without revealing
sensitive information.

Recommendation

Fund research on technical means to prevent proliferation of AI-enabled and autonomous
weapon systems. Controlling the proliferation of AI-enabled and autonomous weapon
systems poses significant challenges given the open-source, dual-use, and inherently
transmissible nature of AI algorithms.31 The proliferation of makeshift autonomous weapon
systems which primarily utilize commercial components will be particularly difficult tocontrol via regulation and will necessitate capable intelligence sharing and domestic law
enforcement efforts to prevent their use by terrorists and other non-state actors. Regarding
more sophisticated autonomous weapon systems, the United States should double down
on efforts to design and incorporate proliferation-resistant features, such as standardized
ways to prevent unauthorized users from utilizing such weapons, or reprogramming asystem’s functionality by changing key system parameters. DoD and DoE should fundtechnical research on such methods, and if appropriate, these methods could be sharedwith Russia and China, or potentially other countries, to prevent the proliferation or loss ofcontrol of certain AI-enabled autonomous weapon systems.32

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A U T O N O M O U S W E A P O N S Y S T E M S A N D R I S K S A S S O C I AT E D W I T H A I - E N A B L E D WA R FA R EThis report does not contain a separate Blueprint for Action for Chapter 4. This is because given theimportance of the topic, the Commission chose to detail its arguments, recommendations, and thespecific actions required to implement them directly in this chapter. Additionally, further detail on how
the United States should adapt its TEVV policies to maintain confidence in AI systems can be found in
Chapter 7 and its associated Blueprint for Action, and recommendations on relevant changes to DoD
organizational structure can be found in Chapter 3.

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CHAPTER 4

Chapter 4 - Endnotes
1

IHL is also referred to as the law of armed conflict (LOAC) and the law of war.

Paul Scharre, Army of None: Autonomous Weapons and the Future of War, W.W. Norton & Co. at 39(April 24, 2018).

2

3
Background on Lethal Autonomous Weapon systems in the CCW, United
Nations (last accessed Jan. 11, 2021), https://www.unog.ch/80256EE600585943/(httpPages)/8FA3C2562A60FF81C1257CE600393DF6?OpenDocument.

Distinction, International Committee of the Red Cross (last accessed Jan. 15, 2021), https://casebook.icrc.org/glossary/distinction.

4

There is room for improvement in reducing target misidentification in U.S. military operations. In
the Afghanistan war, for example, a study indicated that about half of all civilian casualty incidents
caused by U.S. forces resulted from target misidentification. The use of AI-enabled systems to makemore accurate targeting decisions is perhaps the principal way in which the proper employmentof AI could make warfare more humane. Larry Lewis, Redefining Human Control: Lessons from theBattlefield for Autonomous Control, CNA at 4 (March 2018), https://www.cna.org/cna_files/pdf/DRM2017-U-016281-Final.pdf.

56
Proportionality, International Committee of the Red Cross (last accessed Jan. 15, 2021), https://casebook.icrc.org/glossary/proportionality.

See Paul Scharre, Army of None: Autonomous Weapons and the Future of War, W.W. Norton & Co. at
255-257 (2018).

7

For a properly designed and tested autonomous system which correctly carries out the commander’sintent, the commander is clearly accountable for the actions of that system. It is incumbent on states
to properly design, test, and use such systems and also put in place rigorous procedures ensuringthat any weapon use complies with IHL, including by ensuring individual accountability.

8

The Commission believes DoD’s existing formulation of “appropriate human judgment,” discussed
in the following Judgment, captures that necessary variation and ensures that any decision to employ
lethal force begins with and is under the control of human judgment, and that a human ultimately willremain accountable for any decision to employ force.

9

10
Press Release, U.S. Department of Defense, DoD Adopts Ethical Principles for Artificial Intelligence(Feb. 24, 2020), https://www.defense.gov/Newsroom/Releases/Release/Article/2091996/dod-adoptsethical-principles-for-artificial-intelligence/.

DoDD 5000.01 requires any weapon fielded by DoD to undergo a legal review to ensure compliancewith the Law of Armed Conflict (LOAC), adhering to the requirements set out in Article 36 of theProtocol Additional to the Geneva Conventions of 12 August 1949. DoDD 3000.09 and the DoD AI
Ethics Principles build on top of this baseline. See Department of Defense Directive 5000.01: The
Defense Acquisition System, U.S. Department of Defense at 9 (Sept. 9, 2020), https://www.esd.whs.
mil/Portals/54/Documents/DD/issuances/dodd/500001p.pdf? ver=2020-09-09-160307-310; Protocol
Additional to the Geneva Conventions of 12 August 1949, and relating to the Protection of Victims ofInternational Armed Conflicts (Protocol I), 8 June 1977, International Committee of the Red Cross (lastaccessed Jan. 5, 2021), https://ihl-databases.icrc.org/applic/ihl/ihl.nsf/ WebART/470-750045.
11

Department of Defense Directive No. 2311.01: DoD Law of War Program, U.S. Department of
Defense at 11 (July 2, 2020), https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodd/231101p.pdf? ver=2020-07-02-143157-007.

12

A Guide to the Legal Review of New Weapons, Means and Methods of Warfare: Measures to
Implement Article 36 of Additional Protocol I of 1977, International Committee of the Red Cross at 5, n.
8 (Jan. 2006), https://www.icrc.org/en/doc/assets/files/other/icrc_002_0902.pdf.

13

14
Department of Defense Directive 3000.09: Autonomy in Weapon systems, U.S. Department of
Defense at 2 (Nov. 21, 2012, incorp. change 1 May 8, 2017), https://www.esd.whs.mil/portals/54/
documents/dd/issuances/dodd/300009p.pdf. The weapons-review processes established in DoDD
3000.09 are designed specifically to ensure that any U.S. autonomous weapon system complies with
IHL principles such as discrimination and proportionality while also maintaining appropriate levels ofhuman judgment and ensuring accountability.

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Department of Defense Instruction 5025.01: DoD Issuances Program at 22 (Aug. 1, 2016, incorp.
change 3 May 22, 2019), https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/502501p.
pdf? ver=2020-05-20-081854-657.

15

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for future R&D needed to advance capabilities for Testing, Evaluation, Verification,
and Validation of AI systems, see the section on “System Performance” in Key Considerations for
Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on filewith the Commission).

16

17
The DoD Law of War manual serves as a detailed resource for all DoD personnel responsible for
implementing the law of war and executing military operations. See Department of Defense Law of
War Manual, U.S. Department of Defense (Dec. 2016), https://dod.defense.gov/Portals/1/Documents/
pubs/DoD%20Law%20of%20War%20Manual%20-%20June%202015%20Updated%20Dec%202016.
pdf? ver=2016-12-13-172036-190.
18
Press Release, U.S. Department of Defense, DoD Adopts Ethical Principles for Artificial Intelligence(Feb. 24, 2020), https://www.defense.gov/Newsroom/Releases/Release/Article/2091996/dod-adoptsethical-principles-for-artificial-intelligence/.

David Axe, Don’t Panic, But Russia Is Training its Robot Tanks to Understand Human Speech,
Forbes (June 30, 2020), https://www.forbes.com/sites/davidaxe/2020/06/30/dont-panic-but-russia-istraining-its-robot-tanks-to-understand-human-speech/?sh=7373377914f2.

19

20
Patrick Tucker, SecDef: China Is Exporting Killer Robots to the Mideast, Defense One (Nov. 5,2019), https://www.defenseone.com/technology/2019/11/secdef-china-exporting-killer-robotsmideast/161100/.
21
The United States has expressed similar concerns with respect to treaties banning cluster munitions
and nuclear weapons. See Q&A: Convention on Cluster Munitions, HRW (Nov. 6, 2010), https://www.
hrw.org/news/2010/11/06/qa-convention-cluster-munitions#; Heather Williams, What the Nuclear
Ban Treaty Means for America’s Allies, War on the Rocks (Nov. 5, 2020), https://warontherocks.
com/2020/11/what-the-nuclear-ban-treaty-means-for-americas-allies/. As of March 2021, no ally withwhich the United States has a mutual defense agreement has expressed support for a treaty banning
LAWS.
22
The Commission recognizes that AI should assist in some aspects of the nuclear command andcontrol apparatus, such as early warning, early launch detection, and multi-sensor fusion to validate
single sensor detections and potentially eliminate false detections.

Nuclear Matters Handbook 2020, Office of the Deputy Assistant Secretary of Defense for Nuclear
Matters at 18 (2020), https://fas.org/man/eprint/nmhb2020.pdf.

23

There could be other reasons countries may delegate nuclear weapons launch authority toautonomous systems, particularly if leadership trusts machines to execute launch orders more thanhumans. A political agreement is unlikely to be able to address these concerns, although offering it
would highlight how other nations are engaging in irresponsible and dangerous behavior.

24

Michael Peck, Russia’s ‘Dead Hand’ Nuclear Doomsday Weapon is Back, The National Interest(Dec. 12, 2018), https://nationalinterest.org/blog/buzz/russias-dead-hand-nuclear-doomsday-weaponback-38492.

25

26
Press Release, U.S. Department of State, Deputy Secretary Sullivan’s Participation in Strategic
Security Dialogue with Russian Deputy Foreign Minister Sergey Ryabkov (July 17, 2019),
https://2017-2021.state.gov/deputy-secretary-sullivans-participation-in-strategic-securitydialogue-with-russian-deputy-foreign-minister-sergey-ryabkov/index.html; Press Release, U.S.
Department of State, The United States and Russia Hold Space Security Exchange (July 28, 2020),
https://2017-2021.state.gov/the-united-states-and-russia-hold-space-security-exchange/index.html.

Over the last year, Chinese experts have participated actively in several Track II dialogues with U.S.
experts on the safety of military AI systems, potentially signaling a desire for formal government-togovernment communication on these issues.

27

p
104

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Chapter 4 - Endnotes
See Michael C. Horowitz and Paul Scharre, AI and International Stability: Risks and ConfidenceBuilding Measures, Center for a New American Security (Jan. 2021), https://s3.us-east-1.amazonaws.
com/files.cnas.org/documents/AI-and-International-Stability-Risks-and-Confidence-BuildingMeasures.pdf?mtime=20210112103229&focal=none.

28

Final Report of the 2019 Session of the Group of Governmental Experts on Emerging Technologies
in the Area of Lethal Autonomous Weapon systems, Group of Governmental Experts of theHigh Contracting Parties to the Convention on Prohibitions or Restrictions on the Use of Certain
Conventional Weapons Which May Be Deemed to Be Excessively Injurious or to Have Indiscriminate
Effect, CCW/MSP/2019/CRP.2/Rev.1, (Nov. 13-15, 2019), https://undocs.org/CCW/MSP/2019/9.

29

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for future action on International collaboration and cooperation, see the section on“System Performance” in Key Considerations for Responsible Development & Fielding of Artificial
Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).
30

p
105

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See Chapter 14 of this report for additional information on the difficulty of using export controls toprevent the transfer of AI algorithms.

31

32
Along these lines, the United States shared the technology for Permissive Action Links (PALs),
which prevent the unauthorized arming of a nuclear weapon, with the Soviet Union in the 1970s. It
is not clear if there is an equivalent technology to PALs for AI, one which would reduce the risk ofunauthorized or accidental escalation by an AI system without simultaneously significantly increasing
the military performance of that system. If equivalent technologies are developed, cooperation would
have to be considered on a case-by-case basis.

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CHAPTER 5

Chapter 5: AI and the Futureof National Intelligencep
107

A I A N D T H E F U T U R E O F N AT I O N A L I N T E L L I G E N C E

2025: AI-Enabled Intelligenceand Predictive Analysis

Innovative
Approaches
to Human andMachine Teaming

Empowering
Science andTechnology
Leadership

Capitalizing on AI
Analysis of OpenSource Information

Prioritizing
the Collectionof Scientificand TechnicalIntelligence

Building the
IC Information
Technology
Environmentp108

CHAPTER 5

Intelligence will benefit from rapid adoption of artificial intelligence (AI)enabled technologies more than any other national security mission.
As every possible platform—both machine and human—contributes
to the global information grid, and as the number of sensors grows
exponentially, the volume, velocity, and variety of data threaten to
overwhelm intelligence analysis. Ascertaining the veracity and valueof information will be harder. Analysts will be challenged to provide
the context crucial for turning information into actionable intelligence.
AI will help intelligence professionals find needles in haystacks, connect the dots, and disrupt
dangerous plots by discerning trends and discovering previously hidden or masked indications
and warnings. AI-enabled capabilities will improve every stage of the intelligence cycle fromtasking through collection, processing, exploitation, analysis, and dissemination. AI algorithms
can sift through vast amounts of data to find patterns, detect threats, identify correlations, andmake predictions. AI tools can make satellite imagery, communications signals, economic
indicators, social media data, and other large sources of information more intelligible. AI
can find correlations between open-source data and other sources of intelligence, and help
the Intelligence Community (IC) be more precise, efficient, and effective in its targeting andcollections activities. The constellation of current and emerging AI technologies applicable tointelligence missions includes computer vision for imagery analysis, biometric technologies(such as face, voice, and gait recognition), natural language processing, and algorithmic searchand query functions for large databases, among others. Most important, AI enables data fusionfrom dissimilar data streams to create a composite picture.1
In military scenarios—against technologically advanced adversaries, rogue states, or terrorist
organizations—AI-enabled intelligence, surveillance, and reconnaissance platforms and AIenabled indication and warning (I&W) systems will be critical for the kind of advanced warfighting
capabilities discussed in Chapter 3 of this report. Through automation, AI-enabled systems will
optimize tasking and collection for platforms, sensors, and assets in near-real time in response
to dynamic intelligence requirements or changes in the environment. At the tactical edge,“smart” sensors will be capable of pre-processing raw intelligence and prioritizing the data totransmit and store, which will be especially helpful in degraded or low-bandwidth environments.
Once collected, intelligent processing systems can triage the information, identify trends andpatterns, summarize key implications, and prepare the highest-priority information for humanreview (or flag items of particular interest, based on analyst-defined conditions). This includes
advanced I&W systems that will enable warfighters to anticipate and understand emerging
threats earlier, allowing them to proactively shape the environment, as well as systems close to
the tactical edge identifying adversarial denial and deception efforts. When paired with humanjudgment, these capabilities will enhance all-domain awareness, lead to tighter and more
informed decision cycles, offer recommendations for different courses of action, and allow
rapid counter-actions to adversary actions.

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The need to adapt is made urgent by the quickening diffusion of these new technologies.
Once exquisite IC capabilities are now in wide use around the world.2 Our adversaries’ ability
to quickly adopt AI tools means that the IC may be more vulnerable to deception, information
operations, sources and methods exposure, cyber operations, and counterintelligence
activities. The IC has been an early mover within the government in establishing some of
the underlying infrastructure to enable the adoption of AI, such as contracting an IC-widecommercial cloud service in 2013.3 In addition, the IC’s 2019 Augmenting Intelligence
using Machines (AIM) initiative provided direction and a framework for broader adoption,and some intelligence agencies have made great strides in AI adoption, putting themahead of others in government. Still, critical barriers in authorities, policies, budgets, data
sharing, and technical standards keep the IC from fully realizing its potential, and noneof these recommendations will be effective without substantial reforms of the security
clearance process.
An Ambitious Agenda: AI-Ready by 2025.
To build on the progress that individual agencies have made, the IC should set theambitious goal of adopting and integrating AI-enabled capabilities across every possible
aspect of the intelligence enterprise as part of a larger vision for the future of intelligence.

An AI-Ready IC by 2025:
Intelligence professionals enabled with baseline digital literacy
and access to the digital infrastructure and software required for
ubiquitous AI integration in each stage of the intelligence cycle.
Starting immediately, the IC should prioritize automating each stage of the intelligencecycle to the greatest extent possible and processing all available data and information
through AI-enabled analytic systems before human analyst review. Products should also
be disseminated at machine speed–which means they must be in machine-readable
formats–and systems across the IC must be able to ingest and use them without manualintervention. Optimizing AI-enabled systems in this way will require an entirely different
approach to the creation and review of finished intelligence products. The IC should
require that all intelligence products include both a human-readable version and, justas important, an automated machine-readable version that can be ingested into otheranalytic systems throughout the IC. All future intelligence systems should be optimized for
AI-oriented data collection and processing.

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CHAPTER 5““The IC should require that all
intelligence products include
both a human-readable
version and, as importantly, an
automated machine-readable
version that can be ingested
into other analytic systems
throughout the IC.”
Once the IC has automated its processes within individual intelligence disciplines, it
should fuse those individual processes into a continuous pipeline of all-source intelligence
analysis processed through a federated architecture of continually learning analytic
engines. This transformational change could lead to insights arising from human-machineteaming that are beyond the current limits of unaided human cognition. Such a system
would bring greater clarity to ongoing developments and also enable more accurate andreliable predictive analysis of emerging threats. As analysts gain more trust in AI-enabled
systems, the ratio of human- to machine-led analysis will tip more heavily toward machines.

p111

Graphic 4.1: Jackson Infographic.
A I A N D T H E F U T U R E O F N AT I O N A L I N T E L L I G E N C E

Current

AI - Enabled National
Intelligence.
Days to Weeks

HUMINT
SIGINT
IMINT
MASINT
Planning
andTasking

OSINT
Collection

Processing

Exploitation and Analysis

Dissemination

Optimized: AI-Enabled Automation within Current Intelligence Disciplines
Hours to Days
AI-Enabled Tools
HUMINT
SIGINT
IMINT
MASINT

Fused All
SourceIntelligence

OSINT

Planning
andTasking

Collection

Processing

Exploitation
and Analysis

Dissemination

Transformed: AI-Enabled All Source Intelligence and Predictive Analysis
Continuous Cycle
AI-Enabled Tools
HUMINTSIGNIT

Fused All Source Intelligenceand Predictive Analysis

IMINT
MASINT
OSINT

Planning
andTasking

Collection

Processing

Exploitation
and Analysis

Disseminationp112

CHAPTER 5

Preparing for an AI-ready 2025 demands the following actions:
Recommendation

Empower the IC’s science and technology leadership. The Director of National Intelligence(DNI) should designate the Director of Science and Technology (S&T) within the Office of
the Director of National Intelligence (ODNI) as the IC’s Chief Technology Officer (CTO) andtask and empower this position to drive the IC’s adoption of AI-enabled applications tosolve operational intelligence requirements. To do so, the IC CTO should oversee the AIM
strategy, establish and enforce common technical standards and policies necessary to
rapidly and responsibly scale AI-enabled applications across the IC, and lead acquisition
reform to ensure that the IC can rapidly procure and field systems to its intelligenceprofessionals. The IC CTO should be granted additional authorities for establishing policies
on and supervising IC research and engineering, technology development, technology
transition, appropriate prototyping activities, experimentation, and developmental testing
activities.

Recommendation

Change risk management practices to accelerate new technology adoption. The IC needs
to balance the technical risks involved in bringing new technologies online and quickly
updating them with the substantial operational risks that result from not keeping pace,similar to DoD. Regular software upgrades should be automated to the extent possible. To
share software tools more easily among agencies, reciprocal accreditation of information
technology systems should be the standard.4““The IC needs to balance
the technical risks involvedin bringing new technologies
on line and quickly updatingthem with the substantial
operational risks that result
from not keeping pace ...”p
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A I A N D T H E F U T U R E O F N AT I O N A L I N T E L L I G E N C E

To coordinate these changes, the ODNI should establish a Senior Risk Management
Council focused on technology modernization.5 Its task should be to weigh the risks ofadopting new technologies with the opportunity costs of not doing so. Its goal should beto ensure that analysts have access to the tools they need to do their jobs.
The IC will need support from the intelligence committees in Congress––for example, in theflexible use of funds within a more agile software development framework. To support theargument for greater flexibility, the IC should develop data-driven ways of communicating
operational gains, as well as credible assessments of the risk of inaction.
Improve coordination and interoperability between the IC and DoD. The IC must aggressively
pursue automated interoperability with the DoD for intelligence operations conducted atmachine speeds.6 To do this, security managers and network administrators must build
greater confidence in fast and secure data exchanges. ODNI, the Under Secretary of
Defense for Intelligence and Security, and the Joint Artificial Intelligence Center (JAIC)
should coordinate more on intelligence-related AI projects to minimize duplication ofeffort while maximizing common approaches to AI capability development, testing andevaluation, deployment, international engagement, and policies and authorities. They
should work together to create interoperable and sharable resources and tools––suchas those envisioned in the AI R&D ecosystem described in Chapter 2 of this report––andshould establish a culture of sharing all AI-enabled capabilities whenever feasible.7

Recommendation

Capitalize on AI-enabled analysis of open-source and publicly available information.8 The IC
should develop a coordinated and federated approach to applying AI-enabled applications
to open-source intelligence (OSINT) and should strive to integrate open-source analysis
into existing intelligence processes wherever possible in every intelligence domain.9

Recommendation

Prioritize and accelerate collection of scientific and technical intelligence to better understandadversary capabilities and intentions. Such collection requires the IC to significantly increase
the technical sophistication, capabilities, and capacity of its analytic workforce. That must
involve aggressive efforts to train, recruit, and retain analysts who have the requisite
skills. These analysts must guide collection requirements and provide timely, accurate
assessments. To better coordinate intelligence on these topics, including collecting on
scientific and technical cooperation among our competitors, the DNI should appoint an
Emerging Technology Collection Executive within the National Intelligence Council.10

Recommendation

To recruit more S&T experts into the IC, aggressively pursue security clearance reform forclearances at the Top Secret level and above, and enforce security clearance reciprocity
among members of the IC. ODNI should develop and implement an AI-enabled data andscience-based approach to security-clearance adjudication that significantly shortens
investigation timelines.11

Recommendationp114

CHAPTER 5

Recommendation

Advance and continue to develop a purpose-built IC Information Technology Environment thatcan fuse intelligence from different domains and sources. An AI-enabled technical architectureof this kind could help autonomously integrate intelligence across stove-piped intelligencedomains, which currently often require manual intervention to share raw data or finishedanalysis.12 Doing so would help the IC blend insights from different streams of information tocreate a composite picture. For example, signals intelligence often depends upon human
intelligence or geospatial intelligence. Likewise, the value of human intelligence can almost
always be enhanced by layering signals intelligence or open-source information on top of it.

Recommendation

Embrace fused, predictive analysis as the new standard. Successfully fusing all-source/alldomain intelligence will enable accurate predictive analysis in a way that is not currently
possible. The government’s response to the COVID-19 virus has offered glimpses into
the potential for fused data sets to inform such analysis. For example, U.S. Northern
Command (working with the JAIC and the National Guard Bureau) built predictive models
from dozens of different data sets that helped to identify COVID-19 hotspots and reconcile
demands for critical supplies.13

Recommendation

Develop innovative human-centric approaches to human-machine teaming. The kind of data
fusion envisioned here through autonomous machine-to-machine integration will require
new concepts for human-machine teaming that optimize the strengths of each.14 The IC
will need new approaches that amplify and extend human cognition to effectively handle
the scale and complexity of the information generated by all-source intelligence analytic
engines. When developing these systems, the IC must understand and make deliberate
decisions on when and under what conditions the human or machine should act alone andunder what conditions human-machine teaming is desirable.

““The kind of data fusionenvisioned here throughautonomous machine-tomachine integration will require
new concepts for humanmachine teaming that optimize
the strengths of each.”p
115

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Chapter 5 - Endnotes
For additional information on AI-enabled use cases throughout the intelligence cycle, see thediscussion on “Applications” in Maintaining the Intelligence Edge: Reimagining and ReinventingIntelligence Through Innovation, CSIS Technology and Intelligence Task Force at 8-22 (Jan. 13, 2021),
https://csis-website-prod.s3.amazonaws.com/s3fs-public/publication/210113_Intelligence_Edge.pdf.

1

2
AIM Initiative: A Strategy for Augmenting Intelligence Using Machines, Office of the Director of
National Intelligence (2019), https://www.dni.gov/files/ODNI/documents/AIM-Strategy.pdf (foreword by
the Honorable Sue Gordon, Principal Deputy Director of National Intelligence).

Frank Konkel, The Details about the CIA’s Deal with Amazon, The Atlantic (July 14, 2014),
https://www.theatlantic.com/technology/archive/2014/07/the-details-about-the-cias-deal-withamazon/374632/.
3

In adopting new software systems, the IC follows a risk-management framework developed by the
National Institute of Standards and Technology (NIST). While it is a useful framework overall, it canalso create delays or prevent the IC from keeping up with cutting-edge AI tools that are commerciallyavailable. For more information, see FISMA Implementation Project, NIST (Dec. 3, 2020), https://csrc.
nist.gov/projects/risk-management/rmf-overview.

4

The Senior Risk Management Council would help the IC implement guidance from the proposed
Tri-Chair Committee on Emerging Technology and function similarly to the role this commission
recommended for the Under Secretary of Defense for Research and Engineering as a co-chair on the
Joint Requirements Oversight Council in DoD.

5

For more information, see Kent Linnebur, et al., Intelligence After Next: The Future of the IC
Workplace, MITRE Center for Technology and National Security (Nov. 1, 2020), https://www.mitre.org/
sites/default/files/publications/pr-20-1891-intelligence-after-next-the-future-of-the-ic-workplace.pdf.

67These efforts should leverage the JAIC’s Joint Common Foundation (JCF).

Pub. L. 116-260, The Consolidated Appropriations Act (2021), Division W, Section 326 (“Opensource intelligence strategies and plans for the intelligence community”), Section 623 (“Independent
study on open-source intelligence”), and Section 624 (“Survey on Open Source Enterprise”) provide astarting point for the IC to reimagine the role of open-source intelligence.

8

It is important to note that open-source intelligence (OSINT) is not limited to traditional media
sources (newspapers, radio broadcasts, etc.) and social media. OSINT also includes publicly
available information such as public government data sources (official reports, budget documents,
hearing testimonies, etc.), professional and academic publications, commercial data sources(industry reports, financial statements, commercial imagery, etc.), and more.

910
For additional information, see the discussion on “Elevating Technical Intelligence” in Maintaining
the Intelligence Edge: Reimagining and Reinventing Intelligence Through Innovation, CSIS Technology
and Intelligence Task Force at 12 (Jan. 13, 2021), https://csis-website-prod.s3.amazonaws.com/s3fspublic/publication/210113_Intelligence_Edge.pdf.
11
For more information on the need for an academic and scientific review of behavioral approaches
to security clearance adjudication, see David Luckey, et al., Assessing Continuous Evaluation
Approaches for Insider Threats: How Can the Security Posture of the U.S. Departments and Agencies
Be Improved?, RAND Corporation at 28-34 (2019), https://www.rand.org/pubs/research_reports/
RR2684.html.

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Chapter 5 - Endnotes
12

The technical aspects of such an environment are covered in more detail in Chapter 2 of this report.

Air Force General Terrence J. O’Shaughnessy, Commander, U.S. Northern Command & Army
Lieutenant General Laura J. Richardson, Commander, U.S. Army North, Transcript: US NORTHCOM
and ARNORTH Commanders Discuss Ongoing COVID-19 Efforts, U.S. Department of Defense (April
21, 2020), https://www.defense.gov/Newsroom/ Transcripts/ Transcript/Article/2160070/us-northcomand-arnorth-commanders-discuss-ongoing-covid-19-efforts/.

13

p
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14
See Kenneth M. Ford, et al., Cognitive Orthoses: Toward Human-Centered AI, AI Magazine at 7
(Winter 2015), https://doi.org/10.1609/aimag.v36i4.2629; John Laird, et al., Future Directions in Human
Machine Teaming Workshop, U.S. Department of Defense (July 16-17, 2019), https://basicresearch.
defense.gov/Portals/61/Future%20Directions%20in%20Human%20Machine%20Teaming%20
Workshop%20report%20%20%28for%20public%20release%29.pdf; Gagan Bansal, et al., Is the Most
Accurate AI the Best Teammate? Optimizing AI for Teamwork, AAAI 2021 (Feb. 2021), https://www.
microsoft.com/en-us/research/publication/is-the-most-accurate-ai-the-best-teammate-optimizing-aifor-teamwork/.

p118

CHAPTER 6

Chapter 6: Technical Talent
in Governmentp
119

T E C H N I C A L TA L E N T I N G O V E R N M E N T

Improve Technical Talent in Government

Organize

Recruit

Build

Employp120

CHAPTER 6

The artificial intelligence (AI) competition will not be won by the sidewith the best technology. It will be won by the side with the best,
most diverse and tech-savvy talent. The Department of Defense(DoD) and the Intelligence Community (IC) both face an alarming
talent deficit. This problem is the greatest impediment to the U.S.
being AI-ready by 2025. National security agencies need moredigital experts now or they will remain unprepared to buy, build, anduse AI and its associated technologies. Digital expertise is the mostimportant requirement for government modernization, but few parts ofgovernment have adequately invested in building a digital workforce.1““DoD and the IC both face analarming talent deficit.”

To expand its digital and AI workforce, the government needs to:• Organize technologists within government through a talent management systemdesigned to house highly skilled specialists;• Recruit people who already have the skills the government needs, such as industry
experts, academics, and recent college graduates;• Build its own workforce by training and educating current and future government
employees; and• Employ its digital workforce more effectively to ensure digital talent can perform
meaningful work once they are in government.
p
121

“
“

T E C H N I C A L TA L E N T I N G O V E R N M E N T“Digital expertise is the mostimportant requirement forgovernment modernization ...”The Current Model.

Government organizations responsible for creating AI solutions are struggling to build
their digital workforce. Real obstacles impede recruiting and retaining AI practitioners andbroader digital talent. The government does not compete with private-sector salaries andsuffers from a cumbersome hiring process, and all reforms are hindered by a slow security
clearance process.
We should not accept an undesirable status quo as the inevitable future. The government
can compete with the private sector for talent. The government may not match privatesector salaries, but it does offer the opportunity to tackle national security challenges
and to make a substantial contribution to society. The biggest obstacle hindering therecruitment of digital talent is not compensation. It is the perception, and too often thereality, that it is difficult for digital talent in government to perform meaningful work, withmodern computing tools, at the forefront of a rapidly changing field.2“We should not accept an
undesirable status quo as
the inevitable future. The
government can compete withthe private sector for talent.”p122

CHAPTER 6“

The Commission is not persuaded by the argument that the government should focus
on project management and data collection and management, and outsource alldevelopment. We have heard this argument from leaders who do not believe it is feasible
for the government to hire or train its own AI experts. Interestingly, we have not heard thisargument from industry.

“Government strategies
that do not develop agovernment technicalworkforce are short-sighted.”
Government strategies that do not develop a government technical workforce are shortsighted. Government agencies that rely solely on contractors for digital expertise will
become incapable of understanding the underlying technology well enough to makesuccessful acquisition decisions independent of contractors.3 This situation createsnational security risks. While contractors should continue to play a critical role, they
are incentivized, and in some sense required, to fulfill the terms of their contract, not topursue overall system improvements or to disagree with poorly thought-out requirements
or ineffective strategies. As a result, agencies that rely on contractors force their digital
experts to have a secondary voice in key decisions, even those related to their field ofexpertise. The government will always have contractors. But the government can andshould grow its own digital workforce.
Organize.
How a digital workforce is organized is as important as the workforce’s level of expertise.
To generate and manage a proficient digital workforce at the scale required by the national
security enterprise, the government needs to establish a talent management framework
tailored to the task.

Recommendationp123

Departments and select agencies should create Digital Corps. We propose that departments
and select agencies should create Digital Corps that would recruit, train, and educate
personnel; place people in and remove them from digital workforce billets; manage digital
careers; and set standards for digital workforce qualifications. Departments and selectagencies would create billets for members of these Digital Corps and provide guidance tomembers about the work they perform for the agencies.

T E C H N I C A L TA L E N T I N G O V E R N M E N T

The Digital Corps model is inspired by the Army’s Medical Corps, which organizes
experts with specialized healthcare skills that do not fit into the Army’s traditional talentmanagement framework. Like the Medical Corps, agency-specific Digital Corps shouldhave specialized personnel policies, guidelines for promotion, training resources, andcertifications to demonstrate proficiency in new digital areas.
Recruit.
To fill these Digital Corps and to improve its broader digital workforce, the government needs
to improve recruiting and the hiring process, accelerate security clearances, use temporary
hiring vehicles such as the Intergovernmental Personnel Act, and build mechanisms forpart-time civilian service.4 Many AI and other digital practitioners are interested in working
with the government as either full-time employees or part-time employees. Of thosedesiring full-time employment, some seek an entire career as a government civilian or in
the military. Others are less willing to make long-term commitments and instead desire to
become temporary, full-time employees, fellows, talent exchange participants, or militaryreservists. A third group is willing to work with or for the government part-time, but they
are unwilling to become full-time civilian employees and have no desire to serve as partof the military.
Gaps in the
Recruitment M odel.

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CHAPTER 6

Recommendation

Establish a civilian National Reserve Digital Corps. The government should tap into thepool of technologists willing to contribute part of their time to public service by creatinga mechanism to hire them. While part-time employees are not a substitute for full-time
employees, they can help improve AI education, perform data triage and acquisition, help
guide projects and frame digital solutions, build bridges between the public and private
sectors, and take on other important tasks.
To eliminate this recruitment gap, the government should establish a civilian National
Reserve Digital Corps (NRDC) modeled after the military reserve’s commitments andincentive structure. Members of the NRDC would become civilian special government
employees in one of the agency Digital Corps and work at least 38 days each year asadvisors, instructors, or developers across the government.

Recommendation

Streamline the hiring process and expand digital talent pipelines. The government hiring
system’s problems are well known: It moves too slowly, struggles to attract experts in acompetitive market, and makes it difficult for experts who are young or do not have a degree
to be hired, especially at a pay grade matching their level of expertise. These challenges
are not caused by a lack of hiring authorities or an inherently slow hiring process. The
Commission has been unable to identify a gap in hiring authorities for the digital workforce.
To clear this recruiting bottleneck, the government needs to expand science, technology,
engineering, and mathematics (STEM) and AI talent pipelines from universities togovernment service, streamline the hiring process, and create agency- and military
service–specific digital talent recruiting offices either for Digital Corps or agencies. The
recruiting offices would monitor their corps, agency, or service’s need for specific types ofdigital talent and be empowered to recruit technologists virtually, by attending conferences
and career fairs, recruiting on college campuses, hosting prize competitions, and offeringscholarships, recruiting bonuses, and referral bonuses.

National Reser ve
Digital C orps.

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125

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“

T E C H N I C A L TA L E N T I N G O V E R N M E N T

The Commission has been
unable to identify a gap in
hiring authorities for thedigital workforce.

Standing Digital Corps will oversee government-wide progress and make recommendations
to expand and improve digital talent hiring and pipelines. They should also be able toexperiment with new authorities.
Build.

The government will not be able to recruit its way out of its technology workforce deficit.
AI and digital talent are simply too scarce in the United States. In 2020, there were morethan 430,000 open computer science jobs in the United States, while only 71,000 new
computer scientists graduate from American universities each year.5 The government
should also make a new commitment to building its workforce from the ground up with amajor initiative.

“The United States needs toestablish a new service academy
to train future civil servants in thedigital skills that are needed tomodernize the government.”p126

CHAPTER 6

Recommendation

Establish a United States Digital Service Academy. The United States needs to establish
a new service academy to train future civil servants in the digital skills that are needed
to modernize the government. The United States Digital Service Academy (USDSA)
would be an accredited, degree-granting university that receives both government andprivate funding, is managed by a purpose-built independent agency within the federal
government, and meets the government’s needs for digital expertise––as determined byan interagency board, assisted by a Federal Advisory Committee composed of privatesector and academic technology leaders. The USDSA should be modeled off of the five
U.S. military service academies but produce trained and educated government civilians
Graphic 5.4: Proposed Implementation Plan for USDSA.
for all federal government departments and agencies.

Proposed
Implementation
Plan for U S D S A .

(Years 1-2)

Phase One

• Identify and secure an appropriate site for initial
USDSA build-out with room for future expansion.
• Identify gaps in the government’s current andenvisioned digital workforce by an interagency task
force under Office of Personnel Management
leadership.
• Establish the USDSA administration as a new
Executive Branch agency with an individual
appropriation that will be responsible for the phased
implementation plan and the management of theinstitution.
• Recruit tenure-track faculty.
• Recruit adjunct faculty, primarily from private-sector
technology companies.
• Grant the USDSA the authority to accept outside funds
and gifts from individuals and corporations for startup,
maintenance, and infrastructure costs.

(Years 3-5)

Phase Two

• Begin classes with an initial class of 500 students atthe beginning of year three.
• Demonstrate compliance with all requirements andstandards of the regional accrediting organization inorder to be granted Membership status.

p127• Appropriate $40 million to pay for administrative costs.
• Satisfy the necessary requirements set by the
Department of Education, as well as the state the
USDSA is in, for degree-granting approval.
• Apply for degree program-specific accreditation throughthe Computing Accreditation Commission on Collegesof Accreditation Board for Engineering and Technology.
• Apply for accreditation with a regional accreditingorganization approved by the Department of Education
and Council for Higher Education Accreditation in order
to be granted “Candidate” status.
• Construct the initial physical infrastructure.
• Appropriate additional costs for the selection andpurchase of the physical location and construction of
the infrastructure.

(Years 6-7)

Phase Three• Graduate the first class.
• Ongoing improvement through accreditationassessments.
• Assess, and as appropriate, expandclass sizes.

“

T E C H N I C A L TA L E N T I N G O V E R N M E N T“Digitally talented people shouldbe able to reasonably expect
to spend a career performing
meaningful work focused on theirfield of expertise in government.”

Employ.

Digitally talented people should be able to reasonably expect to spend a career
performing meaningful work focused on their field of expertise in government. Without
such an expectation, they are unlikely to join the government workforce, and without theirexperience matching expectations, they are unlikely to stay for long. Aligning expectations
and experience for the digital workforce requires three changes:• Opportunity for technologists to spend an entire career focused on the field they are
passionate about;• Well-informed leaders, some of whom are digitally proficient themselves; and• Access to tools, data sets, and infrastructure.
These changes are more tactical than those described above, but no less impactful.
Strategic initiatives succeed or fail at the tactical level, and many digital initiatives thatmight otherwise have strategic impact are struggling or failing tactically in part because
the government does not employ its technologists effectively.
Establish new digital career fields. New career fields challenge an organization’s definition of
its necessary competencies and, potentially, the nature of its identity. If the military services
create career fields for software developers and data scientists, this will almost inevitablychange what it means to be a soldier, sailor, airman, or marine, much as the introductionof aviation did generations ago. The government should create civilian occupational series
for software development, software engineering, knowledge management, data science,and AI. The military services should create career fields in software development, data
science, and AI, with both management and specialist tracks. Digital corps will needadditional career fields as they develop, but these steps will establish a strong foundation.

Recommendationp128

CHAPTER 6

Recommendation

Expand access to tools, data sets, and infrastructure. Highly skilled technologists working
in government are regularly denied access to software engineering tools. The digital
workforce needs access to enterprise-level software capabilities on par with those foundin the private sector. Capabilities include software engineering tools, access to software
libraries, vetted open-source support, curated data sets, and infrastructure for large-scalecollaboration.
All career fields need improved access to the latest open-source libraries and tools.6 Most
advanced AI and machine learning (ML) libraries need vast amounts of data available totrain models on. Providing AI practitioners rich data sets across the physical and biologicalsciences, economics, and behavioral studies will let them focus on their areas of expertise
rather than scraping obscure sources for data.

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Chapter 6 - Endnotes
There are pockets of excellence in several parts of the government, such as in the United States
Digital Service, Kessel Run, the Army Artificial Intelligence Task Force, the USAF-MIT AI Accelerator,
components of the Intelligence Community, and the national labs– –but there are too few, and they
have not spread widely enough across the government. Agencies’ requirements for the size and typeof AI workforce vary, but every agency NSCAI has engaged has expressed a need to expand its AI
workforce, and the recommendations here are broadly applicable.

1

2

NSCAI staff discussions with the Defense Innovation Board and Defense Digital Service (May 2019).

William A. LaPlante, Owning the Technical Baseline, Defense AT&L at 18-20 (July-Aug. 2015), https://apps.dtic.mil/dtic/tr/fulltext/u2/1016084.pdf.
3

4
For more information on the Intergovernmental Personnel Act, see Intergovernmental Personnel Act,OPM (last accessed Feb. 1, 2021), https://www.opm.gov/policy-data-oversight/hiring-information/intergovernment-personnel-act/.

Code.org (last accessed Jan. 11, 2021), https://code.org/promote. See also Oren Etzioni, What
Trump’s Executive Order on AI Is Missing: America Needs a Special Visa Program Aimed at AttractingMore AI Experts and Specialists, Wired (Feb. 13, 2019), https://www.wired.com/story/what-trumpsexecutive-order-on-ai-is-missing/.
5

For the AI career field in particular, TensorFlow is one of the world’s most popular libraries fortraining neural networks and other machine learning (ML) algorithms. PyTorch is another open-source
library that aids in transforming research prototypes to production-ready machine learning models.

6

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Chapter 7:
Establishing Justified
Confidence in AI Systemsp131

E S TA B L I S H I N G J U S T I F I E D C O N F I D E N C E I N A I S Y S T E M S

Justified Confidence toAdopt and Field AI.

Accountability
and Governance
Leadership

Robust andReliable AI
Human-AI
Interaction
and Teaming
Testing and
Evaluation,
Verification andValidation (TEVV)p132

CHAPTER 7

Artificial intelligence (AI) systems must be developed and fieldedwith justified confidence.1 If AI systems do not work as designed,or are unpredictable in ways that can have significant negative
consequences, then leaders will not adopt them, operators will not
use them, Congress will not fund them, and the American people will
not support them.

““If AI systems ... areunpredictable in ways that
can have significant negative
consequences, then leaders will
not adopt them, operators will
not use them, Congress will notfund them, and the American
people will not support them.”
Achieving acceptable AI performance often is linked to the decision to accept some levelof risk. No technology works perfectly under all conditions. Risk calculus changes withcircumstances. The variables and considerations that inform judgments to rely on AI willvary significantly across military, intelligence, homeland security, and law enforcement
missions. In a high-threat environment like combat, in some cases it may be reasonable
to employ a system offering some immediate military advantage, while recognizing thatit might fail; in other cases, however, a reasonable commander might want the highest
assurances of AI reliability before fielding when lives are at risk.

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E S TA B L I S H I N G J U S T I F I E D C O N F I D E N C E I N A I S Y S T E M S“As departments and agencies
rely more heavily on machines,
a central guiding principle
across national security scenarios
is the continued centrality ofhuman judgment.”

As departments and agencies rely more heavily on machines, a central guiding principle
across national security scenarios is the continued centrality of human judgment. Thosecharged with utilizing AI need an informed understanding of risks, opportunities, andtradeoffs. They need awareness of the possibilities and limitations in a system’s expected
performance. Ultimately, they need to formulate an educated answer to this question: In thegiven circumstance, how much confidence in the machine is enough confidence? Theseissues bear on the full lifecycle of an AI system––from acquisition or system development
and the thresholds for justified confidence to deploy a specific AI-intensive system to theperformance of the system in the field.
While there is no absolute assurance of perfection, there are policies and best practices
that support making these decisions responsibly. Agencies are broadly aware of theprincipal challenges in employing AI systems and the necessity of incorporating bestpractices in the engineering and management of AI systems.
The Commission has produced a detailed framework to guide the responsible
development and fielding of AI across the national security community (see the Appendix
on Key Considerations for Responsible Development and Fielding of AI). It contains key
considerations for policymakers and technical practitioners covering the full breadthof the AI lifecycle. The framework includes recommended practices that should be
integrated and updated as the technology advances. The Commission is heartened thatsome departments have already taken actions to integrate recommendations from ourframework.2
To assist agencies in meeting baseline criteria for Responsible AI, we highlight the main
challenges and key recommendations in our framework across five issue areas.

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1. Robust and Reliable AI.
Current AI systems, such as those used for perception and classification, have different
kinds of failure––characterized as rates of false positives and false negatives. They areoften brittle when operating at the edges of their performance competence, and it isdifficult to anticipate their competence boundaries.3 They are also vulnerable to attack,and they can exhibit unwanted bias in operation. For national security missions, these canbe serious problems. U.S. government agencies should:
Recommendation

Focus more federal R&D investments on advancing AI security and robustness. These
investments should also advance the interpretability and explainability of AI systems, sousers can better understand whether the systems are operating as intended.

Recommendation

Consult interdisciplinary groups of experts to conduct risk assessments, improve documentation
practices, and build overall system architectures to limit the consequences of system failure.4
Such architectures should securely monitor component performance and handle errors
when anomalies are detected5; contain AI components that are self-protecting (validating
input data) and self-checking (validating data passed to the rest of the system); and include
aggressive stress testing.

““The government needs
AI systems that augment
and complement humanunderstanding anddecision-making so that thecomplementary strengths ofhumans and AI can be leveraged
as an optimal team. Achieving
this remains a challenge.”p
135

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2. Human-AI Interaction and Teaming.
The government needs AI systems that augment and complement human understanding
and decision-making so that the complementary strengths of humans and AI can beleveraged as an optimal team. Achieving this remains a challenge. For instance, humans
are prone both to over-trusting and to under-trusting machines depending on context.
Challenges also exist for measuring the performance of human-AI teams, conveying
enough information while avoiding cognitive overload, enabling humans and machines
to understand the circumstances in which they should pass control between each other,and maintaining appropriate human engagement to preserve situational awareness andmeaningfully take action when needed. Agencies will also need to determine machine
performance standards and expectations as compared with humans. The government
should:Pursue a sustained, multidisciplinary initiative through national security research labs to enhance
human-AI teaming. This initiative should focus on maximizing the benefits of human-AI
interaction; better measuring human performance and capabilities when working with
AI systems, including testing through continuous contact and experimentation with end
users; and helping AI systems better understand contextual nuances of a situation.

Recommendation

Clarify policies on human roles and functions, develop designs that optimize human-machineinteraction, and provide ongoing and organization-wide AI training.

Recommendation

DoD AI Total RDT&E Investments by Research Area, FY 2015-2025
Source: Govini

D o D AI
total R DT& E
Investments.

This figure displays estimated DoD spending levels across five major research categories devised by NSCAI
commissioners, indicating that investments in human-AI interaction lags behind other research categories.
Note the spending levels presented in figure represent estimates based on an analysis of DoD RDT&E budget
documents for FY2021-FY2025. See Analysis of DoD RDT&E Investments in AI, NSCAI (on final with theCommission). Due to inherent quality issues in the source data, estimates presented contain significant, difficult
to estimate margins of error.
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CHAPTER 7

3. Testing and Evaluation, Verification and Validation (TEVV).
Having justified confidence in AI systems requires assurances that they will perform
as intended, including when interacting with humans and other systems. The TEVV of
traditional legacy systems is not sufficient at providing these assurances. As a result,agencies lack common metrics to assess trustworthiness that AI systems will perform as
intended. To minimize performance problems and unanticipated outcomes, an entirely
new type of TEVV will be needed. This is a priority task, and a challenging one. The federal
government will need to increase R&D investments to improve our understanding of how to
conduct AI and software-related TEVV. Toward this end:
Recommendation

DoD should tailor and develop TEVV policies and capabilities to meet the changes needed
for AI as AI-enabled systems grow in number, scope, and complexity in the Department. Thisshould include establishing a TEVV framework and culture that integrates continuous
testing; making TEVV tools and capabilities more readily available across DoD; updating or
creating live, virtual, and constructive test ranges for AI-enabled systems; and restructuring
the processes that underlie requirements for system design, development, and testing.6

Recommendation

National Institute of Standards and Technology (NIST) should provide and regularly refresh a
set of standards, performance metrics, and tools for qualified confidence in AI models, data,
and training environments, and predicted outcomes. NIST should lead the AI community
in establishing these resources, closely engaging with experts and users from industry,
academia, and government to ensure their efficacy.
4. Leadership.
Responsible development and fielding of AI requires end users and senior leaders to beaware of system capabilities and limitations so that they are not misused. It also requires
subject-matter experts to support training, acquisition, risk assessment, and adoption ofbest practices as they evolve. Today, only the DoD has a dedicated lead for Responsible
AI; employees in national security agencies taking on these roles typically do so on a
voluntary, part-time basis. Without full-time dedicated staff, agencies will not succeed infully adopting and implementing these recommended practices. The government should:

Recommendation

Appoint a full-time, senior-level Responsible AI lead in each department or agency criticalto national security and each branch of the armed services. Such an official should drive
Responsible AI training, provide expertise on Responsible AI policies and practices, leadinteragency coordination, and shape procurement policies.

Recommendation

Create a standing body of multidisciplinary experts in the National AI Initiative Office. The
standing body would provide advice to agencies as needed on responsible AI issues. The
group should include people with expertise at the intersection of AI and other fields suchas ethics, law, policy, economics, cognitive science, and technology, including adversarial
AI techniques.

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5. Accountability and Governance.
Congress and the public need to see that the government is equipped to catch andfix critical flaws in systems in time to prevent inadvertent disasters and hold humans
accountable, including for misuse. Agencies need the ability to monitor AI performanceas systems run (to assess if they are performing as intended) and to build systems with
the necessary instrumentation to do so.7 Departments and agencies critical to national
security and oversight entities have all expressed challenges with having visibility into theirsystems, while vendors are calling for clarity on instrumentation/auditability requirements.
Government agencies should:
Adapt and extend existing accountability policies to cover the full lifecycle of AI systems andtheir components.

Recommendation

Establish policies that allow individuals to raise concerns about irresponsible AI development
and institute comprehensive oversight and enforcement practices. These should include
auditing and reporting requirements, a review mechanism for the most sensitive or highrisk AI systems, and appeals and grievance processes for those affected by the actionsof AI systems.

Recommendationp138

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Chapter 7 - Endnotes
The term “justified confidence,” taken from a widely used international standard, uses a specificdefinition of assurance as being “grounds for justified confidence.” It notes that “stakeholders needgrounds for justifiable confidence prior to depending on a system” and that “the greater the degreeof dependence, the greater the need for strong grounds for confidence.” ISO/IEC/IEEE International
Standard – Systems and Software Engineering – Systems and Software Assurance, IEEE/ISO/IEC
15026-1 (2019), https://standards.ieee.org/standard/15026-1_Revision-2019.html.

1

The Department of Defense’s Joint Artificial Intelligence Center (JAIC) Subcommittees on
Responsible AI and Test & Evaluation have both conducted substantial mapping exercises todetermine which existing practices correspond to recommendations found in the Key Considerations.
Recommendations from the Key Considerations are also reinforced by inclusions in the Department ofHomeland Security (DHS)’s AI Strategy. See Department of Homeland Security Artificial Intelligence
Strategy, DHS (Dec. 2020), https://www.dhs.gov/publication/us-department-homeland-securityartificial-intelligence-strategy?topic=intelligence-and-analysis.

2

Like other intelligent systems, including software and humans, AI systems have competency
limitations. However, we have less science to understand the performance limitations of AI systems
including why, when, and how they fail.

3

Such interdisciplinary teams should explore the possibility of documentation/labels specifying thenarrow task/mission for which a system was designed and tested. As noted in the Appendix on Key
Considerations for Responsible Development & Fielding of AI, documentation of the AI lifecycle
should include information about the data used to train and test a model and the methods used to testa model, both based on the context in which it will be used. It also should include requirements for retesting, retraining, and tuning when a system is used in a different scenario or setting.

4

Monitoring can add a layer of robustness, but must itself also be guarded to prevent new openings
for external espionage or tampering with AI systems.

5

Upgrades to digital infrastructure, as outlined in Chapter 2 of this report, will be required to augment
physical test ranges to create digital testing environments that can leverage digital twins.

6

Cases in which new sensors and instrumentation are added can also introduce new vulnerabilities.
It is especially important to ensure that the overall architecture of such systems is secure against
external espionage and tampering.

7

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CHAPTER 8

Chapter 8:Upholding Democratic Values:
Privacy, Civil Liberties, andCivil Rights in Uses of AI for
National Securityp
141

U P H O L D I N G D E M O C R AT I C VA L U E S : P R I VA C Y, C I V I L L I B E R T I E S , A N D C I V I L R I G H T S I N U S E S O F A I F O R N AT I O N A L S E C U R I T Y

Democratic Model of AI Governance

Increase Public
Transparency
Invest In and
Adopt AI Tools
to EnhanceOversight andAuditing

Strengthen
Oversight
Mechanisms

Develop andTest Systems
for Privacy
and Fairness

Protect Legal
Redress and Due
Processp142

CHAPTER 8

The basic purpose of the American government is to protect the
security and liberty of the American people. Americans have along tradition of debating how best to achieve these twin goals
when tensions arise between them. The two decades following
9/11 saw intensive efforts to calibrate the government’s powers tostop another terrorist attack with its obligations to respect individual
rights and liberties. Artificial intelligence (AI) is ushering in the next
era of this debate because new technologies offer government
agencies more powerful ways to collect and process information,
track individuals’ behavior and movements, and act on the basisof computer-generated analyses.
In addition to supporting military and intelligence missions abroad, these tools are promisingfor national security purposes closer to home—whether to examine foreign intelligence tofind signs of danger to the United States, to screen for threats at the borders, to protect
against cyber attacks and information operations, or to identify domestic terrorism plots.
Americans have concerns that AI applications used for these security and public safety
purposes—especially those involving biometric technologies or the analysis of aggregatedpersonal data—will invade their privacy, restrict their freedoms of speech and assembly,
and reinforce bias and discrimination. At the same time, if applied effectively, AI can help
improve protections for privacy and civil liberties. Machine analysis could be more precise,and AI systems potentially could enhance oversight through real-time monitoring.
For the United States, as for other democratic countries, use of AI by officials must comportwith principles of limited government and individual liberty. These principles do not upholdthemselves. In a democratic society, any empowerment of the state must be accompanied
by wise restraints to make that power legitimate in the eyes of its citizens.
As this report argues, the promise of emerging AI technologies to enhance national
security is real and significant. The ability of U.S. intelligence, homeland security, andlaw enforcement agencies to develop and use them for national security purposes must
be preserved. To do so, however, the government must ensure that their use is effective,
legitimate, and lawful. Public trust will hinge on justified assurance about compliance withprivacy, civil liberties, and civil rights.
Democratic AI Governance and Novel Challenges for Privacy,
Civil Liberties, and Civil Rights.
With new models of techno-authoritarian governance gaining traction abroad, the UnitedStates must continue to serve as a beacon of democratic values. The democratic modelp
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must prove its resilience in the face of emerging technological changes that could
challenge it. Fundamentally, we are confident that the American system—and the rules,
norms, and institutions that uphold it—can adapt to uphold the dual imperatives of security
and liberty in the AI era.
For the Intelligence Community (IC), core features of that system include laws, rules, andprocedures to minimize the collection, retention, and dissemination of U.S. persons’ data,
as well as oversight from all three branches of government.1 Homeland security and law
enforcement agencies likewise operate within frameworks of policy, oversight, and judicial
review that guide border protection and criminal investigations. Ultimately, the actions of
all federal agencies are subject to the Constitution’s guarantees.
Within this context, the advent of modern AI—and the novel capabilities it can bringto intelligence, homeland security, and law enforcement missions—raises a number ofconcerns and difficult questions and challenges with respect to the privacy, civil liberties,
and civil rights of U.S. persons. For example:• AI-powered analytics can help officials process and make sense of huge amounts of
information, which can be aggregated to form a revealing “mosaic” picture of a person’s
activities, whereabouts, and patterns of behavior.2 Combining disparate data streams
involving geolocation, web browsing, financial transactions, and other data sources
creates the possibility of new insights for analysts or investigators. This could be highly
useful to identify threats, but it has also raised questions about the proper scope andauthorization for border or law enforcement searches.3• Much of this personal information is held by private companies. This fact of modern
digital life has raised constitutional questions about whether and when individuals
should have a “reasonable expectation of privacy” in the information they provide to
third parties like technology firms—and questions about the circumstances in whichthat information may be accessed and utilized by intelligence, homeland security, or law
enforcement agencies for a legitimate national security purpose.4• AI can help automate aspects of data collection and analysis. Such methods canaugment the ability of analysts or investigators to sift through and triage masses of
information to establish patterns or pinpoint threats. But they also raise questions about
the proper roles of machine and human analysis in these processes, including formaking predictive judgments. To the extent that an AI system’s functions are opaque,it may be difficult to trace and justify the computational process that led the system tomake a recommendation. Determining when and how to rely on algorithms is especiallypertinent to minimization and querying procedures in the IC and to building cases forlaw enforcement action.5• AI models can evolve based on changing data and interaction with other models,leading to unexpected outcomes. As a result, AI systems require more continuous
testing and evaluation than prior generations of technology.
• Unintended bias can be introduced during many stages of the machine learning (ML)process, which can lead to disparate impacts in American society, a problem that has
been documented in law enforcement contexts.6p144

CHAPTER 8

Tenets for Managing AI Challenges.
This Commission will not endeavor to draw all of the lines for what may be permissible
or wise in particular circumstances. However, important principles to follow in different
national security contexts include the following:
Foreign Intelligence Collection and Analysis: The Office of the Director of National
Intelligence (ODNI) AI Ethics Guidance to the Intelligence Community is an encouraging
step, as it places strong emphasis on utilizing AI for foreign intelligence missions inways that uphold the privacy and civil liberties of Americans.7 As these guidelines are
implemented, it will be important to pay close attention to ensuring that data minimization,
retention, and querying procedures are adequate and rigorously enforced.
Border Security: AI surveillance and analysis capabilities can make the government’s
operations more efficient and effective at the borders and ports of entry. But to sustain
public support for these uses, the Department of Homeland Security (DHS) must take careto ensure that automated screening processes lead agents only to the information theyneed and are authorized to access, and do not impermissibly single out individuals basedon characteristics such as race or religion.
Domestic Security and Public Safety: Rapid advances in AI-enabled technologies forlaw enforcement purposes, including biometric surveillance techniques such as facialrecognition, may be outpacing rules for their proper use. The government must exercise
special caution in managing risks to bedrock constitutional principles including equal
protection, due process, freedom from unreasonable searches and seizures, and freedomsof speech and assembly.8
In carrying out these missions, it will be important to maintain clear distinctions between
appropriate authorities in these different national security contexts. It is also important togain greater public confidence by enhancing transparency, improving the performanceand reliability of AI technologies, ensuring due process, and strengthening oversight. With
these tenets in mind, the government should take the following steps.

p
145

“

U P H O L D I N G D E M O C R AT I C VA L U E S : P R I VA C Y, C I V I L L I B E R T I E S , A N D C I V I L R I G H T S I N U S E S O F A I F O R N AT I O N A L S E C U R I T Y“Agencies should assess nearterm opportunities and researchgaps in applications of AI to
address privacy and civil liberties
challenges ...”

Recommendations.

Invest in and adopt AI tools to enhance oversight and auditing in support of privacy andcivil liberties. Agencies should assess near-term opportunities and research gaps inapplications of AI to address privacy and civil liberties challenges, such as ML techniques
for classification, recommendation, anomaly detection, and other applications.9 Examplesof advances in AI to improve auditing include tools that support financial audits andmodel risk management. Agencies should examine the utility of these and other current or
emerging practices.10
Improve public transparency about how the government uses AI. There is a lack oftransparency into agency policies and procedures and into the accuracy of AI systems
that may impact civil liberties.11 The “black box” nature of some ML systems only adds tothis opacity.12 More transparency could help to ease public concerns. Of course, in certain
operational contexts, especially for intelligence and law enforcement agencies, secrecy
is essential to the mission. However, existing transparency mechanisms could be utilized
more effectively, and, in some cases, revised. New agency reporting requirements would
also be beneficial.

Recommendation

Recommendation• For AI systems that impact U.S. persons, Congress should require AI Risk Assessment
Reports and AI Impact Assessments from the Intelligence Community, the Departmentof Homeland Security, and the Federal Bureau of Investigation. These should assess
the privacy, civil liberties and civil rights implications for each qualifying AI system orsignificant system refresh.13• DHS and the FBI should also improve practices for issuing system of records notices
and privacy impact assessments to provide a more holistic view of the role of AI systems
before they are fielded.

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Recommendation

Develop and test systems with the goal of advancing privacy preservation and fairness. ML
systems in particular require ongoing assessments of privacy and fairness assurances,including the specific definition of fairness being assumed. Although an ML system may
meet requirements at a static point in time, ongoing compliance is not a given oncethe system is operational. This is in large part due to changing data, the introduction of
unintended bias, and potential re-identification of anonymized data.14 This is a complex
technical area, and continued work in the technical, legal, and policy domains is required
to find greater consensus on technical approaches to preserving privacy, civil liberties,
and civil rights.15 Meanwhile, agencies should take several steps:• Assess and mitigate risks in the design, development, and testing of AI systems.
In addition to conducting risk assessments for the privacy, civil liberties and civil rights
of U.S. persons, IC elements, DHS, and the FBI should take measures to mitigate thoserisks, and document remaining risks that are accepted. In doing so, they should adopt
practices from the Key Considerations, including using privacy protections such as
robust anonymization, and when possible, privacy-preserving technology; taking steps
to mitigate bias in development and testing; and assessing model performance on an
ongoing basis.16• Identify an office, committee, or team in each agency that will conduct a predeployment review of AI technologies that will impact privacy, civil liberties,
and civil rights. This should include review in advance of their deployment and forcompliance over the lifespan of the system. An office in each IC element, DHS, andthe FBI should be equipped to assess data, model, and system documentation, and toassess the testing results of systems with respect to their intended use.
• Establish third-party testing centers for national security-related AI systems that
could impact U.S. persons. Such independent, third-party testing could be done bya national laboratory, a University Affiliated Research Center, or a Federally Funded
Research and Development Center. Such testing should be mandatory for high-stakes
systems but otherwise voluntary.17 It would provide agencies with additional expertise tohelp overcome in-house limitations.

Recommendation

Strengthen the ability of those impacted by government actions involving AI to seek redress andhave due process. AI systems will make errors.18 Agencies have to accept non-zero false
positive and false negative rates in order to deploy any AI system. It is important to ensure
opportunities for redress, consistent with the constitutional principle of due process—for example, when a system error leads to a benefit being denied (e.g., visa approval);
restrictions on movement (e.g., being placed on a no-fly list); or an arrest.19 There are also
due process concerns in cases in which AI contributes to building a case to press criminal
charges.20 We recommend two steps to start addressing these issues:• Review DHS and FBI policies and practices that may impact due process and theability to seek redress. DHS and the FBI should review agency policies and practices toensure that parties aggrieved by government action involving AI technology, including
through system actions or misuse, can seek redress and clearly know how to do so. This
review should include whether adequate notice of AI use in decision-making is provided
to impacted parties, as well as the degree to which AI systems can be audited to trace
the process by which a system arrived at a recommendation, if it is contested.

p147

U P H O L D I N G D E M O C R AT I C VA L U E S : P R I VA C Y, C I V I L L I B E R T I E S , A N D C I V I L R I G H T S I N U S E S O F A I F O R N AT I O N A L S E C U R I T Y• Issue Attorney General guidance on AI and due process. The guidance should
describe how relevant agencies should safeguard the due process rights of U.S. persons
when AI use may lead to a deprivation of life or liberty.

“

Strengthen oversight mechanisms to address current and evolving concerns. The advancementof AI requires a forward-looking approach to oversight that anticipates the continued
evolution and adoption of new technologies and better positions the government to
responsibly manage their employment well into the future.

Recommendation“The advancement of AI requires
a forward-looking approachto oversight that anticipates
the continued evolution andadoption of new technologies,
and better positions thegovernment to responsibly
manage their employment wellinto the future.”

The government should:• Establish a task force to assess the privacy and civil liberties implications of AI andemerging technologies. The goal of the task force would be to identify gaps and makerecommendations to ensure that uses of AI and associated data in U.S. government
operations comport with U.S. law and values, and to study organizational reforms thatwould support this goal. Specifically, it should assess existing policy and legal gaps for
current AI applications and emerging technologies, and make recommendations for:o legislative and regulatory reforms on the development and use of AI andemerging technologies and associated data21; ando institutional changes to ensure sustained assessment and recurring guidanceon privacy and civil liberties implications of AI applications and emerging
technologies.

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CHAPTER 8• Strengthen the ability of the Privacy and Civil Liberties Oversight Board (PCLOB)
to provide meaningful oversight and advice on AI use for national security. Since
its creation in 2007, following a recommendation of the 9/11 Commission, PCLOB has
had an especially important role in overseeing, and advising the government on, U.S.
counterterrorism missions. In recent years, it has started turning attention to the use of
new technologies in foreign intelligence collection and analysis.22 The board should
be given visibility into AI systems before they are fielded, including at a more granular
technical level, and should be resourced and staffed to fulfill the more technicallysophisticated mission that the AI era now requires.23• Empower DHS Offices of Privacy and Civil Rights and Civil Liberties (CRCL). The
Chief CRCL Officer, in coordination with the Privacy Officer, must play an integral role in
the legal and approval processes for the procurement and use of AI-enabled systems,
including for associated data used in DHS ML systems.
• Require stronger coordination and alignment among federal oversight andaudit organizations. Compliance by agencies with AI documentation and testing
requirements should be supported by rigorous, technically informed oversight. To
achieve this and overcome current audit and oversight impediments, a standing bodyshould align and coordinate to enhance AI oversight and audit with respect to privacy,
civil liberties, and civil rights.24

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CHAPTER 8

Chapter 8 - Endnotes
For a compilation of Attorney General guidelines from the IC components, see Status of Attorney
General Approved U.S. Person Procedure under E.O. 12333, ODNI (July 14, 2016), https://www.
dni.gov/files/documents/ Table_of_EO12333_ AG_Guidelines%20for%20PCLOB_%20Updated%20
July_ 2016.pdf. Elements of the IC oversight system include counsels and privacy officials within
intelligence agencies, the Department of Justice, independent bodies such as the Privacy and Civil
Liberties Oversight Board, Federal courts including the Foreign Intelligence Surveillance Court, andthe House and Senate intelligence committees.

1

On the mosaic concept, see, e.g., Steven M. Bellovin, et al., When Enough Is Enough: Location
Tracking, Mosaic Theory, and Machine Learning, NYU Journal of Law & Liberty, Vol. 8 (Sept. 3, 2013),
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2320019.

2

For an informative discussion of evolving debates over Fourth Amendment regulation of government
searches in the context of AI, see James E. Baker, The Centaur’s Dilemma: National Security Law for
the Coming AI Revolution, Ch. 6 (Brookings, 2020).

3

Congress and the Judiciary will need to assess the adequacy of current legal constraints overthe federal government’s obtainment and use of third-party data, including data acquired fromdata brokers. Either through evolving case law or legislation, agencies would benefit from clarity
surrounding the Fourth Amendment’s application with respect to third-party data. On third-party
doctrine, see Richard M. Thompson II, The Fourth Amendment Third-Party Doctrine, Congressional
Research Service (June 5, 2014), https://fas.org/sgp/crs/misc/R43586.pdf.

4

For a discussion and different views on the implications of human and machine analysis in theintelligence context, see Robert Litt, The Fourth Amendment in the Information Age, Yale Law Journal(April 27, 2016), https://www.yalelawjournal.org/forum/fourth-amendment-information-age; Cindy
Cohn, Protecting the Fourth Amendment in the Information Age: A Response to Robert Litt, Yale Law
Journal (July 27, 2016), https://www.yalelawjournal.org/forum/protecting-the-fourth-amendment-in-theinformation-age.
5

Concerns about algorithmic error rates and disparate performance across age, skin tones,
and genders are especially pronounced for facial recognition. See Patrick Grother, et al., Face
Recognition Vendor Test, Part 3: Demographic Effects, NIST (Dec. 2019), https://nvlpubs.nist.gov/
nistpubs/ir/2019/NIST.IR.8280.pdf. The Gender Shades Project found that various facial recognition
systems were very accurate for white men, but they were significantly less accurate for women andpeople of color (and worst for women of color). See Gender Shades (last accessed Jan. 11, 2021),
http://gendershades.org/.

6

7
See Principles of Artificial Intelligence Ethics for the Intelligence Community, ODNI (last accessed
Jan. 11, 2021), https://www.dni.gov/index.php/features/2763-principles-of-artificial-intelligenceethics-for-the-intelligence-community.

Some observers have found a “chilling effect” that impacts the degree to which individuals exercise
freedoms of expression, association, and assembly. See, e.g., Rachel Levinson-Waldman, Hiding in
Plain Sight: A Fourth Amendment Framework for Analyzing Government Surveillance in Public, Emory
Law Journal Vol. 66 (2017), https://scholarlycommons.law.emory.edu/elj/vol66/iss3/4/.

8

Xuning (Mike) Tang & Yihua Astle, The Impact of Deep Learning on Anomaly Detection, Law.com(Aug. 10, 2020), https://www.law.com/legaltechnews/2020/08/10/the-impact-of-deep-learning-onanomaly-detection/.

910See, e.g., Bernhard Babel, et al., Derisking Machine Learning and Artificial Intelligence, McKinsey &Company (Feb. 19, 2019), https://www.mckinsey.com/business-functions/risk/our-insights/deriskingmachine-learning-and-artificial-intelligence; Saqib Aziz & Michael Dowling, Machine Learning andAI for Risk Management, Disrupting Finance at 33-50 (Dec. 7, 2018), https://link.springer.com/
chapter/10.1007/978-3-030-02330-0_3.

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11For instance, disclosure by U.S. Customs and Border Protection (CBP) when using facial recognitionat airports has been inconsistent, and claims exist that the FBI failed to provide information about its
Next Generation Identification database and use of facial recognition as required by law. In 2020, theU.S. Government Accountability Office (GAO) found that “CPB’s privacy notices—which inform thepublic about its use of this technology—were not always current or available [at airports] where thistechnology is being used or on CBP’s website.” Facial Recognition: CBP and TSA Are Taking Steps to
Implement Programs, but CBP Should Address Privacy and System Performance Issues, GAO (Sept.
2, 2020), https://www.gao.gov/products/GAO-20-568; see also The Perpetual Line-Up: Unregulated
Police Face Recognition in America, Georgetown Law Center on Privacy & Technology (Oct. 18,
2016), https://www.perpetuallineup.org/.
12
In a 2018 report, GAO has raised concerns about lack of transparency from tech companies
that build algorithms and “limited testing on the systems for accuracy.” Artificial Intelligence:
Emerging Opportunities, Challenges, and Implications, GAO (March 2018), https://www.gao.gov/
assets/700/690910.pdf.

The Commission proposes that the task force described in this chapter, and in the accompanying
Blueprint for Action, should provide binding guidance on two issues: first, when the IC, DHS, and FBI
should prepare and publish AI Risk Assessment Reports and AI Impact Statements; and second, whatshould constitute a “qualifying AI system or significant system refresh.”

13

14
For example, pseudonymized data can be linked with other data to uncover a cell phoneowner’s identity. See Byron Tau & Michelle Hackman, Federal Agencies Use Cellphone
Location Data for Immigration Enforcement, Wall Street Journal (Feb. 7, 2020), https://www.
wsj.com/articles/federal-agencies-use-cellphone-location-data-for-immigration-enforcement11581078600?mod=breakingnews.

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations for
Responsible Development & Fielding of AI. For further discussion of recommendations to: (1) employ
technologies and operational policies that align with privacy preservation and mitigate unwanted
bias and (2) to continuously monitor and evaluate AI system performance, see sections “Aligning
Systems and Uses with American Values and the Rule of Law” and “System Performance” in Key
Considerations for Responsible Development & Fielding of Artificial Intelligence: Extended Version,
NSCAI (2021) (on file with the Commission).

15

Key Considerations for Responsible Development & Fielding of Artificial Intelligence, NSCAI (July2020), https://www.nscai.gov/previous-reports/.

16

17
To provide agencies guidance on when such a test mechanism should be leveraged, anorganization should establish guidance on thresholds by which agencies would be required to
conduct third-party testing. This should include criteria for when an AI system may pose high enoughrisk for privacy, civil liberties, and civil rights that it would trigger a testing requirement by a thirdparty auditor.

See, e.g., Kashmir Hill, Wrongfully Accused by an Algorithm, New York Times (June 24, 2020),
https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html.

18

19
An individual’s right to due process, including notice, is grounded in the Constitution and the caselaw expounding on that right. Our recommendation’s aim is to help ensure that the government does
its part to uphold these long-standing rights notwithstanding its use of AI.

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Chapter 8 - Endnotes
Due process rights require that individuals have the ability to meaningfully challenge a decision
made against them. In federal criminal trials, this includes the government explaining how an
unfavorable decision was reached, so it can be contested. In cases where AI-assisted or AIenabled decisions are made, certain AI techniques will be less conducive to due process. See
Danielle Keats Citron, Technological Due Process, Washington University Law Review (2008), https://
openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1166&context=law_lawreview; Ryan Calo &Danielle Keats Citron, The Automated Administrative State: A Crisis of Legitimacy, Emory Law Journal(March 9, 2020), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3553590. Early cases inwhich an AI system’s predictions, classifications, or recommendations have been challenged incourt illustrate that defendants encounter substantial impediments in seeking to exercise their rights.
See Litigating Algorithms: Challenging Government Use of Algorithmic Decision Systems, AI Now
Institute (Sept. 2018), https://ainowinstitute.org/litigatingalgorithms.pdf. There are also open questions
including federal rules of evidence and criminal procedure as they relate to AI. For instance,
evidentiary standards for admitting AI evidence in court have yet to be developed.
20

21
Examples include baseline AI standards and policy guidance for biometric identification
technologies, for government procurement of commercial AI products, and for federal data privacy
standards.

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22See Projects, PCLOB (last accessed Jan. 9, 2021), https://www.pclob.gov/Projects.

PCLOB works alongside multiple oversight organizations to conduct oversight. It will also
be important for PCLOB and such organizations to better align and coordinate to conduct
complementary AI oversight and auditing with respect to privacy, civil liberties, and civil rights.

23

For examples of impediments, see Taka Ariga & Stephen Sanford, A is for Accountability: Oversightin the Age of Artificial Intelligence, ECA Journal at 88-91 (Jan. 2020), https://www.eca.europa.
eu/Lists/ECADocuments/JOURNAL20_01/JOURNAL20_01.pdf; see also Press Release, Officeof the Inspector General of the Intelligence Community, The Inspector General of the Intelligence
Community Issues Statement on Artificial Intelligence (May 30, 2019), https://www.dni.gov/files/
ICIG/Documents/News/ICIG%20News/2019/May%2030%20-%20AI/Press%20Release%20-%20AI.
pdf; Michael K. Atkinson, Semiannual Report: October 2018– March 2019, Office of the Inspector
General of the Intelligence Community (2019), https://www.oversight.gov/sites/default/files/oig-sareports/20190430_ICIG-SAR_Oct18-Mar19.pdf.

24

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PART TWOp155

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C EPART II: WINNING THE TECHNOLOGY COMPETITION

155

Chapter 9: A Strategy for Competition and Cooperation

157

Chapter 10: The Talent Competition

171

Chapter 11: Accelerating AI Innovation

183

Chapter 12: Intellectual Property

199

Chapter 13: Microelectronics

211

Chapter 14: Technology Protection

223

Chapter 15: A Favorable International Technology Order

241

Chapter 16: Associated Technologies

253

p
156

CHAPTER 9

Chapter 9: A Strategy forCompetition and Cooperationp
157

A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O N

Organizing the U.S. Government to Tackle
Emerging Technology Challenges

Create theTechnology
CompetitivenessCouncilDevelop a National
Technology Strategy

Establish HighLevel U.S.-China
S&T Dialoguep158

CHAPTER 9

The impact of artificial intelligence (AI) on the world will extend far
beyond narrow national security applications. The development of
AI constitutes a new pillar of strategic competition, and it heightens
the competition in existing pillars. The nation with the most resilientand productive economic base will be best positioned to seize
the mantle of world leadership. That base increasingly depends
on the strength of the innovation economy, which in turn will
depend on AI. AI technologies will drive waves of advancementin critical infrastructure, commerce, transportation, health,education, financial markets, food production, and environmental
sustainability.
The race to research, develop, and deploy AI and associated technologies is alreadyintensifying strategic competition. The U.S. government must embrace the AI competitionand organize to win it. The American approach to innovation, which has served the country
well for decades, must be recalibrated to account for the centrality of the competition
involving AI and associated technologies to the emerging U.S.-China rivalry. To retain
its innovation leadership and position in the world, the United States needs a stronger
government-led technology strategy that integrates promotion and protection policies andlinks investments in AI to a larger constellation of related emerging technologies.1““The race to research,develop and deploy AI andassociated technologies isalready intensifying strategiccompetition. The U.S.
government must embrace the
AI competition and organize to
win it.”p
159

A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O NThis chapter articulates the nature of the AI competition and the two prerequisites forwinning it: organizing for technology competition under White House leadership andestablishing the principles for continued cooperation with competitors. The following
chapters (10–16) enumerate the core elements of an integrated strategy and prescribe
actions to ensure the United States wins the AI competition and sets the foundation to win
the broader technology competition. It is foremost an affirmative agenda for growing andrecruiting talent, promoting a diverse AI innovation ecosystem, and investing in the R&D
to harness AI and associated technologies to build a healthier, more prosperous, andsecure society. Protection of research, intellectual property (IP), and investments will benecessary to complement the effort to invigorate AI competitiveness at home and build a
coalition of like-minded partners in the world.

The United States Government must understand and define thetechnology competition, organize for it, and set the terms to engage
with China.
Organizing for theCompetition

Made in China 2025
and AI World Leader
2030:
China has alreadydeveloped a strategy
for technology
leadership, picked key
technology sectors,
started high-techprojects within key
sectors, and delegatedauthority acrossindividual government
agencies.

Understanding theC ompetition.

Establish a White House
National Technology
Competitiveness Council:
Empower a single entity
in the White House to set
strategic direction andoversee a coordinatedapproach to technology
competition.
Managing the Competition
Begin a U.S.-China
Comprehensive S &T
Dialogue: Establish a highlevel dialogue with China
to discuss challenges andmanage tensions related toemerging technologies ( e.g.
AI, quantum, biotech).
Winning the Competition
Develop a National
Technology Strategy: The
Technology CompetitivenessCouncil should develop anational strategy to guide
U.S. policy across theconstellation of emerging
technologies starting with AI.

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CHAPTER 9

The U.S.-China AI Competition Is Serious and Complex.
The leading indexes that measure progress in AI development generally place the UnitedStates ahead of China.2 However, the gap is closing quickly. China stands a reasonable chanceof overtaking the United States as the leading center of AI innovation in the coming decade.3
In recent years, technology firms in China have produced pathfinding advances in natural
language processing,4 facial recognition technology,5 and other AI-enabled domains. China’s
businesses, investors, technologists, and academics are integral to global AI development.
China’s social media and e-commerce companies compete for users around the world. Its
telecoms build global 5G infrastructure. Its venture capitalists and large technology firms
invest huge sums in new startups.6 Its leading AI companies have research labs in the UnitedStates7 and elsewhere.8 Its researchers produce a trove of respected AI papers that advancethe field.9 None of this would concern us from a national security perspective, except for thefact that China is led by a single-party authoritarian regime that threatens American interests.

““China stands a reasonablechance of overtaking theUnited States as the leading
center of AI innovation in the
coming decade.”
China has moved more quickly and with more determination than the United States, guided
by a constellation of AI plans for government ministries, universities, and companies.10 These

strategic documents reflect Beijing’s view that advances in AI will fundamentally reshape
military and economic competition in the coming decades.11 China has backed up its
strategic plans with significant state subsidies to technology firms and academic institutions
that engage in cutting-edge AI research.12 China preserves its capital by taking advantage ofbasic research done by the West so that it can focus more on applications. It pours significant
sums of money into research and talent in relevant fields,13 and it promotes “national champion”
companies to win markets abroad.14 Through its military-civil fusion programs, China hassought to integrate advances in AI from the commercial and academic worlds into military
power.15 Using espionage, technology transfer programs, and targeted investment, Beijingseeks to acquire sensitive IP and data from the United States and other countries.16p161

A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O N

The U.S.-China competition is complicated by the complex web of supply chains, research
partnerships, and business relationships that link the world’s two AI leaders. Dramatic steps
to sever these ties could be costly for Americans and reverberate across the world. The
relationships between American and Chinese academics, innovators, and markets are
deep, often mutually beneficial, and help advance the field of AI.17 Moreover, it remains in
the U.S. national interest to leverage formal diplomatic dialogue about AI and other emerging
technologies and to explore areas for cooperative AI projects that will benefit humanity.

““The United States cancompete against China
without ending collaborative
AI research and severing alltechnology commerce.”
The United States can compete against China without ending collaborative AI research andsevering all technology commerce. Broad-based technological decoupling with China could
deprive U.S. universities and companies of scarce AI and science, technology, engineering,and mathematics (STEM) talent,18 sever American companies’ efficient supply chains,19 andcut off access to markets and capital for innovative firms.20 Instead, the United States should
conceive of targeted disentanglement as just one element of its overall approach, which, if
applied judiciously to key sectors, can help build U.S. technological resilience, reduce threats
from illicit technology transfer, and protect national security–critical sectors.
The Policy Challenges.
China’s competitive approach should not define the U.S. approach to innovation, but it does
present an alternative model of AI development, frame the stakes of competition, and expose thesheer breadth of public policy choices the U.S. government must make to preserve American
advantages.
The United States will need to reexamine its immigration policies to ensure that America
wins the competition for AI talent. It will need to consider AI and broader STEM education
initiatives through the lens of global competitiveness. It will have to consider how todiversify the AI research agenda and expand access to the data and tools necessary top162

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conduct AI research in the face of costs for compute and data that are consolidating AI in
fewer locations and shifting the balance from universities to the private sector. The UnitedStates will have to consider whether the long-standing approaches to IP are best suited toan era in which IP theft is pervasive and the U.S. IP regime has not yet fully accounted for
AI and other emerging technologies. The United States will need to protect its leadershipin the design of microelectronics, which may include encouraging the domestic reshoringof critical manufacturing on which our national security depends. And the United States
will have to ensure that its tools and policies designed to prevent illicit technology transfer
are postured to address the national security challenges presented by dual-use emerging
technologies.

These AI-specific challenges,
in turn, expose even more
fundamental questions
spanning the technology,
economic, and national security
spheres:• How to compete with a rival withoutcompromising U.S. values—including
free-market principles, individual
liberty, and limited government.
• How to ensure the proper balance
between defense and economic
priorities.
• How to preserve hardware advantages
without suffocating the domestic
designers and producers that rely on
foreign competitors’ markets.
• How to capitalize on and shapeprivate-sector developments fornational security ends without stifling
private sector-led and free-market
innovation.

• How to draw on the best global
talent without enabling damaging
technology and knowledge transfer tocompetitors.
• How to foster an open collaborative
research environment while closing
licit and illicit loopholes exploited byforeign competitors.
• How to sustain long-term strategies for
R&D that are nevertheless responsive
to rapidly shifting geopolitical andtechnology developments.
• How to ensure the free flow ofinvestment/capital without allowing
strategic competitors to buy strategic
advantage.
• How to engage with our allies
and other partners to reduce theirdependence on China’s digitaltechnologies, build more resilient
supply chains, and develop
technology standards and norms thatreflect democratic values.

The Need for a Stronger Government Role in Technology Strategy.
The Commission is not calling for a state-directed economy, a five-year plan, or Chinastyle “military-civil fusion.” It is instead urging a government-led process to restore amore balanced equilibrium between government, industry, and academia that ensures ap163

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diverse research environment, competitive economy, and the sustainment of a research
agenda that supports the needs of the nation. The government has a long history ofmobilizing industry and academia and making huge investments when the United States
is challenged.21 Against the backdrop of a declared and committed competitor like China,
and given AI’s transformative potential, the United States is confronting such a moment.

“... the U.S. government
champions AI leadership in
speeches and memorandums,
but deploys few resources
relative to commercial
investment and historicfunding benchmarks ...”

Today, the U.S. government champions AI leadership in speeches and memorandums,
but it deploys few resources relative to commercial investment and historic funding
benchmarks and relies on a decentralized governance structure for achieving it.22 Thereis talk of a global talent competition, but in recent years the United States has tightened
restrictions on visas for highly skilled workers,23 and U.S. students at the kindergarten to
12th grade (K-12) level have lagged behind East Asian and European competitors in exams
designed to measure competency in STEM fields.24 Tech leaders and government officials
talk about the importance of “public-private partnership,” but there is little action in either
direction to deepen it in concrete ways. U.S. experts warn of the danger of AI being used
for techno-authoritarian ends,25 but Washington has not led any new enduring coalition
to create democratic alternatives. Current policies amount to a compilation of disparate
AI-related activities underway in the federal government. Nowhere can one find a strategy
coupled with the organization and resources to win an AI competition and preserve theUnited States’ AI leadership.
The government will have to orchestrate policies to promote innovation; protect industries
and sectors critical to national security; recruit and train talent; incentivize domestic
research, development, and production across a range of technologies deemed essential
for national security and economic prosperity; and marshal coalitions of allies andp164

CHAPTER 9

partners to support democratic norms. Some elements of a national strategy will need
to be coordinated and replicated at the state level, through state-specific strategies tosupport AI research, commerce, and education. This will require a complex sequencing
of promotion and protection actions to minimize costs and risks of punitive actions; ensure
basic and applied research agendas are mutually reinforcing; coordinate approaches withinternational partners; and align executive priorities with legislative powers. It will require
identifying technology trends and assessing the relative strengths of the United States andits competitors. It will require, above all, strong and consistent White House leadership.
The Case for White House Leadership.
The government will require a center of power that can exert gravitational pull on domestic
economic, national security, and science and technology policies. We have no suchorganization today. Several separate Executive Office of the President (EOP) entities
possess some responsibility and capacity to fulfill the basic organizational requirements:
the National Security Council (NSC),26 the Office of Science and Technology Policy (OSTP)
27
and its associated National Science and Technology Council (NSTC),28 and the National
Economic Council (NEC).29 The Domestic Policy Council (DPC) also has critical related
responsibilities and a similar mandate with leadership in the realm of immigration policy,
education policy, and regulatory policy.30 An additional entity—the Office of Management
and Budget (OMB)—oversees related budgets and government reform efforts.
In the absence of an overarching structure, it is left to the President and Vice President
to identify, adjudicate, and reconcile the positions that emerge from parallel interagency
processes, while leaving endless room for gadflies to run the gaps and influence thePresident. The President needs a tool to help decide and drive a new technology strategy
down through the necessary but not sufficient existing councils and into the rest of thegovernment. The White House should:
Technology
C ompetitive
C ouncil.

p
165

“

A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O N“The government will requirea center of power that canexert gravitational pull on
domestic economic, national
security, and science andtechnology policies. We haveno such organization today.”Create a Technology Competitiveness Council. The United States must strengthen executive
leadership in technology policy in the White House by empowering a single entity to
implement a comprehensive technology strategy. The Commission proposes creating a new
Technology Competitiveness Council (TCC), which would include the same amalgamationof EOP leaders and Cabinet secretaries as other White House forums for convening theinteragency, and be chaired by the Vice President with a newly appointed Assistant to
the President for Technology Competitiveness serving as the day-to-day leader. The TCC
would ensure that the gaps between NEC, OSTP, and NSC responsibilities are filled andlinked to OMB. It would not replace the NSC, NEC, or OSTP-led NSTC structures, but would
provide a forum for reconciling competing security, economic, and scientific priorities andelevate technology policy and concerns from a technical to a strategic level. To coordinate
the council’s work, it is necessary to create a new principal, the Assistant to the President
for Technology Competitiveness, responsible for ensuring policies pertaining to emerging
technologies receive sufficient Presidential-level attention.

Recommendation

Develop a National Technology Strategy. The TCC should create a National Technology
Strategy, building on the elements we present here, which can guide U.S. policy across all
key emerging technologies starting with AI. The goal of the National Technology Strategy
should be to ensure long-term, overall U.S. leadership in technology, particularly emerging
technologies critical to national security and competitiveness. The strategy should weighthe difficult tradeoffs between competing policy interests and priorities, identify criticaltechnologies where competitors have sought to match or overtake U.S. leadership, andfacilitate an integrated policy approach to emerging technologies. As a starting point,
the strategy should build on the following pillars: 1) winning the AI talent competition; 2)promoting American AI innovation; 3) protecting U.S. AI advantages; and 4) leading afavorable international AI order.

Recommendationp
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Recommendation

Establish a high-level U.S.-China Comprehensive Science & Technology dialogue. The UnitedStates should establish a regular, high-level diplomatic dialogue with China that benefits theAmerican people, remains faithful to our allies, and presses China to abide by international
norms. The dialogue should focus on challenges presented by emerging technologies—toinclude AI, biotechnology, and other technologies as agreed by both sides. The dialogue
should have two overarching objectives:• Identify targeted areas of cooperation on emerging technologies to solve global
challenges such as climate change and natural disaster relief; and• Provide a forum to air a discrete set of concerns around specific uses of emerging
technologies while building relationships and establishing processes between the twonations.

p
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Chapter 9 - Endnotes
While the U.S. government has released a number of documents emphasizing the importanceof AI research and development—see, for example, President Trump’s executive order on AI—the
U.S. lacks a comprehensive, whole-of-government plan to guide policymakers, researchers, andbusinesses toward a more secure U.S. future. Artificial Intelligence for the American People, The
White House (last accessed Jan. 28, 2021), https://trumpwhitehouse.archives.gov/ai/.

12See, e.g., Alexandra Mousavizadeh, et al., The Global AI Index, Tortoise Media (Dec. 3, 2019),
https://www.tortoisemedia.com/2019/12/03/global-ai-index/; Jean François Gagné, et al., Global AI
Talent Report 2020 (last accessed Dec. 29, 2020), https://jfgagne.ai/global-ai-talent-report-2020/;
The Global AI Talent Tracker, MacroPolo (last accessed Dec. 29, 2020), https://macropolo.org/
digital-projects/the-global-ai-talent-tracker/; Jeffrey Ding, et al., MERICS Web Seminar: China as an
AI Superpower? Quantifying China’s AI Progress Against the US and Europe, MERICS (July 1, 2020),
https://merics.org/en/video/merics-web-seminar-china-ai-superpower-quantifying-chinas-ai-progressagainst-us-and-europe.

Audrey Cher, ‘Superpower Marathon’: U.S. May Lead China in Tech Right Now— But Beijing Has theStrength to Catch Up, CNBC (May 17, 2020), https://www.cnbc.com/2020/05/18/us-china-tech-racebeijing-has-strength-to-catch-up-with-us-lead.html; Graham Allison & Eric Schmidt, Is China Beating
the U.S. to AI Supremacy?, Belfer Center for Science and International Affairs (Aug. 2020), https://www.belfercenter.org/publication/china-beating-us-ai-supremacy; Will Knight, China May Overtake
the US with the Best AI Research in Just Two Years, MIT Technology Review (March 13, 2019),
https://www.technologyreview.com/2019/03/13/136642/china-may-overtake-the-us-with-the-best-airesearch-in-just-two-years/.

3

4
Karen Hao, Three Charts Show How China’s AI Industry Is Propped Up by Three Companies, MIT
Technology Review (Jan. 22, 2019), https://www.technologyreview.com/2019/01/22/137760/the-futureof-chinas-ai-industry-is-in-the-hands-of-just-three-companies/.

James Kynge & Nian Liu, From AI to Facial Recognition: How China Is Setting the Rules in New Tech,
Financial Times (Oct. 7, 2020), https://www.ft.com/content/188d86df-6e82-47eb-a134-2e1e45c777b6.

5

Yusho Chao, Chinese Venture Capitalists Take a Shine to Startups Again, Nikkei (Sept. 13, 2020),
https://asia.nikkei.com/Business/Finance/Chinese-venture-capitalists-take-a-shine-to-startups-again.
See also, Visualizing Chinese Tech Giants Billion-dollar Acquisitions, CB Insights (May 28, 2020),
https://www.cbinsights.com/research/bat-billion-dollar-acquisitions-infographic/.

6

See, e.g., A Chinese Tech Giant Is Setting Up an A.I. Research Lab on Amazon’s Home Turf, CNBC
(May 2, 2017), https://www.cnbc.com/2017/05/02/tencent-ai-research-lab-seattle.html.

7See, e.g., Saheli Roy Choudhury, Alibaba Sets Up Joint A.I. Research Center Outside China to Focus
on AI, CNBC (Feb. 28, 2018), https://www.cnbc.com/2018/02/28/alibaba-sets-up-joint-a-i-researchlab-in-singapore.html.

8

In 2019, China had the largest number of submitted and accepted papers to the Association for
the Advancement of AI (AAAI), one of the longest-running AI conferences. See Artificial IntelligenceIndex: 2019 Annual Report, Stanford Institute for Human-Centered AI at 41 (2019), https://hai.stanford.
edu/sites/default/files/ai_index_ 2019_report.pdf. The Allen Institute for AI also predicts that China ispoised to overtake the U.S. in the share of top-cited, breakthrough papers in AI by 2025. See Field
Cady & Oren Etzioni, China May Overtake US in AI Research, Allen Institute for Artificial Intelligence(March 13, 2019), https://medium.com/ai2-blog/china-to-overtake-us-in-ai-research-8b6b1fe30595.

9

10
For a selection of such strategic documents, see AI Policy— China, Future of Life Institute (lastaccessed Dec. 30, 2020), https://futureoflife.org/ai-policy-china/; Graham Webster, et al., Full
Translation: China’s ‘New Generation Artificial Intelligence Development Plan,’ New America (Aug. 1,2017), https://www.newamerica.org/cybersecurity-initiative/digichina/blog/full-translation-chinas-newgeneration-artificial-intelligence-development-plan-2017/ (translating China’s State Council Notice on
the Issuance of the New Generation Artificial Intelligence Development Plan, dated July 20, 2017).
11
Gregory C. Allen, Understanding China’s AI Strategy, Center for a New American Security (Feb. 6,2019), https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy.

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Chapter 9 - Endnotes
Ashwin Acharya & Zachary Arnold, Chinese Public AI R&D Spending: Provisional Findings, Center
for Security and Emerging Technology (Dec. 2019), https://cset.georgetown.edu/wp-content/uploads/
Chinese-Public-AI-RD-Spending-Provisional-Findings-1.pdf; see also Emily Weinstein, Mapping
China’s Sprawling Efforts to Recruit Scientists, Defense One (Nov. 30, 2020), https://www.defenseone.
com/ideas/2020/11/mapping-chinas-sprawling-efforts-recruit-scientists/170373/; David Cyranoski,
China Joins the Battle for AI Talent, Nature (Jan. 17, 2018), https://www.nature.com/articles/d41586018-00604-6.

12

13

Id.

14
U.S.-China Economic and Security Review Commission, Hearing on Technology, Trade, andMilitary-Civil Fusion: China’s Pursuit of Artificial Intelligence, New Materials, and New Energy at 46,
115-116 (June 7, 2019), https://www.uscc.gov/sites/default/files/2019-10/June%207,%202019%20
Hearing%20Transcript.pdf.

U.S.-China Economic and Security Review Commission, Hearing on Technology, Trade, andMilitary-Civil Fusion: China’s Pursuit of Artificial Intelligence, New Materials, and New Energy (June7, 2019), https://www.uscc.gov/sites/default/files/2019-10/June%207,%202019%20Hearing%20
Transcript.pdf.

15

16

Id.

17
As Eric Schmidt noted in Building a New Technological Relationship and Rivalry. See Hal Brands &Francis J. Gavin, COVID-19 and World Order: The Future of Conflict, Competition, and Cooperation,
Johns Hopkins University Press at 406-418 (Aug. 31, 2020), https://muse.jhu.edu/chapter/2696578.
18
Ishan Banerjee & Matt Sheehan, America’s Got AI Talent: US’ Big Lead in AI Research Is Built on
Importing Researchers, MacroPolo (June 9, 2020), https://macropolo.org/americas-got-ai-talent-usbig-lead-in-ai-research-is-built-on-importing-researchers/?rp=m.

U.S. Dependence on China’s Rare Earth: Trade War Vulnerability, Reuters (June 27, 2019), https://www.reuters.com/article/us-usa-trade-china-rareearth-explainer/u-s-dependence-on-chinas-rareearth-trade-war-vulnerability-idUSKCN1TS3AQ.

19

Member Survey, US-China Business Council (Aug. 2019), https://www.uschina.org/sites/default/
files/member_survey_ 2019_-_en_0.pdf.
20

21
For instance, adjusted for inflation, the Manhattan Project cost an estimated $27 billion and theApollo program totaled roughly $121 billion. Deborah Stine, The Manhattan Project, the Apollo
Program, and Federal Energy Technology R&D Programs: A Comparative Analysis, Congressional
Research Service (June 30, 2009), https://fas.org/sgp/crs/misc/RL34645.pdf (conversion into 2020
dollars was calculated using the U.S. Bureau of Labor Statistics’ CPI Inflation Calculator, available athttps://www.bls.gov/data/inflation_calculator.htm).

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22
In 2018, U.S. federal R&D funding amounted to 0.7% of GDP, down from its peak at above 2% in
the 1970s. See James Manyika & William H. McRaven, Innovation and National Security: Keeping
Our Edge, Council on Foreign Relations (Sept. 2019), https://www.cfr.org/report/keeping-our-edge/
recommendations/.

Zolan Kanno-Youngs & Miriam Jordan, Trump Moves to Tighten Visa Access for High-Skilled Foreign
Workers, New York Times (Oct. 6, 2020), https://www.nytimes.com/2020/10/06/us/politics/h1b-visasforeign-workers-trump.html.

23

Moriah Balingit & Andrew Van Dam, U.S. Students Continue to Lag Behind Peers in East Asia
and Europe in Reading, Math and Science, Exams Show, Washington Post (Dec. 3, 2019), https://www.washingtonpost.com/local/education/us-students-continue-to-lag-behind-peers-in-east-asiaand-europe-in-reading-math-and-science-exams-show/2019/12/02/e9e3b37c-153d-11ea-91103b34ce1d92b1_story.html.

24

Alina Polyakova & Chris Meserole, Exporting Digital Authoritarianism, Brookings (Aug. 2019), https://www.brookings.edu/research/exporting-digital-authoritarianism/.

25

26
The National Security Council has a statutory mandate to “advise the President with respect to theintegration of domestic, foreign, and military policies relating to the national security so as to enable
the Armed Forces and the other departments and agencies of the United States Government tocooperate more effectively in matters involving the national security.” 50 U.S.C. § 3021(b)(1).

See Pub. L. 94-282, National Science and Technology Policy, Organization, and Priorities Act of1976, 90 Stat. 459 (1976), https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/
ostp_organic_statute.pdf.

27

The function of the NSTC under the supervision of the Director of OSTP is: “(1) to coordinate
the science and technology policy-making process; (2) to ensure science and technology policy
decisions and programs are consistent with the President’s stated goals; (3) to help integrate thePresident’s science and technology policy agenda across the Federal Government; (4) to ensure
science and technology are considered in development and implementation of Federal policies andprograms; and (5) to further international cooperation in science and technology. The Assistant may
take such actions, including drafting a Charter, as may be necessary or appropriate to implement
such functions.” William J. Clinton, Executive Order 12881: Establishment of the National Science andTechnology Council (Nov. 23, 1993), https://www.govinfo.gov/content/pkg/ WCPD-1993-11-29/pdf/
WCPD-1993-11-29-Pg2450.pdf.

28

William J. Clinton, Executive Order 12835: Establishment of the National Economic Council (Jan. 25,
1993), https://www.govinfo.gov/content/pkg/ WCPD-1993-02-01/pdf/ WCPD-1993-02-01-Pg95.pdf.

2930
William J. Clinton, Executive Order 12859: Establishment of the Domestic Policy Council (Aug. 16,
1993), https://www.archives.gov/files/federal-register/executive-orders/pdf/12859.pdf.

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CHAPTER 10

Chapter 10:
The Talent Competitionp171

T H E TA L E N T C O M P E T I T I O NWinning the AI Talent Competition

NDEA II Witha Focus on
Digital Skills

Attract andRetain theWorld’s Brightestp172

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The United States is in a global competition for scarce AI talent.1
The Commission is very concerned with current talent trends. The
number of domestic-born students participating in AI doctorate
programs has not increased since 1990, and competition forinternational students has accelerated, endangering the UnitedStates’ ability to retain international students.2 For the first timein our lifetime, the United States risks losing the competition for
talent on the scientific frontiers. Cultivating more potential talent athome and recruiting and retaining more existing talent from foreigncountries are the only two options to sustain the U.S. lead.

““For the first time in our lifetime,
the United States risks losing
the competition for talent on thescientific frontiers.”
Competitors and allies recognize the importance of implementing AI talent strategies.
Between 2000 and 2014, China’s university system increased its number of science,technology, engineering, and mathematics (STEM) graduates by 360%, producing 1.7
million in 2014 alone.3 The number of STEM graduates in the United States’ university
system rose by 54% during the same time period, and many were international students.4
China’s researchers now represent roughly 29% of top-tier deep learning talent in theworld.5 China and other states have also taken steps to attract international talent withflexible immigration policies and incentives for tech talent.6
The United States needs to invest in all AI talent pipelines in order to remain at the forefrontof AI now and into the future. A passive strategy will not work in the face of the AI talent
competition.

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T H E TA L E N T C O M P E T I T I O N

To achieve dominance in AI, the U.S. needs to train four archetypes to propel AI in
America: researchers, implementers, end users, and informed consumers.

Researchers

AI research engineers will focus on R&D of technologies that enable andadvance semi- and fully-autonomous systems. They serve as algorithm experts
with up-to-date knowledge of modern AI research and may be involved in the
inception of ideas and drive the development cycles from research to testing of
prototypes for a major project or component of a major project.

Implementers

They will be responsible for data cleaning, feature extraction and selection,
and analysis; model training and tuning; partnerships with domain knowledge
experts and end users; and the discovery of local opportunities for exploitation.
Developers require less training and education than AI experts, and will havetraining, education, and/or experience that is roughly equivalent to an associate
or bachelor’s degree; and that includes relevant ethics and bias mitigation indata processing and model training.

End Users

They will have their daily business augmented/enabled by AI. Use of AI willstrongly resemble the use of currently available software in that it will require
some system-specific training, but, with the exception of some positions thatmanage data, little to no AI specific expertise.

Informed Consumers

This group of people needs the ability to make better consumer choices whenpurchasing technology and understand the importance of their actions in the market.

The Promise and Limits of Expanding STEM.
Investments in STEM education are a necessary part of increasing American national
power and improving national security. The United States ranks well overall on international
measures of talent because of our ability to attract international talent, in spite of ouruneven kindergarten to 12th grade (K-12) education system.7 It is critical that the UnitedStates invest significantly in STEM education as an engine to drive the growth of AI talent in
America. Investments in STEM education alone, however, will not be enough for the UnitedStates to win the international competition for AI and STEM talent. China is producing
large numbers of computer scientists, engineers, and other STEM graduates.8 For theforeseeable future, the United States’ STEM education system does not have the capacity
nor the quality to produce sufficient STEM or AI talent to supply the United States’ markets
or national security enterprise.9 To compete, the United States must reform its education
system to produce both a higher quality and quantity of graduates.
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Recommendation

Pass a National Defense Education Act II. Motivated by fear that America had fallen behindin education and innovation after the Soviets launched Sputnik in 1957, Congress passed
the National Defense Education Act (NDEA) the following year. The NDEA promoted theimportance of science, mathematics, and foreign languages for students, authorizing morethan $1 billion toward decreasing student loans, funding for education at all levels, andfunding for graduate fellowships. Many students were able to attend college because ofthis legislation. In 1960, 3.6 million students attended college; by 1970 it was 7.5 million.10
This act helped America win the Space Race, helped power the microelectronics industry,
accelerated the U.S. capacity to innovate, and, ultimately, played an important role in
America’s victory in the Cold War.
The Commission believes the time is right for a second NDEA, one that mirrors the firstlegislation, but with important distinctions. NDEA II would focus on funding students
acquiring digital skills, like mathematics, computer science, information science,
data science, and statistics. NDEA II should include K-12 education and reskilling
programs that address deficiencies across the spectrum of the American educational
system, purposefully targeting under-resourced school districts. The Commission also
recommends investments in university-level STEM programs with 25,000 undergraduate,
5,000 graduate, and 500 PhD-level scholarships. Undergraduate scholarships should
include credit hours at community colleges to ensure more Americans have access toaffordable STEM education. Ultimately, the goal of NDEA II is to widen the digital talentpool by incentivizing programs for underrepresented Americans.

““The Commission believes thetime is right for a second NDEA ...”

Recommendationp
175

Strengthen AI talent through immigration. Immigration reform is a national security imperative.
Nations that can successfully attract and retain highly skilled individuals gain strategicand economic advantages over competitors. Human capital advantages are particularly
significant in the field of AI, where demand for talent far exceeds supply.11 Highly skilled
immigrants accelerate American innovation, improve entrepreneurship, and create jobs.12
The United States benefits far more from the immigration of highly skilled foreign workers
than other countries. In 2013, the United States had 15 times as many immigrant inventors
as there were American inventors living abroad.13 By contrast, Canada, Germany, andthe U.K. all maintain a net negative inventor immigration rate.14 Compared with other U.S.
advantages in the AI competition—such as financial resources or hardware capacity—thisimmigration advantage is harder for other countries to replicate.

““T H E TA L E N T C O M P E T I T I O N“Nations that can successfully
attract and retain highly
skilled individuals gain strategicand economic advantages
over competitors.”

Unfortunately, international students in the United States are increasingly choosing tostudy in other countries or return home.15 One reason is the growing backlog of green card
petitions.16 Indian immigrants face a particularly long wait. Many will spend decades on
constrictive work visas waiting to receive their green cards, hindering both the technology
sector’s ability to recruit talent and Indian immigrants’ quality of life. At the same time, othercountries are increasing their efforts to attract and retain AI talent, including immigrants in
Silicon Valley.17“Restrictions harm U.S. innovationand economic growth and only
help our competitors by enabling
their human capital to grow. They
also incentivize U.S. technology
companies to move to where
talent resides, whether rightacross our borders or overseas.”p176

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While immigration benefits the United States, policymakers must also bear in mind thethreat of unwanted technology transfer. However, restricting immigration is far too blunt atool to solve this problem.18 Restrictions harm U.S. innovation and economic growth andonly help our competitors by enabling their human capital to grow. They also incentivize
U.S. technology companies to move to where talent resides, whether right across ourborders or overseas.19 Technology transfer will only get worse if significant componentsof the U.S. technology sector move their research and development to China or othercountries that are more vulnerable than the United States to technology transfer efforts.20
A more effective strategic approach would pair actions to improve the United States’ ability
to attract top global talent with targeted efforts to combat technology transfer vectors.
NSCAI addresses technology transfer in detail in Chapter 14 of this report. Changes toimmigration policies should be paired with those recommendations.
Immigration policy can also slow China’s progress. China’s government takes the threat ofbrain drain seriously, noting that the United States’ ability to attract and retain China’s talentis an obstacle to the Chinese Communist Party’s (CCP) ambitions.21 Increasing China’s
brain drain will create a dilemma for the CCP—which will be forced to choose between
losing even more human capital, further slowing their economic growth and threatening
their advancement in AI, or denying Chinese nationals opportunities to study and work in
the United States. At the same time, the United States should be cautious about potential
adverse effects on talent pools in partner nations.

““Increasing China’s brain drain will
create a dilemma for the CCP ...”

Recommendation

Broaden the scope of “extraordinary” talent to make the O-1 visa more accessible andemphasize AI talent. The O-1 temporary worker visa is for people with extraordinary abilityor achievement. Currently adjudicators determine an applicant’s eligibility through a
subjective assessment. For the sciences and technology, this aligns largely with academic
criteria such as publications in major outlets and is not well suited for people who excel inindustry.

Recommendation

Implement and advertise the international entrepreneur rule. The International Entrepreneur
Rule (IER) allows U.S. Citizenship and Immigration Services (USCIS) to grant a periodof authorized stay to international entrepreneurs who demonstrate that “their stay in the
United States would provide a significant public benefit through their business venture.”22
An executive action could announce the administration’s intention to use the IER to boost
immigrant entrepreneurship, job creation for Americans, and economic growth. USCISp177

T H E TA L E N T C O M P E T I T I O N

could also be directed to announce that it will give priority to entrepreneurs active inhigh-priority STEM fields such as AI, or in fields that use AI for other applications, suchas agriculture. Entrepreneurs’ ability to attract investors should be used as a screening
criterion for entrepreneurs.
Expand and clarify job portability for highly skilled workers. The criteria for workers with H-1B,
O-1, and other temporary work visas to obtain open market work permits for a one-year
renewable period are too limited and ambiguous. Changes should clarify when highly
skilled, nonimmigrant workers are permitted to change jobs or employers, increase job
flexibility when an employer either withdraws their petition or goes out of business, andincrease flexibility for H-1B workers seeking other H-1B employment.

Recommendation

Recapture green cards lost to bureaucratic error. Federal agencies generally issue fewer green
cards than they are allowed. As of 2009, the federal government had failed to issue morethan 326,000 green cards based on cumulative bureaucratic error.23 The Departments ofState and Homeland Security (DHS) should publish an up-to-date report on the number ofgreen cards lost due to bureaucratic error. Using available authorities, both should grant
lost green cards to applicants waiting in line. Congress should support the Departments ofState and Homeland Security by passing legislation to recapture lost green cards.24

Recommendation

Grant green cards to students graduating with STEM PhDs from accredited American universities.
Congress should amend the Immigration and Nationality Act25 to grant lawful permanent
residence to any vetted (not posing a national security risk) foreign national who graduatesfrom an accredited United States institution of higher education with a doctoral degree ina STEM-related field in a residential or mixed residential and distance program and has
a job offer in a field related to science, technology, engineering, or mathematics. They
should not be counted toward permanent residency caps.

Recommendation

Double the number of employment-based green cards. Under the current system,
employment-based green cards are unduly scarce: 140,000 per year, fewer than half ofwhich go to the principal worker.26 This leaves many highly skilled workers unable to gain
permanent residency and unable to transfer jobs or negotiate with employers as effectively
as domestic workers. This decreases the appeal of joining the American workforce. To
reduce the backlog of highly skilled workers, the United States should double the numberof employment-based green cards, with an emphasis on permanent residency for STEM
and AI-related fields.

Recommendation

Create an entrepreneur visa. International doctoral students are more likely than their native
peers to want to found a company or become an employee at a startup, but they are lesslikely to pursue those paths.27 One reason is the constraints of the H-1B visa system.28
Similarly, immigrant entrepreneurs without the capital to use the EB-5 route to permanent
residency are forced to use other visas that are designed for academics and workers
in existing companies, not entrepreneurs.29 All of these issues make the United States

Recommendationp178

CHAPTER 10

less attractive for international talent, and, perhaps as important, reduce the ability ofstartups and other small companies—the main source of new jobs for Americans—to hirehighly skilled immigrants, who have been shown to improve the odds that the businesswill succeed. Congress should create an entrepreneur visa for those who would provide
a “significant public benefit” to the United States if allowed to stay in the country for alimited trial period to grow their companies.30 This visa should serve as an alternative
to employee-sponsored, investor, or student visas and should instead target promisingpotential founders.
Recommendationp
179

Create an emerging and disruptive technology visa. The National Science Foundation (NSF)
should identify critical emerging technologies every three years. DHS would then allow
students, researchers, entrepreneurs, and technologists in applicable fields to apply for
emerging and disruptive technology visas. This would provide much-needed talent R&D
and strengthen our economy.31

T H E TA L E N T C O M P E T I T I O N

Chapter 10 - Endnotes
Estimates on the gap of talent necessary to fill AI slots vary greatly, but it is agreed upon that the
gap in talent currently is and will continue to be significant as nations compete for scarce resources.
See Remco Zwetsloot, et al., Strengthening the U.S. AI Workforce: A Policy and Research Agenda,
Center for Security and Emerging Technology at 2 (Sept. 2019), https://cset.georgetown.edu/wpcontent/uploads/CSET-Strengthening-the-U.S.-AI-Workforce.pdf (“The Research Institute at Tencent,a major Chinese technology company, asserts there are roughly 300,000 AI researchers andpractitioners worldwide, with market demand for millions of roles. Element AI, a leading Canadian
AI company, estimated in 2018 that there are roughly 22,000 PhD-educated researchers globally
who are able to work on AI research, with only about 25 percent of those ‘well-versed enough in thetechnology to work with teams to take it from research to application.’ AI firm Diffbot estimates that
there are over 700,000 people skilled in machine learning worldwide.”).

1

Remco Zwetsloot, et al., Keeping Top AI Talent in the United States, Center for Security andEmerging Technology at iii-vi (Dec. 2019), https://cset.georgetown.edu/wp-content/uploads/KeepingTop-AI-Talent-in-the-United-States.pdf.

2

The Rise of China in Science and Engineering, NSF National Science Board (2018), https://www.nsf.
gov/nsb/sei/one-pagers/China-2018.pdf (China also passed the United States in the global share ofpeer-reviewed S&E articles).

3

4
Science & Engineering Indicators 2018, NSF National Science Board (2018), https://www.nsf.gov/
statistics/2018/nsb20181/assets/561/higher-education-in-science-and-engineering.pdf.

For these purposes “top tier” talent was defined by accepted papers at the prestigious AI deeplearning conference Neural Information Processing Systems in 2019. This reflected approximately the
top 20% of researchers in the field. The Global AI Talent Tracker, MacroPolo (last accessed Dec. 28,
2020), https://macropolo.org/digital-projects/the-global-ai-talent-tracker/. China has placed a strongemphasis on deep learning, just one of the important components of AI.

5

For example, China’s Thousand Talents Plan is part of a state-organized blueprint to be a global
leader in science and technology by 2050. Staff Report, Threats to the U.S. Research Enterprise:
China’s Talent Recruitment Plans, U.S. Senate Permanent Subcommittee on Investigations at 14 (Nov.
2019), https://www.hsgac.senate.gov/imo/media/doc/2019-11-18%20PSI%20Staff%20Report%20-%20
China’s%20Talent%20Recruitment%20Plans.pdf.

6

The Global AI Talent Tracker, MacroPolo (last accessed Dec. 28, 2020), https://macropolo.org/
digital-projects/the-global-ai-talent-tracker/. See also Gordon Hanson & Matthew Slaughter, HighSkilled Immigration and the Rise of STEM Occupations in U.S. Employment, National Bureau of
Economic Research at 1 (Sept. 2016), https://www.nber.org/system/files/working_papers/w22623/
w22623.pdf.
7

The Rise of China in Science and Engineering, NSF National Science Board (2018), https://www.nsf.
gov/nsb/sei/one-pagers/China-2018.pdf.

8

As noted in Chapter 6 of this report, there were 433,116 open computer science jobs in the UnitedStates in 2019, while only 71,226 new computer scientists graduated from American universities in
2019. Code.org (last accessed Jan. 11, 2021), https://code.org/promote. See also Oren Etzioni, What
Trump’s Executive Order on AI Is Missing: America Needs a Special Visa Program Aimed at AttractingMore AI Experts and Specialists, Wired (Feb. 13, 2019), https://www.wired.com/story/what-trumpsexecutive-order-on-ai-is-missing/.

910
Sputnik Spurs Passage of the National Defense Education Act, U.S. Senate (last accessed Dec. 28,
2020), https://www.senate.gov/artandhistory/history/minute/Sputnik_Spurs_Passage_of_National_
Defense_Education_ Act.htm#:~:text=The%20National%20Defense%20Education%20Act%20of%20
1958%20became%20one%20of,and%20private%20colleges%20and%20universities.
11
According to one report, job listings for AI on one popular job website “increased more than fivefold between 2015 and 2017 and demand for ‘deep learning’ skills increased by a factor of more than30,” while the number of people looking for jobs in the field grew much more slowly. This mismatch isslowing the adoption of AI. Most firms report that skills gaps are one of the top obstacles preventing
them from adopting AI. Remco Zwetsloot, et al., Strengthening the U.S. AI Workforce: A Policy andResearch Agenda, Center for Security and Emerging Technology at 1 (Sept. 2019), https://cset.
georgetown.edu/wp-content/uploads/CSET-Strengthening-the-U.S.-AI-Workforce.pdf.

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CHAPTER 10

Chapter 10 - Endnotes
William S. Kerr, High-Skilled Immigration, Innovation, and Entrepreneurship: Empirical Approaches
and Evidence, National Bureau of Economic Research at 7-8 (Aug. 2013), https://www.nber.org/
papers/w19377; Gordon Hanson & Matthew Slaughter, Strengthening the U.S. AI Workforce, HighSkilled Immigration and the Rise of STEM Occupations in U.S. Employment, National Bureau of
Economic Research at 23 (Sept. 2016), https://www.nber.org/system/files/working_papers/w22623/
w22623.pdf; Remco Zwetsloot, et al., Strengthening the U.S. AI Workforce: A Policy and Research
Agenda, Center for Security and Emerging Technology at 5 (Sept. 2019), https://cset.georgetown.edu/
wp-content/uploads/CSET-Strengthening-the-U.S.-AI-Workforce.pdf.
12

Carsten Fink, What Leads Inventors to Migrate?, World Economic Forum (July 17, 2013), https://www.
weforum.org/agenda/2013/07/what-leads-inventors-to-migrate/.

13

Ernest Miguelez & Carsten Fink, Measuring the International Mobility of Inventors: A New Database,World Intellectual Property Organization at 16 (May 2013), https://www.wipo.int/edocs/pubdocs/en/
wipo_pub_econstat_wp_8.pdf.

14

According to the Center for Security and Emerging Technology, in 2016, 14% of international
students declined offers to study at U.S. universities to study at home, and 19% decided to study inanother country. In 2018, these numbers rose, with 39% staying at home and 59% studying in anothercountry. Remco Zwetsloot, et al., Keeping Top AI Talent in the United States: Findings and Policy
Options for International Graduate Student Retention, Center for Security and Emerging Technology
at 26 (Dec. 2019), https://cset.georgetown.edu/wp-content/uploads/Keeping-Top-AI-Talent-in-theUnited-States.pdf.

15

Shulamit Kahn & Megan MacGarvie, The Impact of Permanent Residency Delays for STEM PhDs:
Who Leaves and Why, Research Policy (Nov. 2020), https://www.sciencedirect.com/science/article/
abs/pii/S0048733319301982.

16

17
Tina Huang & Zachary Arnold, Immigration Policy and the Global Competition for AI Talent, Center
for Security and Emerging Technology at 8 (June 2020), https://cset.georgetown.edu/research/
immigration-policy-and-the-global-competition-for-ai-talent/.
18
Zachary Arnold, et al., Immigration Policy and the U.S. AI Sector: A Preliminary Assessment, Center
for Security and Emerging Technology at 22 (Sept. 2019), https://cset.georgetown.edu/research/
immigration-policy-and-the-u-s-ai-sector/.

Remco Zwetsloot, et al., Strengthening the U.S. AI Workforce: A Policy and Research Agenda,
Center for Security and Emerging Technology at 5 (Sept. 2019), https://cset.georgetown.edu/wpcontent/uploads/CSET-Strengthening-the-U.S.-AI-Workforce.pdf.

19

China is the world’s largest single source of AI talent. Leading U.S. technology companies such
as Google and Microsoft have established cutting-edge research centers in China, in part to accessthat talent. This increases China’s AI R&D capacity and potential for technology transfer, and,if the companies remain American, it reduces the American Intelligence Community’s (IC) legal
authorization to collect information about Chinese technology development. See The Global AI Talent
Tracker, MacroPolo (last accessed Jan 17, 2020), https://macropolo.org/digital-projects/the-globalai-talent-tracker/; Roxanne Heston & Remco Zwetsloot, Mapping U.S. Multinationals’ Global AI R&D
Activity, Center for Security and Emerging Technology at 20 (Dec. 2020), https://cset.georgetown.
edu/wp-content/uploads/CSET-Mapping-U.S.-Multinationals-Global-AI-RD-Activity-1.pdf.
20

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21Remco Zwetsloot, US-China STEM Talent “Decoupling”: Background, Policy, and Impact, Johns
Hopkins Applied Physics Laboratory at 19 (2020), https://www.jhuapl.edu/assessing-us-chinatechnology-connections/dist/407b0211ec49299608551326041488d4.pdf (“[T]he head of the [Chinese
Communist Party’s (CCP)] Central Talent Work Coordination Small Group … complained that ‘thenumber of top talents lost in China ranks first in the world.’”); see also Joy Dantong Ma, China’s
AI Talent Base Is Growing, and Then Leaving, MacroPolo (July 30, 2019), https://macropolo.org/
chinas-ai-talent-base-is-growing-and-then-leaving/?rp=m (noting that of the 2,800 Chinese NeurIPSparticipants between 2009 and 2018, about three-quarters of them were currently working outside of
China).
22
International Entrepreneur Parole, USCIS (May 25, 2018), https://www.uscis.gov/humanitarian/humanitarian-parole/international-entrepreneur-parole. There is currently no visa category well-suited
to entrepreneurship in immigration statute. The IER, which relies on parole authority, was initiatedafter legislative avenues were exhausted. Legislative fixes would be preferable, but have so far they
have proven politically infeasible.

A 2010 report to Congress indicated that some 242,000 unused family-based green cards were
ultimately applied to the employment-based backlog, while Congress recaptured some 180,000 green
cards via special legislation, leaving more than 326,000 green card numbers wasted. Citizenship andImmigration Services Ombudsman: Annual Report 2010, U.S. Department of Homeland Security (June30, 2010), https://www.dhs.gov/xlibrary/assets/cisomb_ 2010_annual_report_to_congress.pdf. The
number today is likely higher, but DHS has not published updated statistics.

23

Prior examples of Congressional action include provisions in the American Competitiveness in the
21st Century Act of 2000 and the REAL ID Act of 2005. See Pub. L. 106-313, 114 Stat. 1251, 1254
(2000) and Pub. L. No. 109-013, 119 Stat. 231, 322 (2005).

24

25

Specifically, 8 U.S.C. § 1151(b)(1).

William Kandel, The Employment-Based Immigrant Backlog, Congressional Research Service at 4-5(March 26, 2020), https://fas.org/sgp/crs/homesec/R46291.pdf.

26

Michael Roach, et al., Are Foreign STEM PhDs More Entrepreneurial? Entrepreneurial
Characteristics, Preferences and Employment Outcomes of Native and Foreign Science & Engineering
PhD Students, National Bureau of Economic Research at 1 (Sept. 2019), https://www.nber.org/system/
files/working_papers/w26225/w26225.pdf.

27

28

Id. at 12.

EB-5 visas require a minimum $900,000 investment in a business in the United States. William R.
Kerr, Global Talent and U.S. Immigration Policy: Working Paper 20-107, Harvard Business Schoolat 14 (2020), https://www.hbs.edu/faculty/Publication%20Files/20-107_0967f1ab-1d23-4d54-b5a1c884234d9b31.pdf.

29

30
83 Fed. Reg. 24415, Removal of International Entrepreneur Parole Program, U.S.
Department of Homeland Security (May 29, 2018), https://www.federalregister.gov/
documents/2018/05/29/2018-11348/removal-of-international-entrepreneur-parole-program.

Oren Etzioni, What Trump’s Executive Order on AI Is Missing: America Needs a Special Visa
Program Aimed at Attracting More AI Experts and Specialists, Wired (Feb. 13, 2019), https://www.
wired.com/story/what-trumps-executive-order-on-ai-is-missing/.

31

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CHAPTER 11

Chapter 11:
Accelerating AI Innovationp
183

A C C E L E R AT I N G A I I N N O VAT I O N

Leadership in AI Innovation

Scale and
Coordinate Federal
AI R&D Funding

Expand AccessThrough a National AI
R&D Infrastructure

Strengthen
Public-Private
Partnerships

Tackle Humanity’s
Biggest Challengesp184

CHAPTER 11

To remain the world’s leader in artificial intelligence (AI), the U.S.
government must renew its commitment to investing in America’s
national strength: innovation. This will require making substantial
new investments in AI R&D and establishing a national AI research
infrastructure that democratizes access to the resources that fuelAI. Members of Congress must come to terms with the realitythat tens of billions of dollars will be needed over the next several
years. The return on these investments will transform America’s
economy, society, and national security.
A lack of national urgency is dangerous at a time when underlying weaknesses have emergedin our AI ecosystem that impair innovation and when viewed against the backdrop of China’s
state-directed AI progress. The development of AI in the United States is concentrated
in fewer organizations in fewer geographic regions pursuing fewer research pathways.
Commercial agendas are dictating the future of AI and concentrating heavily in one discipline:
machine learning (ML).1 Despite promising moves, government funding has lagged behind
the transformative potential of the field, limiting its ability to shape research toward the public
good and support progress across a range of AI disciplines.2 As a result, the AI innovation
environment rests on a narrowing foundation.

““A lack of national urgency
is dangerous at a time when
underlying weaknesses haveemerged in our AI ecosystemthat impair innovation, and whenviewed against the backdrop
of China’s state-directed
AI progress.”p
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A C C E L E R AT I N G A I I N N O VAT I O N

These trends toward consolidation come as a result of resources. Declining per-unit costs ofcloud-based computing and availability of open-source platforms have lowered barriers toaccess for core ML. However, those same conditions have enabled pursuit of sophisticatedmodels that require extensive training data, often held in privately controlled data sets or
knowledge graphs, enormous computing power, and substantial hardware and software
engineering.3 These prerequisites now define the cutting edge of AI research and effectively
limit the number of American researchers able to contribute to the field and tackle the hardchallenges that could unlock new frontiers of AI.
Ingenuity, Not Access, Should Be the Key to AI Innovation in America.
The consolidation of the AI industry threatens U.S. technological competitiveness in five ways:• Brain Drain. Brain drain from academic institutions to the private sector threatens tohollow out the foundations of the United States’ advantage in basic AI research: its
universities.4 Federal funding that has not kept pace with the growth of the field has led
to low grant application success rates and amplified time spent on the bureaucracy of
pursuing and completing proposals.5 However, academic experts and their students
are not just lured to big tech by the promise of less bureaucracy and higher financial
incentives. Increasingly, the private sector is the best place to conduct cutting-edge
research with access to the best computing and data resources. The result is theweakening of the teaching base for the next generation of AI leaders in industry andacademia and the narrowing of the overall AI research agenda.6

Compute Required to Train Largest Deep Learning Models (2012-2017)

C ompute Required
to Train L argest
D eep Learning
M odels (2 012-2 017)

Source: OpenAI, AI and Compute (May 16, 2018), https://openai.com/blog/ai-and-compute/
A “petaflop/s-day” is a measure of compute that consists of performing 10^15 floating point operations
per second for one day.
GPT-3 and more recent models are not represented here because they are too large to fit on this scale.
p186

CHAPTER 11• Diversity. The growing divide between “haves” and “have nots” in AI will exacerbate thewell-documented lack of diversity in the field,7 limiting the field’s collective ability tobuild equitable, inclusive systems.
• Research Focus. American technology firms are accountable to their shareholders andwill logically not invest in areas of national security importance or make uncertain bets
on fundamental research that does not hold commercial or economic benefit for thecompany.8 While return-focused investments can lead to applications that contributeto the public good or benefit government work, there are gaps. ML and the underlying
algorithms were in exactly this position two decades ago—seemingly without
commercial promise—only to be sustained by federal research dollars until computing
power and an overabundance of data transformed the discipline.9 A recent study foundthat 82% of the algorithms in use today originated from federally funded non-profits
and universities, compared to just 18% that originated from private companies.10• Competition. The rising cost of developing cutting-edge ML models and highlikelihood of acquisition by leading technology companies means AI startups havenarrowing paths to growth in the United States.11 Lack of competition undermines
the industry’s ability to innovate and be globally competitive in the research anddevelopment of AI.
• Regional Divergence. The clustering of technology firms in regions like Silicon Valley
drives innovation by expediting knowledge sharing and sharpening domestic rivalry.12
However, this trend has benefited some regions and demographics more than others.13
More than 90% of U.S. innovation sector job creation occurred in just five major coastal
cities between 2005 and 2017.14 This divergence concentrates gains from technologicalprogress in just a few regions and misses out on latent innovation potential in the rest of
the country.
The federal government holds the responsibility to reverse these trends. It must step inand step up to provide strategic direction and sustained resources, as both a funder andconsumer of technology.15 It must break the mold of standard scientific research funding.
The outcomes of technology innovation, which generate greatest value when translated
into fieldable solutions, are driven by multi-sector contributions and a culture of risk
acceptance. The status quo at federal agencies and research entities is insufficient tomake these big bets and propel promising technology concepts from laboratory to field.
A passive national approach that relies too heavily on the private sector to drive innovationand determine research agendas—and that presumes commercial innovation can simply“spin-in” to become government applications—will not win this strategic competition, norwill it fully capitalize on the transformative potential of AI. The United States—throughgovernment leadership in partnership with industry and academia—must increase thediversity, competitiveness, and accessibility of its AI innovation environment. That begins
with a substantial infusion of new R&D dollars.

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“

A C C E L E R AT I N G A I I N N O VAT I O N“The United States—throughgovernment leadership in
partnership with industry andacademia—must increase thediversity, competitiveness, andaccessibility of its AI innovation
environment.”

Scale and coordinate federal AI R&D funding. A bold, integrated push for long-term
investments in AI R&D will foster nationwide AI innovation and drive breakthroughs. An
infusion of sustained resources, guided by a comprehensive strategy and distributed
through a diversity of mechanisms, will enable U.S. researchers to push the boundaries ofthe field by supporting a wide range of AI approaches and novel applications of AI to otherfields. Specifically, the United States should:

Recommendation• Establish a National Technology Foundation (NTF). A new, independent organization
would complement successful existing organizations, such as the NSF and DARPA,by providing the means to more aggressively move science into engineering. The NTF
would drive technology progress at a national level by focusing on generating value atintermediate levels of technical maturity, prioritizing use-inspired concepts, establishing
infrastructure for experimentation and testing, and supporting commercialization of
successful outcomes. This requires an organization that is structured to accept higher
levels of risk and empowered to make big bets on innovative ideas and people.
• Increase federal funding for non-defense AI R&D at compounding levels, doubling
annually to reach $32 billion per year by Fiscal Year 2026. This would bring AI
spending to a level near to federal spending on biomedical research.16 Overall, thegovernment should spend at least 1% of GDP on R&D to reinforce a base of innovation
across scientific fields.17 Additional funding should strengthen basic and appliedresearch, expand fellowship programs, support research infrastructure, and cover
several agencies, with an emphasis on:o National Technology Foundation (proposed)
o Department of Energyo National Science Foundationo National Institutes of Healtho National Institute of Standards and Technology
o National Aeronautics and Space Administrationp
188

CHAPTER 11

AI R& D
Investment Levels.

DHS - Department of Homeland Security

NIST - National Institute of Standards and

DOE - Department of Energy

Technology

DOI - Department of the Interior

NOAA - National Oceanic and Atmospheric

DOT - Department of Transportation

Administration

FDA - Food and Drug Administration

NSF - National Science Foundation

NASA - National Aeronautics and Space

Treasury - Treasury/Financial Crimes

Administration

Enforcement Network

NIH - National Institutes of Health

USDA - U.S. Department of Agriculture

NIJ - National Institute of Justice

VA - Department of Veterans Affairs

Source: The Networking & Information Technology Research & Development Program, Supplement to The
President’s FY2021 Budget, National Science & Technology Council (Aug. 14, 2020), https://www.nitrd.gov/
pubs/FY2021-NITRD-Supplement.pdf.

• Prioritize funding for key areas of AI R&D. Amplified federal funding should prioritize
AI R&D investments in areas critical to advance technology that will underpin futurenational security and economic stability, supporting areas that may not receive
significant private-sector investment. Coordinated through the newly established
National AI Initiative,18 investments should reflect a portfolio approach, focusedon advancing basic science, solving specific challenge problems, and facilitating
commercialization breakthroughs.

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A C C E L E R AT I N G A I I N N O VAT I O NTest and evaluation,verification andvalidation (TEVV)

Novel machine-learning(ML) directions
Further non-traditional
approaches to supervised
ML in an unsupervised orsemi-supervised manner
as well as the transfer oflearning from one task or
domain to another.

Develop a better
understanding of how
to characterize theperformance of an AIsystem, including improved
methods to predict AIsystem behavior in systemof-systems contexts andnew environments.

Robust and resilient AI

Complex multi-agent
scenarios

Cultivate methods thatcan overcome adverse
conditions, including
multiple classes ofadversarial attacks, andadvance approaches
that enable assessmentof types and levels ofvulnerability, immunity, andfairness.

Advance the understandingof interacting cohorts ofAI systems, including
research into adversarial
vulnerabilities andmitigations, along with theapplication of game theory
to varied and complex
scenarios.

Integrated AI, modeling,simulation, and design

Advancedscene understanding

Progress the use of simulations
as sources of data andscenarios for training andtesting AI systems, and use AI
to serve as a generative design
engine in scientific discovery
and engineering.

Evolve perceptual models toincorporate multi-source andmulti-modal information to
support enhanced actionableawareness and insight acrossa range of complex, dynamic
environments and scenarios.

Preserving personal privacy

AI system risk assessment

Assure privacy protection indata use for AI development
and operation throughadvancements in anonymity
techniques and technologies
such as multi-party federated
learning.

Advance capabilities to support
risk assessment, including
standard methods andmetrics for evaluating degrees
of auditability, traceability,interpretability, explainability,
and reliability.

Enhanced human-AI interaction and teaming
Progress the understandingof human-AI complementarity,
methods for augmenting humanreasoning, fluid handoffs inmixed-initiative systems, andAI technology’s perceptionof human intention andcommunications.

Toward more generalartificial intelligence
Research challenges andmysteries of human intellect,
including the ability tolearn in an unsupervisedmanner, amass and applycommonsense, build causal
models, exercise selfawareness, and generalize
knowledge.

Priorit y A reas
for AI Research
Investment.

AI autonomous systems
Advance a system’s ability
to accomplish goals
independently, or w
 ith minimal
supervision from humanoperators in environmentsthat are complex and
unpredictable.

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CHAPTER 11• Triple the number of National AI Research Institutes. The government should triple
the current number of federally funded national AI research institutes across a range ofregions and research areas.19 This would increase training and research opportunities
for students and academic faculty, national lab researchers, and non-profit researchorganizations.

“• Invest in talent that will transform the field. In parallel, NSF or the proposed NTF
should invest in top AI researchers and interdisciplinary teams, launching grant awards
that make big bets on the people and the out-of-the-box ideas that could lead to
unexpected breakthroughs.

“Democratized access to computeenvironments, data, and testing
facilities will provide researchers
beyond leading industry players
and elite universities the ability
to pursue progress on the cuttingedge of AI.”

Recommendation

Expand access to AI resources through a National AI Research Infrastructure. Democratizedaccess to compute environments, data, and testing facilities will provide researchers
beyond leading industry players and elite universities the ability to pursue progress on
the cutting edge of AI. It will strengthen the foundation of American AI innovation by
supporting more equitable growth of the field, expanding AI expertise across the country,
and applying AI to a broader range of fields. This national infrastructure should have fivemain elements:• A National AI Research Resource (NAIRR).20 To bridge the “compute divide,”21 the
NAIRR would provide verified researchers and students subsidized access to scalable
compute resources, co-located with AI-ready government and non-government
data sets, educational tools, and user support. It should be created as a public-private
partnership, leveraging a federation of cloud platforms.22• A set of domain-specific AI R&D test beds. Sponsored by various federal agencies,
these would provide accessible facilities, establish benchmarking standards, and build
communities of discovery around AI application areas that are in the public interest.

p
191

A C C E L E R AT I N G A I I N N O VAT I O N• Large-scale, open training data. This should include curation, hosting, and maintenanceof complex data sets; incentives to the private sector and academia to share data sets; andfunding for teams of data engineers and scientists to unlock public data currently held by
the government for use by the AI research community.
• An open knowledge network. Coordinated by the Office of Science and Technology
Policy, such a resource would enable use of constructed and organized world knowledge todevelop AI systems that operate effectively and efficiently.23• A Multilateral AI Research Institute. To foster collaborative R&D with researchers from key
allies and partners (described further in Chapter 15 of this report).
These resources would work in complement to each other, providing a virtuous cycle of data,experimentation, testing, and knowledge-building that would fuel innovation and application of
AI to a wide range of challenge problems and fields of study.
National AI
Research
Infrastructure.

Leverage both sides of the public-private partnership. U.S. leadership in technologies like AI
depends upon closer public-private collaboration and a shared sense of responsibility for
U.S. global competitiveness. To promote innovation and accelerate the growth of globallycompetitive firms in strategic emerging sectors, the government should:

Recommendation• Create markets for AI and other strategic technologies. The application of AI acrossgovernment agencies can save taxpayer dollars and improve the quality of publicp192

CHAPTER 11

services. Some applications can be adopted directly from the private sector, whileothers are unique to the government mission. By accelerating AI adoption acrossfederal agencies, the government can drive additional commercial investment in AI
applications that benefit national security and the public good.24• Form a network of regional innovation clusters focused on strategic emerging
technologies. The government should designate regional innovation clusters focusedon strategic emerging technologies like AI to foster the growth of small companies
in sectors that are critical to overall U.S. competitiveness. Established through acompetitive bid process, the clusters would offer participants from industry andacademia
tax incentives,
research
grants, and access to federal R&D resources.
Graphic
9.5: Regional
US Innovation
Clusters

RegionalInnovation

Cluster Strength* by County, 20170.00%

med=47.30%

100%

Clusters.

Example Cluster Organization
Firm 1

Firm 2

Technology
Research Center

Research
Institution

KEY:
Federal R&D
Resource

DOD Laboratories and Centers
DOE National Laboratories*Cluster strength is the percent of traded employment in a region with high employment specialization. This is one of many important factors to consider
when selecting locations for regional innovation clusters.

Image source: U.S. Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard
Business School. Data source: U.S. Census Bureau.

The private sector should:• Privately fund an AI competitiveness consortium. Private firms should establish a
non-profit organization with $1 billion in funding over five years to broaden AI research
opportunities and support AI skills and education. This donation-funded organization
would focus on fostering economic opportunity through resources for AI research andAI skills training. Such corporate social responsibility spending to promote AI educationand entrepreneurship would help bridge the gap between digital “haves” and “havenots.”p
193

A C C E L E R AT I N G A I I N N O VAT I O N

Tackle some of humanity’s biggest challenges.

Recommendation

Tackle S ome of
Humanit y ’s Biggest
Challenge.

““By focusing on solving realhuman problems that impact the
lives of millions of people, we canbuild a new raison d’etre for the
triangular alliance of government,
academia, and industry ...”p194

CHAPTER 11

Chapter 11 - Endnotes
A 2020 analysis of arXiv papers on AI found private-sector basic AI research to be thematicallynarrower than the broader corpus of AI publications, focusing on deep learning and computational
infrastructure to support deep learning. Furthermore, the study found that elite academic institutions
that collaborate more closely with industry had a similar narrowing of thematic concentration,
leading to a tilting of the U.S. AI research environment away from the diversity still preserved inother countries. Joel Klinger, et al., A Narrowing of AI Research?, ArXiv (Nov. 18, 2020), https://
arxiv.org/pdf/2009.10385.pdf. Increasing specialization of hardware achieved through industry
investments has further prioritized commercial use cases, making it costly to pursue approaches
outside the mainstream. Sara Hooker, The Hardware Lottery, ArXiv (Sept. 21, 2020), https://arxiv.org/
pdf/2009.06489.pdf.

1

The Trump Administration’s proposed budget for non-defense AI R&D in Fiscal Year 2021 was$1.5 billion, a growth from the just under $1 billion spent in Fiscal Year 2020. The Networking &Information Technology Research & Development Program, Supplement to The President’s FY2021
Budget, National Science & Technology Council at 4, 12 (Aug. 14, 2020), https://www.nitrd.gov/
pubs/FY2021-NITRD-Supplement.pdf. The National AI Initiative Act of 2020 included in the National
Defense Authorization Act for 2021 included authorization for additional investments in AI R&D at the
National Science Foundation (NSF), Department of Energy (DOE), National Institute of Standards andTechnology (NIST), and the National Oceanic and Atmospheric Administration (NOAA). See Pub. L.
116-283, William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021, 134
Stat. 3388 (2021).

2

OpenAI estimates that since 2012, the amount of compute used in the largest AI training runs isdoubling every 3.4 months. See Dario Amodei & Danny Hernandez, AI and Compute, OpenAI (May16, 2018), https://openai.com/blog/ai-and-compute/. Based on projections by OpenAI, at the current
rate of increasing costs of model training, “in 4 years, training the largest model will cost more thanlaunching a rocket into orbit.” Yaroslav Bulatov, Large-scale AI and Sharing of Models, Medium (July20, 2019), https://yaroslavvb.medium.com/large-scale-ai-and-sharing-of-models-4622ba59ec18. For
efforts that involve robotics or real-world applications, development requires additional resources interms of complex modeling and simulation capabilities for training algorithms as well as specialized
facilities for experimentation.

3

A recent study found that from 2004 to 2018, 131 AI professors left universities for industry and
90 adopted a dual affiliation while maintaining part-time positions at a university. The study also
documented the adverse effect that these departures had on AI startups of students from these
universities. Michael Gofman & Zhao Jin, Artificial Intelligence, Education, and Entrepreneurship,
SSRN at 2 (Oct. 26, 2020), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3449440. Highsalaries in the commercial sector pull researchers from academic tracks—in 2019, 57% of AI/ML
PhD graduates in North America went to industry versus staying in academia for post-doc, research,or faculty appointments. Stuart Zweben & Betsy Bizot, 2019 Taulbee Survey, Computing Research
Association at 11 (May 2020), https://cra.org/wp-content/uploads/2020/05/2019-Taulbee-Survey.pdf.

4

For example, NSF, which provides 85% of federal funding for computer science, funded $188 million
in core AI research in 2019 but did not have room in the budget to fund another $178 million worth ofhighly rated proposals. This was an improvement from 2018, when they funded $165 million but left
$185 million of highly rated work unfunded. Furthermore, NSF (in partnership with the Department ofAgriculture) funded seven National AI Research Institutes in 2020 but was unable to fund the morethan 30 that were judged worthy of supporting; NSF presentation to NSCAI (January 2020).

5

The time computer science faculty can spend holding concurrent appointments in industry has
increased from 20% to up to 50% to 80%, which has implications on their academic responsibilities
including recruitment of students and development of coursework and seminars, as well as thepossible consequence of aligning graduate-student work to industry’s needs over high-impact
basic research. Shwetak Patel, et al., Evolving Academia/Industry Relations in Computing Research,
Computing Community Consortium at 3 (June 2019), https://cra.org/ccc/wp-content/uploads/
sites/2/2019/06/Evolving-AcademiaIndustry-Relations-in-Computing-Research.pdf.

6

The annual Taulbee study that tracks the field of computer science (CS) found that women makeup 21.0% of CS bachelor degree graduates and 20.3% of CS doctoral graduates, and domestic
underrepresented minorities make up 14.7% of CS bachelor degree graduates and only 3.1% ofdoctoral graduates. Stuart Zweben & Betsy Bizot, 2019 Taulbee Survey, Computing Research
Association at 4, 5, 22 (May 2020), https://cra.org/wp-content/uploads/2020/05/2019-Taulbee-Survey.
pdf. A trend toward narrowing participation in the field holds the potential to worsen this state. See
Nur Ahmed & Muntasir Wahed, The De-democratization of AI: Deep Learning and the Compute Divide
in Artificial Intelligence Research, ArXiv (Oct. 22, 2020), https://arxiv.org/abs/2010.15581.

7

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See, e.g., Joel Klinger, et al., A Narrowing of AI Research?, ArXiv (Nov. 18, 2020), https://arxiv.org/
pdf/2009.10385.pdf; Sara Hooker, The Hardware Lottery, ArXiv (Sept. 21, 2020), https://arxiv.org/
pdf/2009.06489.pdf.

8From the very outset of the field, the federal government had a hand in fostering research. The Air
Force, via RAND, supported the work of Herbert Simon and Allen Newell, who in 1956 created the firstsuccessful AI computer program, the Logic Theorist. Mariana Mazzucato, The Entrepreneurial State:
Debunking Public vs. Private Sector Myths, Anthem Press (2013). The Defense Advanced Research
Projects Agency (DARPA) (then ARPA) funded the work of Charles Rosen, who developed the firstself-navigating robot, “Shakey,” in 1972. Shakey the Robot, DARPA (last accessed Dec. 30, 2020),
https://www.darpa.mil/about-us/timeline/shakey-the-robot. Reinforcement learning, the approach on
which many of today’s commercial applications are based, was sustained through the “AI Winter”
of the 1990s by NSF’s support of Andrew Barto; NSCAI staff engagement with NSF (Aug. 8, 2019).
DARPA’s 30 years of funding for research on image understanding created the foundation forautonomous driving capabilities. DARPAtv, DARPA Artificial Intelligence Colloquium Opening Video,
YouTube (March 12, 2019), https://www.youtube.com/watch? v=FTaW6ZJ9oyQ. The PAL program run
by DARPA in the mid-2000s led to the development of the first artificially intelligent assistant, which
eventually became Siri. DARPAtv, DARPA and AI: Visionary Pioneer and Advocate, YouTube (Dec. 7,2018), https://www.youtube.com/watch? v=ri5gOjYgLns.

9

10
Neil C. Thompson, et al., Building the Algorithm Commons: Who Discovered the Algorithms
that Underpin Computing in the Modern Enterprise?, Global Strategy Journal at 4 (2020), https://
onlinelibrary.wiley.com/doi/epdf/10.1002/gsj.1393.
11
For example, non-elite universities and AI startups have difficulty affording the cost of computeresources and data for training sophisticated ML models. Nur Ahmed & Muntasir Wahed, The Dedemocratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research,
arXiv (Oct. 22, 2020), https://arxiv.org/abs/2010.15581. Ninety percent of Silicon Valley AI startups
were purchased by large technology companies between 2013 and 2018. Ryan Kottenstette, Silicon
Valley Companies Are Undermining the Impact of Artificial Intelligence, TechCrunch (March 15,2018), https://techcrunch.com/2018/03/15/silicon-valley-companies-are-undermining-the-impact-ofartificial-intelligence/. These same companies dominate U.S. patent lists, excluding adoption patents.
Al AuYeung, Who is Winning the AI Race?, IPWatchdog (Feb. 1, 2020), https://www.ipwatchdog.
com/2020/02/01/winning-ai-race/id=118431/.

Michael Porter, Clusters and the New Economics of Competition, Harvard Business Review (Nov.Dec. 1998), https://hbr.org/1998/11/clusters-and-the-new-economics-of-competition.

12

William R. Kerr & Frederic Robert-Nicoud, Tech Clusters, Journal of Economic Perspectives at 63(Summer 2020), https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.34.3.50.

13

14
Specifically, Seattle, Boston, San Francisco, San Diego, and San Jose. Robert D. Atkinson, et al.,The Case for Growth Centers: How to Spread Tech Innovation Across America, Brookings (Dec. 9,2019), https://www.brookings.edu/research/growth-centers-how-to-spread-tech-innovation-acrossamerica/.

The NSF and other government agencies are doing admirable work, with the resources available,
to encourage diverse research and create economies of scale for AI innovation, but they will not
produce a strategic effect at the current level of effort, which is set against the backdrop of an
overall decline in federal investment in R&D. Other notable recent federal initiatives include DARPA’s
Artificial Intelligence Exploration Program, which fast tracks funding for awards up to $1 million to
explore feasibility of new AI concepts within an 18-month timeframe; and NSF’s National AI ResearchInstitutes effort, which in 2020 funded seven multi-institution, university-based research institutes at$4 million per year for five years and plans to launch another eight in 2021. Artificial Intelligence at
NSF, NSF (Aug. 26, 2020), https://www.nsf.gov/cise/ai.jsp.

15

Funding for the National Institutes of Health (NIH) has grown from $30 billion in 2010 to $41 billion
in 2020. NIH Budget History: NIH Budget Mechanism Detail, NIH Data Book (Oct. 2019), https://report.nih.gov/nihdatabook/category/1; Budget, NIH (June 29, 2020), https://www.nih.gov/about-nih/what-we-do/budget.

16

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Chapter 11 - Endnotes
In 1953, the U.S. spent 0.72% of its GDP on R&D. In 1957, when the then-Soviet Union launched
Sputnik, it had grown to 1.3%. R&D spending peaked at 1.86% in 1964. In 2017, it declined below
1953 levels to 0.61%. Federal R&D Budget Dashboard, American Association for the Advancementof Science (last accessed Jan. 14, 2021), https://www.aaas.org/programs/r-d-budget-and-policy/
federal-rd-budget-dashboard.
17

18
The National AI Initiative Act of 2020 included in the National Defense Authorization Act for Fiscal
Year 2021 creates a structure for a more strategic approach to harnessing AI through establishmentof a National AI Initiative Office within the Office of Science and Technology Policy and associatedadvisory group and interagency construct. See Pub. L. 116-283, William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).
19
The NSF awarded grants for the first national AI research institutes in 2020, supporting seven
university-based, multi-institution consortia organized around fundamental and applied areas of AI
research, and plans to fund a second round of institutes in 2021, coordinating support not only withinteragency partners but also with private sector stakeholders to launch eight additional institutes.
Artificial Intelligence at NSF, NSF (Aug. 26, 2020), https://www.nsf.gov/cise/ai.jsp.

Acting on a recommendation NSCAI issued in our First Quarter Recommendations, Congress hastaken the first step to establish the NAIRR in the Fiscal Year 2021 National Defense Authorization Act,
creating a task force to develop a roadmap for a future NAIRR. The result of this effort will be due toCongress 18 months after appointment of task force members. See Pub. L. 116-283, William M. (Mac)Thornberry National Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021); see alsoFirst Quarter Recommendations, NSCAI at 12 (March 2020), https://www.nscai.gov/previous-reports/.

20

21
Since the explosion of deep learning in 2012 and accompanying growth in use of specialized
hardware for AI computing, there has arisen what some have termed the “compute divide”—adisparity in access between large technology companies and elite universities and middle- andlower-tier universities to the resources necessary for cutting-edge AI research. Nur Ahmed & Muntasir
Wahed, The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence
Research, ArXiv (Oct. 22, 2020), https://arxiv.org/abs/2010.15581.

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22This approach could build on successful models such as the COVID-19 High Performance
Computing Consortium, (https://covid19-hpc-consortium.org/) and NSF’s CloudBank, (https://www.
cloudbank.org/).

This would build on prior work undertaken by the Networking and Information Technology Researchand Development (NITRD) Program Big Data Interagency Working Group. Open Knowledge Network:
Summary of the Big Data IWG Workshop, National Science & Technology Council (Nov. 2018), https://www.nitrd.gov/pubs/Open-Knowledge-Network-Workshop-Report-2018.pdf. It would also build
upon ongoing efforts through NSF’s Convergence Accelerator track on Open Knowledge Networks.
NSF Convergence Accelerator Awards Bring Together Scientists, Businesses, Nonprofits to Benefit
Workers, NSF (Sept. 20, 2019), https://www.nsf.gov/news/special_reports/announcements/091019.jsp.

23

Congress took an important step in the Consolidated Appropriations Act, 2021 by calling on
the General Services Administration to create a five-year program to be known as the ‘‘AI Centerof Excellence’’ (AI CoE) to “facilitate the adoption of artificial intelligence technologies in theFederal Government,” among other duties. The AI CoE can help bridge discrete efforts acrossfederal agencies to create a sizable market for government-specific AI applications. See Rules
Committee Print 116-68, Text of the House Amendment to Senate Amendment to H.R. 133, U.S.
House Committee on Rules at 378-81 (Dec. 11, 2020), https://rules.house.gov/sites/democrats.rules.
house.gov/files/BILLS-116HR133SA-RCP-116-68.pdf (referring specifically to section 103 of theConsolidated Appropriations Act, 2021). In addition, the Defense Innovation Unit (DIU) is playing arole in creating markets at the intersection of AI and other strategic technologies through its projectbased approach. Focus areas include AI applications for space systems, advanced diagnostics,
semiconductors/advanced hardware, and other critical technologies identified by NSCAI in Chapter
16 of this report. DIU’s experience indicates that creating a market for strategic technologies begins
with the Department of Defense (DoD) and other government agencies pursuing an approach that is(a) contractually flexible, (b) aligned with firms’ technological development plans, and (c) generating
financial incentives through opportunities to scale production. DIU Making Transformative Impact Five
Years In, DoD (Aug. 27, 2020), https://www.defense.gov/Explore/News/Article/Article/2327021/diumaking-transformative-impact-five-years-in/.

24

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CHAPTER 12

Chapter 12:
Intellectual Propertyp
199

INTELLECTUAL PROPERTYIntellectual Property Policy
is a National Security Priority

Issue Executive Orderon IP for AI and
Emerging Technologies.

Develop Plan to Reformand Establish IP Policies
and Regimes.

Propose Executive andLegislative Actions.

Assess “IP
Considerations.”

Integrate into
National Security,
Economic, andTechnology
Competitiveness
Strategies.

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China is both leveraging and exploiting intellectual property (IP)policies as a critical tool within its national strategies for emerging
technologies. The United States has failed to similarly recognize
the importance of IP in securing its own national security, economic
interests, and technology competitiveness. The U.S. has notdeveloped comprehensive IP policies to incentivize investments1
in and protect the creation of artificial intelligence (AI) and otheremerging technologies.2 The consequence of this policy void—which includes legal uncertainties created by current U.S. patenteligibility and patentability doctrine, the lack of an effective response
to China’s domestic and geopolitical strategies centered on its IP
institutions,3 and the lack of effective data protection policies—isthat the U.S. could lose its prime position in IP global leadership.
At the same time, by strengthening its IP regimes,4 China is poised
to “fill the void” left by weakened U.S. IP protections, particularly
for patents, as the U.S. has lost its “comparative advantage insecuring stable and effective property rights in new technologicalinnovation.”5 This stark policy asymmetry has multiple significant
domestic and international implications for the U.S.
First, U.S. courts have severely restricted what types of computer-implemented andbiotech-related inventions can be protected under U.S. patent law.6 Critical AI and biotechrelated inventions have been denied patent protection since 2010.7 Facing uncertainty
in obtaining and retaining patent protection, inventors pursue trade secret protection.
Trade secrets do not readily promote innovation markets, because trade secrets, unlike
patents, do not contribute to accessible technical knowledge in the public domain.8 Whilethese impacts might not be immediate, the long-term effects on AI and other emerging
technology developments and competitiveness are concerning.9

p201

INTELLECTUAL PROPERTYSecond, China has met its strategic policy goal of increasing the quantity of its patentapplications and issued patents, creating the narrative that it has “won” the innovation
race. In 2019, the total number of “invention” patent applications filed at the China National
Intellectual Property Administration (CNIPA) was approximately three times as many asutility patent applications filed at the U.S. Patent and Trademark Office (USPTO).10 China
also led the world in international patent applications under the Patent Cooperation Treaty(PCT) system of the World Intellectual Property Organization (WIPO).11 Critically, China is
now frequently identified as the current leader in domestic patent application filings for
AI inventions.12 Globally, AI patent applications originating from China outnumber thoseoriginating from the United States, especially in recent years.13““The U.S. has not developed
comprehensive IP policies toincentivize investments in andprotect the creation of AI andother emerging technologies.
The consequence of thispolicy void ... is that the U.S.
could lose its prime position in
IP global leadership.”p
202

Graphic 12.9:
12.9: USUS China
China IPIP Trends
TrendsGraphic
CHAPTER 12

China IPIP Trends
TrendsChina

China I P Trends.

China’s National
National IP
IP Regimes
Regimes forfor AIAI
China’sand Emerging
Emerging Technologies
Technologies
andNational 13th
13th Five-Year
Five-Year Plan
Plan forfor thethe Development
Development ofof
National
Strategic
EmergingIndustries
articulates
IP-related
Strategic Emerging Industries articulates IP-related
goals forfor emerging
emerging technologies:
technologies:
goals
Revising
thePatentLaw andand Copyright
Copyright Law
Law
Revising the Patent Law
Strengthening IP
IP rights
rights protections
protections throughthrough rapid
rapid
Strengthening
rightsprotection
centers
rights protection centers
Developing strategic
strategic advancement
advancement plans
plans forfor IPIP
Developing
rightsofemerging
technologies
rights of emerging technologies
Improving overseas
overseas IP
IP rights
rights andand supporting
supporting
Improvingcompanies
involved
inoverseas
M&A
companies involved in overseas M&A

Patent filings
filings areare incentivized
incentivized by:by:PatentPatentsubsidies
Patent subsidies
Rewards forfor granted
granted patents
patents
Rewards
Patentquotasset
byprovincial oror municipal
municipal
Patent quotas set by provincial
governments
governments
Preferential treatment
treatment inin government
government procurement
procurement
Preferential
processes forfor companies
companies with
with Chinese
Chinese IP
IP
processes
Patent protection
protection isis increased
increased throughthrough preliminary
preliminary
Patentinjunctions forfor patentpatent infringement,
infringement, increases
increases inininjunctions
punitivedamagesforIP
infringement(allowsforpunitive damages for IP infringement (allows forquintuple damages
damages forfor willful
willful infringement),
infringement), andandquintuple
specializedIP
courtswith
efficient
resolution
and lowlow
specialized IP courts with efficient resolution andlitigation costs.
costs.
litigation

US: 621,000
621,000
US:
China: 1,400,000
1,400,000
China:

US: 456,000
456,000
US:
China: 241,000
241,000
China:

US: 2,163
2,163
US:
China: 5,708
5,708
China:
Self-declared
Self-declared
5G Standard5G StandardEssential Patents
Essential Patents

US: 57,840
57,840US:
China: 58,990
58,990China:

2019 PCT
2019 PCT
Filings
Filings

2009 Domestic
2009 Domestic
Patent Office
Patent OfficeFilings
Filings

2019 Domestic
2019 Domestic
Patent Office
Patent OfficeFilings
Filings

CSET Translation of National 13th Five-Year Plan for the Development of Strategic Emerging Industries,
Central Committee of the Communist Party of China and the PRC State Council (Published Nov. 29,2016) (translation by CSET on Dec. 9, 2019), https://cset.georgetown.edu/research/national-13thfive-year-plan-for-the-development-of-strategic-emerging-industries//; Eric Warner, Patenting andInnovation in China: Incentives, Policy, and Outcomes, RAND at 17-18 (Nov. 2014), https://apps.dtic.
mil/dtic/tr/fulltext/u2/a619128.pdf; Trademarks and Patents in China: The Impact of Non-Market Factors
on Filing Trends and IP Systems, U.S. Patent and Trade Office (Jan. 2021), https://www.uspto.gov/
sites/default/files/documents/USPTO-TrademarkPatentsInChina.pdf; Ryan Davis, 4 Things to Know
About China’s Revised Patent Law, Law 360 (Nov. 5, 2020), https://www.law360.com/articles/1326419/;
Justice Tao Kaiyuan, China’s Commitment to Strengthening IP Judicial Protection and Creating a Bright
Future for IP Rights, WIPO Magazine (June 2019), https://www.wipo.int/wipo_magazine/en/2019/03/
article_0004.html.
Note: The self-declared 5G standard-essential patent numbers are as of February 2020 and represent
the combined total from the two companies that are the largest filers in each country. For the UnitedStates, 2,163 represents the 1,293 applications filed from Qualcomm and 870 filed from Intel. For China,
5,708 represents the 3,147 filed from Huawei and 2,561 filed from ZTE. This number also represents thestandard-essential patents filed, not the number of patents granted. See Jed John Ikoba, Huawei Has
Filed the Most 5G Patents Globally as of February 2020 - Report, Gizmochina (June 2, 2020), https://www.
gizmochina.com/2020/06/02/huawei-has-the-most-5g-standard-essential-patents-globally/;
China
Becomes Top Filer of International Patents in 2019 Amid Robust Growth for WIPO’s IP Services, Treaties
and Finances, WIPO Media Center (Apr. 7, 2020), https://www.wipo.int/pressroom/en/articles/2020/
article_0005.html; For the domestic patent office filings, according to the China National Intellectual
Property Administration (CNIPA), “the number of invention patent applications it received increased bymore than 500 percent between 2009 and 2019, from 241,000 to 1.4 million (although, interestingly, there
was a 9 percent decrease from 2018 to 2019). In comparison the number of patent applications at thep203

INTELLECTUAL PROPERTYUSPTO increased by only 35% (from 456,000 to 621,000) over the same period. Hence, while in 2009
U.S. patent applications outnumbers Chinese applications by almost two-to-one, by 2019, the ratio hadcompletely reversed. Most of the Chinese patenting increase can be attributed to applications filed bydomestic applicants. Out of the 1.4 million CNIPA applications in 2019, domestic sources filed almost
90 percent (compared to 48 percent of USPTO applications).” See Patrick Thomas & Dewey Murdick,Patents and Artificial Intelligence: A Primer, Center for Security and Emerging Technology at 10 (Sept.
2020), https://cset.georgetown.edu/wp-content/uploads/CSET-Patents-and-Artificial-Intelligence.pdf.
In 2019, there were also almost two million utility model applications in China. Id at n. 17.

““China has met its strategic
policy goal of increasing thequantity of its patent applications
and issued patents, creating thenarrative that it has “won” theinnovation race.”

Third, regardless of quality concerns,14 China’s prolific patent application filings may further
hurt U.S. innovators by creating a vast reservoir of “prior art” (the term in patent law for
the worldwide scientific and technical knowledge by which an invention is evaluated todetermine if it is new). This dramatically increases the quantity of prior art that must be
reviewed in examining a patent application. As a result, the patent examination process
at the USPTO will become increasingly difficult, if not onerous. At the same time, U.S.
inventors may find it more difficult to obtain patents because they must show that theirinventions are not disclosed in the prior art publications anywhere in the world, includingin the Chinese-language patent applications filed in China and internationally.15 As Chinese
patents come to dominate prior art searches by patent offices throughout the world, thecurrent dominance of U.S. patents in worldwide prior art searches will erode.16

Fourth, and consistent with China’s extensive patent application filings, China’s companies
have been identifying too many patents as “standard-essential” in standards development
organizations, alleging that these patents must be practiced to comply with a technicalstandard.17 Although standard development organizations require patent owners to selfidentify patents that may be deemed essential in future standards, these organizations
leave final essentiality determinations to private companies negotiating licenses or, if
there is a dispute, to courts.18 This practice of “overdeclaring” standard-essential patents(SEPs) furthers China’s global narrative that it has “won” the race to such standardizedp204

CHAPTER 12

technologies as 5G, prompting other countries to adopt China’s technologies in their own
communications infrastructures.19 A worrisome result may be that U.S. companies must
pay billions in royalties to China’s companies or face claims and resulting litigation thatthey willfully infringed on Chinese company patent rights.20
Fifth, the lack of explicit legal protections for data or express policies on data ownership mayhinder innovation and collaboration, particularly as technologies evolve.21 The absence ofdata protection regimes may disincentivize parties from making necessary investments todevelop data sets that are critical for machine learning (ML) and AI systems.22 Additionally,
the absence of data governance policies (such as contracting best practices) for IP-type
protections or ownership rules could undermine the willingness of companies to enter
into the public-private partnerships that are crucial for creating cutting-edge technologicalinnovations.23 This could also create challenges for U.S. collaboration with allies and otherpartners in vital AI R&D where data rights or ownership claims come into question.24“

Lastly, as further evidence that China views IP as essential in its domestic economic
development, China continues to pervasively steal American IP-protected technologicaladvances through varied means like cyber hacking of businesses and research institutes,
technological espionage, blackmail, and illicit technology transfer.25“China continues to pervasively
steal American IP-protected
technological advances throughvaried means like cyber hackingof businesses and research
institutes, technologicalespionage, blackmail, and illicit
technology transfer.”p
205

INTELLECTUAL PROPERTYThe IP Policy Void.
The U.S. Government needs to address these vulnerabilities resulting from the lack ofcomprehensive IP policies. Currently, the U.S. Government does not efficiently utilize IP
policy as a tool to support national strategies for national security, economic interests,
and technology competitiveness in AI and emerging technologies. The majority of theUnited States Government’s coordinated IP policy efforts are focused on IP enforcement
and preventing IP theft.26 The U.S., however, lacks an agency or interagency entity that is
empowered to both develop and execute national IP policies that support and integrate withnational strategies. As a result, the United States lacks cohesive, legislatively mandated AI
and emerging-technology IP policies that are integrated into national strategy frameworks
to address, for example, global competition from countries like China.
America’s IP laws and institutions must be considered as critical components for
safeguarding U.S. national security interests, including advancing economic prosperity
and technology competitiveness. The United States must, at a minimum, articulate anddevelop national IP reforms and policies with the goal of incentivizing, expanding, andprotecting AI and emerging technologies, at home and abroad. Such policies should
be developed and proposed via the Executive Branch with a process that integrates
the disparate departments and agencies that serve important roles in promoting U.S.
innovation. The Executive Branch should:““America’s IP laws andinstitutions must be consideredas critical components for
safeguarding U.S. national
security interests, including
advancing economic
prosperity and technology
competitiveness.”p206

CHAPTER 12

Recommendation

National Intellectual
Proper t y
C onsiderations.

p207

Develop and implement national IP policies to incentivize, expand, and protect AI andemerging technologies. The President should issue an executive order to recognize IP
as a national priority and require the development of a comprehensive plan to reformand create IP policies and regimes that further national security, economic interests,
and technology competitiveness strategies. The Commission recommends that theexecutive order direct the Vice President, as chair of the Technology CompetitivenessCouncil (TCC), or otherwise as chair of an interagency task force, to oversee this effort.
The executive order should direct the Secretary of Commerce—in coordination with theUnder Secretary of Commerce for Intellectual Property and the Director of the USPTO27—
to develop proposals to reform and establish new IP policies and regimes, as needed, toincentivize, expand, and protect AI and emerging technologies. The plan should include
proposals for executive and legislative actions for IP policy changes to achieve theseobjectives and should be accompanied by an assessment of a non-exhaustive list of “IPconsiderations.”28 The Executive Order should direct the Vice President to assess which
IP policies, regimes, and reform proposals from the Secretary of Commerce should be
integrated into national security, economic, and technology competitiveness strategies
and empower the Secretary of Commerce to facilitate implementation of such proposals.

INTELLECTUAL PROPERTY

Chapter 12 - Endnotes
Advances in emerging technologies require significant investments. These investments are partly
public, but advances also require extensive private investments.

1

Technologies critical to national security interests include AI and biotechnology. NSCAI proposes aninitial list of emerging technologies key to U.S. national competitiveness in Chapter 16 of this report.

2

CSET translation of National 13th Five-Year Plan for the Development of Strategic Emerging
Industries, Central People’s Government of the People’s Republic of China at 59 (Nov. 29, 2016)(translation by CSET on Dec. 9, 2019), https://cset.georgetown.edu/research/national-13th-fiveyear-plan-for-the-development-of-strategic-emerging-industries/. China continues to make extensive
reforms to its IP regimes in furtherance of its innovation and industrial competitiveness goals. See
Mark Cohen, IPO’s Comments on Recent Patent Legislation: Untangling a Complex Web, China IPR
blog (Dec. 15, 2020), https://chinaipr.com/2020/12/15/ipos-comments-on-recent-patent-legislationuntangling-a-complex-web/.

3

China’s actions include ensuring that AI and associated technologies are eligible for patent
protection, increasing damages awards for patent infringement, continuing to issue preliminary
injunctions for infringement of valid patents, and creating specialized IP courts with more efficient
resolution of IP cases. See Kevin Madigan & Adam Mossoff, Turning Gold into Lead: How Patent
Eligibility Doctrine Is Undermining U.S. Leadership in Innovation, George Mason Law Review at 943946 (April 13, 2017), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2943431 [hereinafter
Turning Gold Into Lead]; Ryan Davis, 4 Things to Know About China’s Revised Patent Law, Law 360(Nov. 5, 2020), https://www.law360.com/articles/1326419/; Liaoteng Wang et. al., A Comparative
Look at Patent Subject Matter Eligibility Standards: China Versus the United States, IP Watchdog(June 12, 2020), https://www.ipwatchdog.com/2020/06/12/comparative-look-patent-subject-mattereligibility-standards-china-versus-united-states/id=122339/; Erick Robinson, Everything You Need toKnow about China’s New Preliminary Injunction Rules, IAM (Dec. 21, 2018), https://www.iam-media.
com/designs/everything-you-need-know-about-chinas-new-preliminary-injunction-rules; Justice Tao
Kaiyuan, China’s Commitment to Strengthening IP Judicial Protection and Creating a Bright Future for
IP Rights, World Intellectual Property Organization, WIPO Magazine (June 2019), https://www.wipo.int/
wipo_magazine/en/2019/03/article_0004.html.

4

5

See Turning Gold Into Lead, at 955.

See Turning Gold Into Lead. In January 2019, the United States Patent & Trademark Office (USPTO)
published initial patent eligibility guidance that applies during examination of patent applications at
the USPTO, which arguably decreased uncertainty as to patent eligibility determinations during thepatent application examination and granting process. However, the United States Court of Appeals for
the Federal Circuit, the appellate court with jurisdiction of appeals from patent cases, held that it is
not bound by the Guidance. See Cleveland Clinic Found. v. True Health Diagnostics LLC, 760 F. App’x
at 1013, 1020 (Fed. Cir. 2019) (non-precedential); In re Rudy, 956 F.3d 1379, 1383 (Fed. Cir. 2020)(precedential) (citing Cleveland Clinic Found., 760 F. App’x at 1021).

6

Athena Diagnostics v. Mayo Collaborative Services, 915 F.3d 743 (Fed. Cir. 2019), rehearing en bancdenied 927 F.3d 1333 (Fed. Cir. 2019) (method of diagnosing certain, previously undiagnosable,
patients suffering from the neurological disorder myasthenia gravis using MuSK autoantibodies); The
Cleveland Clinic Found. v. True Health Diagnostics LLC, 760 F. App’x 1013 (Fed. Cir. 2019) (methodof assessing the risk a patient has cardiovascular disease by analyzing the level of a certain enzyme
in a patient’s blood); Roche Molecular Systems, Inc. v. Cepheid, 905 F.3d 1363 (Fed. Cir. 2018)(DNA primers used in a method to detect the pathogenic bacterium Mycobacterium tuberculosis);
Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371 (Fed. Cir. 2015), cert. denied, 136 S. Ct.
2511 (2016) (method of diagnosing fetal characteristics based on paternally inherited DNA foundin a mother’s bloodstream without creating a major health risk for the fetus); PUREPREDICTIVE,
Inc. v. H20.AI, Inc., No. 17-cv-03049-WHO, 2017 WL 3721480 (N.D. Cal. Aug. 29, 2017) (predictive
analytics); Power Analytics Corp. v. Operation Tech., Inc., No. 16-cv-01955 JAK (FFMx), 2017 WL
5468179 (C.D. Cal. July 13, 2017) (“computer simulation techniques with real-time system monitoringand prediction of electrical system performance”).
7

p
208

CHAPTER 12

Chapter 12 - Endnotes
See Crash Course on Patents: What Is a Patent and Why Is It Useful, Ius mentis (last accessed Dec.
30, 2020), https://www.iusmentis.com/patents/crashcourse/whatis/ (because patents openly publish
details of the invention, other inventors can license this invention or think of enhancements or design
around the disclosure); Steven Hoffman & Calla Simeone, Trade Secret Protection & the COVID-19
Cure: Observations on Federal Policy-Making & Potential Impact on Biomedical Advances, JDSupra(Sept. 15, 2020), https://www.jdsupra.com/legalnews/trade-secret-protection-the-covid-19-37383/(discussing implications of uncertainty in patent eligibility on use of trade secrets for biomedical
advances).
8

Surveys and industry reports demonstrate that investment has already shifted away from patentintensive industries. See Mark F. Schultz, The Importance of an Effective and Reliable Patent System
to Investment in Critical Technologies, Alliance for U.S. Startups and Investors for Jobs at 24-37 (July2020), https://static1.squarespace.com/static/5746149f86db43995675b6bb/t/5f2829980ddf0c536e71
32a4/1596467617939/USIJ+Full+Report_Final_ 2020.pdf.

9Patrick Thomas & Dewey Murdick, Patents and Artificial Intelligence: A Primer, Center for Security
and Emerging Technology at 10 (Sept. 2020), https://cset.georgetown.edu/wp-content/uploads/
CSET-Patents-and-Artificial-Intelligence.pdf [hereinafter CSET, A Primer]; U.S. Patent Statistics Chart
Calendar Years 1963-2019, USPTO (April 2020), https://www.uspto.gov/web/offices/ac/ido/oeip/taf/
us_stat.htm.
10

11
See CSET, A Primer at 11; Aaron Wininger, China Surpasses U.S. to Become Top Filer of PCT
International Patent Applications in 2019, National Law Review (April 7, 2020), https://www.
natlawreview.com/article/china-surpasses-us-to-become-top-filer-pct-international-patentapplications-2019. China is on pace to continue being the top PCT filer in 2020. See Aaron Wininger,
China 2020 H1 Patent Data Indicates China Likely to Remain Top International Filer in 2020, National
Law Review (July 11, 2020), https://www.natlawreview.com/article/china-2020-h1-patent-dataindicates-china-likely-to-remain-top-international-filer.

AI Innovators, RS (last accessed Dec. 30, 2020), https://uk.rs-online.com/web/generalDisplay.
html?id=did-you-know/ai-innovators; George Leopold, China Dominates AI Patent Filings, Enterprise
AI (Aug. 31, 2020), https://www.enterpriseai.news/2020/08/31/china-dominates-ai-patent-filings/;
CSET, A Primer.

12

13

CSET, A Primer at 9, 12, n. 23.

14
Trademarks and Patents in China: The Impact of Non-Market Factors on Filing Trends and IP
Systems, USPTO at 1 (Jan. 2021), https://www.uspto.gov/sites/default/files/documents/USPTOTrademarkPatentsInChina.pdf; Jonathan Putnam, et al., Innovative Output in China, SSRN at 32 (Aug.
2020) (pending revision), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3760816.
15
Jeanne Suchodolski, et al., Innovation Warfare, North Carolina Journal of Law & Technology at
201 (Dec. 7, 2020), https://ncjolt.org/articles/volume-22/volume-22-issue-2/innovation-warfare/[hereinafter Innovation Warfare].
16
Rob Sterne, How China Will Fundamentally Change the Global IP System, IP Watchdog (July 24,2019), https://www.ipwatchdog.com/2019/07/24/china-changing-global-ip-system/id=111613/.
17
Over-declaration is already present in 5G. See Matthew Noble, et al., Determining WhichCompanies Are Leading the 5G Race, IAM (July/August 2019), https://www.twobirds.com/~/
media/pdfs/news/articles/2019/determining-which-companies-are-leading-the-5g-race.
pdf?la=en&hash=8ABA5A7173EEE8FFA612E070C0EA4B4F53CC50DE; Meeting the China Challenge:
A New American Strategy for Technology Competition, Working Group on Science and Technology
in U.S.-China Relations at 27, 29 (Nov. 16, 2020), https://china.ucsd.edu/_files/meeting-the-chinachallenge_ 2020_report.pdf [hereinafter Meeting the China Challenge].
18
IEEE SA Standards Board Bylaws, IEEE Standards Association (last accessed Jan. 15, 2020),
https://standards.ieee.org/about/policies/bylaws/sect6-7.html#loa.

p209

INTELLECTUAL PROPERTY

19
5G Technological Leadership, Hudson Institute (Dec. 5, 2020), https://www.hudson.org/
research/16547-5-g-technological-leadership; Innovation Warfare, at 201, n.130 (China’s firms
recognize the strategic importance of standard-setting activities and that participation in those forums
provides the legal means to both access and influence developing technologies).
20
Because standard-essential patents (SEPs) may reach into the hundreds of thousands fortechnologies, licensing fees carry significant economic repercussions. See 5G Technological
Leadership, Hudson Institute at 3 (Dec. 5, 2020), https://www.hudson.org/research/16547-5-gtechnological-leadership (“[P]atent counting might have negative consequences on firms working in
the US innovation economy … if judges or regulators rely on simple counts of total patents as a metricfor determining the value of patent portfolios. The failure to account for differences in patent quality
risks overcompensating some patent holders, namely those with less valuable technologies, but
undercompensating those that have developed breakthrough innovation.”); Andrei Iancu, Director of
USPTO, Remarks at the Center for The Protection of Intellectual Property 2020 Fall Conference (Oct.
7, 2020), https://cpip.gmu.edu/2020/10/20/cpip-2020-fall-conference-day-one-recap/; Muzammil
Hassan, et al., Who Owns Core 5G Patents? A Detailed Analysis of 5G SEPs, GreyB (2020), https://www.greyb.com/5g-patents/#The-State-of-Declared-5G-Patents; Cody M. Akins, Overdeclarationof Standard-Essential Patents, Texas Law Review (2020), https://texaslawreview.org/wp-content/
uploads/2020/02/Akins.Printer.pdf.
21
Mitchell Smith, A Comparison of the Legal Protection of Databases in the United States and EU:
Implications for Scientific Research, SSRN (May 23, 2010), https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=1613451; Daniel J. Gervais, Exploring the Interfaces Between Big Data andIntellectual Property Law, Journal of Intellectual Property, Information Technology and Electronic
Commerce Law (2019), https://scholarship.law.vanderbilt.edu/faculty-publications/1095.
22
In the USPTO report surveying stakeholders for perspectives on IP policy for AI, “[c]ommenterswere nearly equally divided between the view that new intellectual property rights were necessary
to address AI inventions and the belief that the current U.S. IP framework was adequate to address
AI inventions. Generally, however, commenters who did not see the need for new forms of IP rights
suggested that developments in AI technology should be monitored to ensure needs were keeping
pace with AI technology developments. The majority of opinions requesting new IP rights focused on
the need to protect the data associated with AI, particularly ML.” Public Views on Artificial Intelligenceand Intellectual Property Policy, USPTO at 15 (Oct. 2020), https://www.uspto.gov/sites/default/files/
documents/USPTO_ AI-Report_ 2020-10-07.pdf.

Thomas E. Ayers, Changing How We Buy Weapons Will Benefit Industry, Government
and Taxpayers, Defense News (Nov. 20, 2019), https://www.defensenews.com/opinion/commentary/2019/11/20/changing-how-we-buy-weapons-will-benefit-industry-government-andtaxpayers/ (discussing the tension between Air Force and vendors over IP protection).

23

24

See also the Chapter 15 Blueprint for Action.

25

Meeting the China Challenge, at 4, 16.

26
Annual Intellectual Property Report to Congress, U.S. Intellectual Property Enforcement Coordinator(March 2020), https://trumpwhitehouse.archives.gov/wp-content/uploads/2020/04/IPEC-2019-AnnualIntellectual-Property-Report.pdf (providing an overview of IP responsibilities across the United States
government).

Other Executive Branch departments and agencies, and the U.S. Copyright Office, should resourceand support the Secretary of Commerce in these efforts.

27

A non-exhaustive list of IP considerations should include patent eligibility doctrine, countering
China’s narrative on “winning” AI innovation based on patent application filings, the impact of China’s
patent application filings on USPTO’s examination process and U.S. inventors, impediments in IP
contractual system to public-private partnerships and international collaboration, IP protections
for data, combatting IP theft, AI inventorship, global IP alignment, democratizing innovation and IP
ecosystems, and SEPs process.

28

p
210

CHAPTER 13

Chapter 13: Microelectronics

p211

MICROELECTRONICS

Rebuilding U.S.
Microelectronics Leadership

Stay Two Generations
Ahead inMicroelectronics

Multiple Sourcesof Domestic
Cutting-Edge
Manufacturing

National
Microelectronics
Strategy

Double Down on
Microelectronics R&D

Tax Credits andGrants for U.S.
Fabrication
Facilitiesp212

CHAPTER 13

U.S. leadership in microelectronics is critical to overall U.S.
leadership in artificial intelligence (AI). Several assessments
underpin this argument:• Hardware is a foundational element of the AI stack alongside data, algorithms, andtalent.1• Exponential increases in computational power have driven the last decade of progressin machine learning (ML).2• After decades leading the microelectronics industry, the United States will soon sourceroughly 90% of all high-volume, leading-edge integrated-circuit production fromcountries in East Asia.3 This means the United States is almost entirely reliant on foreignsources for production of the cutting-edge semiconductors critical for defense systems
and industry more broadly, leaving the U.S. supply chain vulnerable to disruption byforeign government action or natural disaster.
• Specialized hardware, novel packaging techniques such as heterogeneous integrationand 3D stacking, and new types of devices will drive future AI developments as
traditional architectures of silicon-based chipsets encounter diminishing marginal
performance improvements.4“• Demand for trusted microelectronics will only grow as the military and Intelligence
Community (IC) continue to incorporate AI into mission-critical systems.5“... the United States is almost
entirely reliant on foreign sources
for production of the cuttingedge semiconductors critical for
defense systems and industry
more broadly, leaving the U.S.
supply chain vulnerable todisruption by foreign government
action or natural disaster.”p
213

MICROELECTRONICS

U.S. leadership in semiconductors has long been taken for granted based on America’s
advantage as a pioneer of the microelectronics industry. Gradually, however, the UnitedStates has been losing its edge. Although American universities and firms remain global
leaders in the key areas of semiconductor R&D and chip design, the semiconductor
industry is now highly globalized and competitive. Taiwan Semiconductor Manufacturing
Corporation (TSMC) leads the world in semiconductor contract manufacturing, andSamsung in South Korea is also producing state-of-the-art logic chips.6 TSMC also leads inthe production of ARM-based chips, which is becoming the predominant chip architecture
for mobile devices, servers, and other key applications of emerging technologies.7 In
a bid to catch up and achieve chip self-sufficiency, China is pursuing unprecedented
state-funded efforts to forge a world-leading semiconductor industry by 2030. Although
China is behind firms headquartered in Taiwan, South Korea, and the U.S. in terms of chip
manufacturing, it is advancing quickly.8 Meanwhile, Intel, the leading U.S. manufacturer,
remains competitive in chip design but has faced manufacturing setbacks for leading-edge
chips and may fall further behind its rivals in Taiwan and South Korea. Current projections
put the firm two generations or more behind the cutting-edge node by 2022.9 These andother concerning trends indicate that America’s leadership in microelectronics is eroding,especially in manufacturing, assembly, testing, and packaging.10
The dependency of the United States on semiconductor imports, particularly from Taiwan,
creates a strategic vulnerability for both its economy and military to adverse foreign
government action, natural disaster, and other events that can disrupt the supply chains forelectronics. Despite tremendous expertise in microelectronics research, development, andinnovation across the country, the United States is constrained by a lack of domesticallylocated semiconductor fabrication facilities, especially for state-of-the-art semiconductors.
If current trends continue, the United States will soon be unable to catch up in fabrication,and could eventually also be outpaced in microelectronics design. If a potential adversary
bests the United States in semiconductors over the long term or suddenly cuts off U.S.
access to cutting-edge chips entirely, it could gain the upper hand in every domain of
warfare. Focusing the efforts of the U.S. Government, industry, and academia to develop
domestic microelectronics fabrication facilities will reduce dependence on imports,
preserve leadership in technological innovation, support job creation, improve national
security and balance of trade, and enhance the technological superiority and readiness ofthe military, which is an important consumer of advanced microelectronics.

p
214

“

CHAPTER 13“Despite tremendous
expertise in microelectronics
research, development, andinnovation across the country,
the United States is limited bya lack of domesticallylocated semiconductor
fabrication facilities ...”

S tate - of-the - A r t
S emiconductor

State-of-the-Art Semiconductor Manufacturing by Firm: 2014-2024

M anufacturing by
F irm: 2 014 -2 0 24.

Node size for 2021-2024 are projections and reflect firm roadmaps
Node size reflects estimated first year of mass production
No roadmap displayed beyond 2021 for SMIC due to export control restrictions on materials currently
required for production beyond 14 nm
p215

MICROELECTRONICS

To regain U.S. leadership in microelectronics, the Executive Branch should finalize andimplement a national microelectronics leadership strategy. Additionally, Congress should
create a 40% refundable tax credit for domestic fabrication investments by firms from theUnited States and its allies and appropriate an additional $12 billion over the next five years
for microelectronics research, development, and infrastructure. Together these efforts
will enable the U.S. government, private sector, and academia to rise to the challenge of
rebuilding U.S. semiconductor superiority.
Objective: Stay two generations ahead of China in state-of-the-art microelectronics andmaintain multiple sources of cutting-edge microelectronics fabrication in the United States.
The United States should focus the attention and resources necessary for long-termcompetition in microelectronics by adopting an overarching national objective: to stay two
generations ahead of potential adversaries in state-of-the-art microelectronics while also
maintaining multiple sources of cutting-edge microelectronics fabrication inside the UnitedStates.11 While the United States has historically led China by at least two generations insemiconductor design and fabrication, this has not been an explicit policy goal. And while
China has not been able to surpass the United States, other nations such as Taiwan andSouth Korea now clearly lead the U.S. in state-of-the-art semiconductor manufacturing.
This leaves the U.S. reliant on foreign sources for critical inputs to defense systems andU.S. industry more broadly. Yet the United States retains a strong position in segments ofthe global value chain for semiconductors, such as design, electronic design automation
tools, and semiconductor manufacturing equipment (SME).12 Therefore, an objective torebuild microelectronics leadership should be stated plainly to concentrate national support
across government, industry, and academia on regaining leadership in sectors such as
semiconductor fabrication where the United States has fallen behind and also to trackprogress over time against a clear yardstick. To achieve this objective, the Commission
recommends focusing action along three fronts:• Implementing a national microelectronics strategy;• Revitalizing domestic microelectronics fabrication by incentivizing multiple cuttingedge domestic fabrication facilities; and• Ramping up microelectronics research.
In addition to these efforts to promote U.S. microelectronics leadership, the UnitedStates and its allies should utilize targeted export controls on high-end semiconductor
manufacturing equipment, described in Chapter 14 of this report, to protect existing
technical advantages and slow the advancement of China’s semiconductor industry.

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CHAPTER 13

U.S . leadership in
microelectronics isessential to overall
U.S . leadershipin AI.

Recommendation

Implement the National Microelectronics Strategy. The United States lacks a national
microelectronics strategy to coordinate semiconductor policy, funding, and incentives
within the Executive Branch and externally with industry and academia. A truly national
strategy would build on this Commission’s work as well as previous studies conductedby the United States government or on its behalf. It would also integrate the disparate
approaches of the Departments of State, Defense, Energy, Commerce, and Treasury, andother relevant agencies, to promote domestic R&D and semiconductor manufacturingexpertise while preventing the illicit transfer of technology to competitors. Finally, it would
be updated on a consistent basis to foster a coordinated approach and adapt to shifting
challenges to microelectronics innovation, competitiveness, and supply chain integrity.
In line with the Commission’s recommendations, the Fiscal Year 2021 National Defense
Authorization Act (NDAA) creates a subcommittee of the National Science and Technology
Council (NSTC), consisting of senior government officials, to develop a National Strategy
on Microelectronics Research and oversee its implementation.13 However, for this key
effort to be successful, it should be prioritized by the White House by requiring the NSTC
subcommittee to submit the National Microelectronics Strategy to the President within 270
days.

Recommendationp
217

Revitalize domestic microelectronics fabrication. The Commission concludes that the UnitedStates is overly dependent upon globally diversified supply chains for microelectronics,
including imports from potential adversaries. Furthermore, as a result of gaps in the U.S.
industrial base, the risks are increasing that the United States could lose access to trusted,assured, and state-of-the-art semiconductors for national security use cases. Despite
these concerns, the Commission has been encouraged by a number of developments
over the past year to revitalize the domestic fabrication of state-of-the-art microelectronics.

“

MICROELECTRONICS“... the United States could lose
access to trusted, assured, andstate-of-the-art semiconductors
for national security use cases.”

Examples include TSMC’s decision to develop an advanced facility in the United Statesand Intel’s publicly stated interest in working with the United States government to develop
a commercial U.S. foundry.14 However, these are only initial steps, and more must be doneby the U.S. government to reach an end state where multiple firms are fabricating stateof-the-art chips domestically. Without several U.S.-based fabrication facilities, both U.S.
industry and U.S. national security face risks from competitive pressures and supply chain
shortages. The most significant recent development has been the inclusion of several
semiconductor-related provisions from the “CHIPS for America Act” in the Fiscal Year
2021 National Defense Authorization Act (NDAA).15 However, these programs requiresufficient appropriations to succeed, and they did not receive appropriated funding in
Fiscal Year 2021, which leaves congressional priorities unclear. Further congressional
action to establish refundable investment tax credits and set the conditions for the domestic
production of advanced microelectronics will be important to enable the United States toremain two generations ahead of China. Specifically, the U.S. government should:• Incentivize domestic leading-edge merchant fabrication through refundable
investment tax credits. Although introduced as part of the CHIPS for America Act,
Congress has not yet passed legislation establishing a 40% refundable investment tax
credit for semiconductor facilities and equipment.16 Existing U.S. incentives reduce thecost of foundry construction attributable to capital expenses, operating expenses, andtaxes by just 10% to 15%. A credit of this magnitude is needed to make the United States
a competitive market for semiconductor manufacturing, as other leading semiconductor
manufacturing nations such as South Korea, Taiwan, and Singapore offer 25% to 30%
cost reduction, roughly double what the United States currently offers.17 This gap inincentives is one driving factor behind the lack of an advanced logic merchant foundryin the United States. Closing the incentive gap and broadening it to include companies
from allied countries will incentivize U.S. firms to construct facilities domestically while
also attracting foreign firms such as TSMC and Samsung. Additionally, increasing
demand in the United States for high-end SME will create new business opportunities
for SME manufacturers from allied countries, particularly Japan and the Netherlands,
which could increase their governments’ willingness to align their export control
policies with strict U.S. policies prohibiting the export of such equipment to China.18

p
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“

CHAPTER 13“... other leading semiconductor
manufacturing nations suchas South Korea, Taiwan,
and Singapore offer 25 to 30
percent cost reduction, roughly
double what the United Statescurrently offers.”

Recommendation

Double-down on federally funded microelectronics research. Each succeeding generationof chips using traditional architectures of silicon-based transistors faces diminishingmarginal gains to performance as they reach the limits imposed by the laws of physics.
As a result, the relative advantage the United States has enjoyed by staying roughly two
generations ahead of potential adversaries in the design phase of developing cuttingedge hardware could decrease over time as the gap between hardware generations
narrows. Therefore, the United States must look to heterogeneous integration and othernovel hardware improvements in the medium term to continue out-innovating competitors.
Over the longer term, the United States must also continue its portfolio approach to future
microelectronics pathways by investing in new materials and entirely new hardware
approaches, such as quantum and neuromorphic computing. Broad-based investments
and incentives will also be important to maintain leadership in other areas of U.S. strength
related to semiconductor manufacturing, including electronic design automation tools and
SME.
Four primary research arms of the United States government focused on both mediumand long-term microelectronics breakthroughs are the Department of Energy, Defense
Advanced Research Projects Agency (DARPA), National Science Foundation (NSF), andthe Department of Commerce, primarily through engagement with industry. Their suite ofexisting programs, such as DARPA’s Electronics Resurgence Initiative, is targeting theright research areas but must expand by an order of magnitude to achieve the necessary
breakthroughs and maintain U.S. competitiveness. Additional funding should support
not only research projects, but also the capital-intensive underlying infrastructure formicroelectronics development, including the National Semiconductor Technology Centerand advanced packaging prototyping activities authorized in the Fiscal Year 2021 NDAA. In
particular, advances in packaging will be critical to future improvements in semiconductorp219

MICROELECTRONICS

capabilities as firms reach physical limits for two-dimensional transistor density.19 The
government should:• Double down on federal research funding to lead the next generation of
microelectronics. The Commission recommends substantially increasing the UnitedStates government’s full range of research efforts focused on microelectronics.
Congress should appropriate an additional $1.1 billion for semiconductor research
and $1 billion for the Advanced Packaging National Manufacturing Program in Fiscal
Year 2022. Building on these investments, these funding levels should continue forfive years, for a total investment of roughly $12 billion. These amounts are consistent
with the funding levels introduced, but not yet appropriated, in the CHIPS for America
Act20 and the American Foundries Act of 2020.21 In line with the existing focus areasof these programs and the Commission’s prior recommendations, the funding should
be applied to developing infrastructure and pursuing breakthroughs in promising areas
such as next-generation tools beyond extreme ultraviolet lithography, 3D chip stacking,photonics, carbon nanotubes, gallium nitride transistors, domain-specific hardware
architectures, electronic design automation, and cryogenic computing.

““... advances in packaging willbe critical to future
improvements in semiconductor
capabilities as firms reachphysical limits for twodimensional transistor density.”p
220

CHAPTER 13

Chapter 13 - Endnotes
Dave Martinez, et al., Artificial Intelligence: Short History, Present Developments, and Future
Outlook, MIT at 27, n. 10 (Jan. 2019), https://www.ll.mit.edu/sites/default/files/publication/doc/201909/Artificial%20Intelligence%20Short%20History%2C%20Present%20Developments%2C%20and%20
Future%20Outlook%20-%20Final%20Report%20-%20Martinez.pdf (citing Andrew Moore, et al.,The AI Stack: A Blueprint for Developing and Deploying Artificial Intelligence, International Society
for Optics and Photonics, Ground/Air Multisensor Interoperability, Integration, and Networking for
Persistent ISR IX [2018]).

1

Recent machine learning (ML) breakthroughs have relied heavily on computing power, and theamount of compute used in the largest AI training runs has been increasing exponentially since 2012.
Girish Sastry, et al., Addendum: Compute Used in Older Headline Results, OpenAI (Nov. 7, 2019),
https://openai.com/blog/ai-and-compute/#addendum.

2

Michaela Platzer, et al., Semiconductors: U.S. Industry, Global Competition, and Federal Policy,
Congressional Research Service at 12 (Oct. 26, 2020), https://crsreports.congress.gov/product/pdf/R/
R46581.
3

4

Sara Hooker, The Hardware Lottery, arXiv (Sept. 21, 2020), https://arxiv.org/pdf/2009.06489.pdf.

Gaurav Batra, et al., Artificial Intelligence Hardware: New Opportunities for Semiconductor
Companies, McKinsey & Co. (Jan. 2, 2019), https://www.mckinsey.com/industries/semiconductors/
our-insights/artificial-intelligence-hardware-new-opportunities-for-semiconductor-companies.

5

Taiwan Semiconductor Manufacturing Corporation (TSMC) has already begun producing 5nm stateof-the-art logic chips and aims to produce 3nm chips by the end of 2021. Samsung is also producing
5nm chips. Intel does not anticipate producing 7nm chips in-house until at least 2022 and may
outsource manufacturing to TSMC. Firms in China are producing 12 nm chips. Richard Waters, Intel
Looks to New Chief’s Technical Skills to Plot Rebound, Financial Times (Jan. 14, 2021), https://www.
ft.com/content/51f63b07-aeb8-4961-9ce9-c1f7a4e326f0; Mark Lapedus, China Speeds Up Advanced
Chip Development, Semiconductor Engineering (June 22, 2020), https://semiengineering.com/chinaspeeds-up-advanced-chip-development/; 5nm Technology, TSMC (last accessed Jan. 16, 2021),
https://www.tsmc.com/english/dedicatedFoundry/technology/logic/l_5nm; Debby Wu, TSMC’s $28
Billion Spending Blitz Ignites a Global Chip Rally, Bloomberg (Jan. 14, 2021), https://www.bloomberg.
com/news/articles/2021-01-14/tsmc-profit-beats-expectations-as-chipmaker-widens-tech-lead; Anton
Shilov, Samsung Foundry Update: 5nm SoCs in Production, HPC Shipments to Expand in Q4, Tom’s
Hardware (Nov. 1, 2020), https://www.tomshardware.com/news/samsung-foundry-update-5nm-socsin-production-hpc-shipments-to-expand-in-q4.

6

ARM and TSMC Announce Multi-Year Agreement to Collaborate on 7nm FinFET Process Technology
for High-Performance Compute, Design & Reuse (March 15, 2016), https://www.design-reuse.com/
news/39433/arm-tsmc-7nm-finfet.html.

78Michaela D. Platzer, et al., Semiconductors: U.S. Industry, Global Competition, and Federal Policy,
Congressional Research Service at 2, 25, 27 (Oct. 26, 2020), https://crsreports.congress.gov/product/
pdf/R/R46581.

Ian King, Intel ‘Stunning Failure’ Heralds End of Era for U.S. Chip Sector, Bloomberg (July 24, 2020),
https://www.bloomberg.com/news/articles/2020-07-25/intel-stunning-failure-heralds-end-of-era-for-us-chip-sector.
9

p
221

MICROELECTRONICS

10
Michaela D. Platzer, et al., Semiconductors: U.S. Industry, Global Competition, and Federal Policy,
Congressional Research Service (Oct. 26, 2020), https://crsreports.congress.gov/product/pdf/R/R46581.
11
The Commission’s previous reports offered a range of initial recommendations to expand accessto trusted semiconductors, increase microelectronics R&D funding, control the export of high-end
semiconductor manufacturing equipment to adversaries, and reshore leading-edge fabrication facilities.
12
John VerWey, The Health and Competitiveness of the U.S. Semiconductor Manufacturing Equipment
Industry, U.S. International Trade Commission: Office of Industries Working Paper (July 1, 2019), http://dx.doi.org/10.2139/ssrn.3413951.

See Pub. L. 116-283, sec. 9906, William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021).

13

14
Stephen Nellis, Phoenix Okays Development Deal with TSMC for $12 Billion Chip Factory, Reuters (Nov.
18, 2020), https://www.reuters.com/article/us-tsmc-arizona/phoenix-okays-development-deal-with-tsmcfor-12-billion-chip-factory-idUSKBN27Y30E; Asa Fitch, et al., Trump and Chip Makers Including Intel Seek
Semiconductor Self-Sufficiency, Wall Street Journal (May 11, 2020), https://www.wsj.com/articles/trumpand-chip-makers-including-intel-seek-semiconductor-self-sufficiency-11589103002.

See Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for Fiscal
Year 2021, 134 Stat. 3388 (2021). These provisions authorize several programs the Commission has
previously identified as essential to U.S. microelectronics leadership. In particular, the provisions would
require drafting a National Microelectronics Leadership Strategy, establishing a National Semiconductor
Technology Center, and creating an incubator for semiconductor startup firms and an Advanced
Packaging National Manufacturing Institute, all of which align with previous recommendations from theCommission.

15

This incentive would reduce a semiconductor firm’s tax bill by 40% on semiconductor manufacturing
equipment and facilities through 2024, followed by reduced tax credit rates of 30% and 20% respectively,
through 2025 and 2026

16

Antonio Varas, et al., Government Incentives and US Competitiveness in Semiconductor Manufacturing,
BCG and SIA (Sept. 2020), https://web-assets.bcg.com/27/cf/9fa28eeb43649ef8674fe764726d/bcggovernment-incentives-and-us-competitiveness-in-semiconductor-manufacturing-sep-2020.pdf.

17

18

See Chapter 14 of this report for additional details on export controls on SME.

19
Heterogeneous Integration Roadmap: Chapter 1: HIR Overview and Executive Summary, IEEE
Electronics Packaging Society (Oct. 2019), https://eps.ieee.org/images/files/HIR_ 2019/HIR1_ch01_
overview.pdf.
20
See S. 3933 and H.R. 7178, Creating Helpful Incentives to Produce Semiconductors (CHIPS) for America
Act, 116th Congress (2020).
21

See S. 4130, American Foundries Act of 2020, 116th Congress (2020).

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CHAPTER 14

Chapter 14:
Technology Protectionp
223

TECHNOLOGY PROTECTION

Improving U.S. Technology
Protection Capabilities

Enhance
Regulatory
Capacity

Utilize Targeted
Export Controls

Strengthen
ResearchProtections

Increase
Investment
Screening
Disclosures

Preserve U.S.
Innovation
Advantagesp224

CHAPTER 14

America’s ability to out-innovate competitors is the dominantcomponent of any U.S. strategy for technology leadership.
Promoting research, entrepreneurship, and talent development
remain the key ingredients of success. However, as the marginof U.S. technological advantage narrows and foreign efforts toacquire American know-how and technology increase, the UnitedStates must also reexamine how it can protect ideas, hardware,companies, and its values.
The United States confronts sustained threats from state-directed technology transfer andtheft targeting artificial intelligence (AI) and other cutting-edge, dual-use technologies
and basic research. China poses the most significant challenge. The Chinese Communist
Party (CCP) has embarked on a multi-pronged campaign of licit and illicit technology
transfer to become a “science and technology superpower” by 2050.1 The campaigndeliberately targets U.S. critical sectors, companies, and research institutions.2 China’s
theft of U.S. technology—be it through circumventing export controls, commercial deals
with U.S. companies to access intellectual property (IP), or espionage—costs the UnitedStates $300 billion to $600 billion per year.3 And that only captures immediate losses, notthe ongoing damage to the U.S. economy over time. China simultaneously exploits open
research environments through cyber-enabled intrusion, talent recruitment programs, andmanipulated research partnerships.4 In effect, China is using American taxpayers’ dollars
to fund its military and economic modernization.

““China’s theft of U.S.
technology—be it throughcircumventing export controls,commercial deals with U.S.
companies to access intellectual
property, or espionage—costs
the United States $300-$600
billion per year.”p
225

TECHNOLOGY PROTECTION

Russia also poses a significant illicit technology transfer threat, particularly as it relates
to technologies with defense applications. Although the Russian government’s efforts tosteal U.S. technology and IP do not operate at the same scale of comparable CCP efforts,
Russia nonetheless is an aggressive and capable collector of technologies. It is likely
to pose continued technology transfer threats over the coming decade, utilizing existing
commercial and academic entities, as well as traditional and cyber espionage.5
Protect.

Modernizing Export Controls and Investment Screening.
How the United States designs policies to limit the movement of commercial goods orcapital in the interests of national security will be one of the defining challenges of the next
decade, as dual-use commercial technologies become increasingly important to national
security. Export controls can and should be utilized not only to prevent the transfer ofparticularly sensitive equipment to strategic competitors, but also to slow competitors’
efforts to develop indigenous industries in sensitive technologies with defense applications.

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CHAPTER 14“

If executed properly, export controls that slow competitors can sustain existing U.S.
defense advantages over long periods of time. For instance, U.S. export controls on jetengine technology have stymied Chinese government-led efforts to produce a modern jetengine domestically for use in military aircraft for nearly 30 years.6“How the United States designs
policies to limit the movement ofcommercial goods or capital in
the interests of national security
will be one of the defining
challenges of the next decade ...”
However, as currently designed and utilized, U.S. export controls and investment screening
procedures are imperfect instruments for the AI competition. As a policy matter, investment
screening and export controls were designed for a different era, when the distinction
between civil and military technologies was clearer and there was little overlap between
the economies of the United States and its competitors. Both conditions have changed.
AI is dual-use and the emerging technology economies of the United States and China
are deeply interconnected, which makes it extremely difficult to design controls that arefeasible, maximize strategic impact, and minimize economic costs. While these tradeoffs
are not new, they are becoming more extreme, as the dual-use nature of AI means manyof its individual components most critical to national security are also commonplace in thecommercial sector.

Meanwhile, U.S. regulatory capacity has not kept pace with technical developments,
as the Departments of Commerce, Treasury, and State all lack sufficient technical andanalytical capacity to effectively design and efficiently enforce technology protection
policies on dual-use emerging technologies. Congress has taken some important steps
in recent years to adapt technology protection regimes to challenges posed by emerging
technologies, most notably the Export Control Reform Act of 2018 (ECRA) and the Foreign
Investment Risk Review Modernization Act of 2018 (FIRRMA).7 However, more than two
years after their passage, implementation of key aspects of both laws remains unfinished,hindering enforcement and confounding the affected industries.8

p227

TECHNOLOGY PROTECTION

These conditions present policymakers with a difficult choice between under-protection,
which will give competitors unacceptable levels of access to sensitive technologies,
and over-protection, which has the potential to stifle innovation and harm overall U.S.
competitiveness. Effective controls must target choke points that impose significant
trickle-down strategic costs on competitors but minimal economic costs on U.S. industry.
But such choke points are increasingly elusive.
Clearly state overarching principles to guide future U.S. dual-use technology protection
policies. The United States must take a smarter and more predictable approach to applying
technology protection policies to AI. The government should state that future technology
protection policies will be guided by four overarching principles:

Recommendation• U.S. technology controls must not supplant investment and innovation.

“• U.S. strategies to promote and protect U.S. technology leadership must be integrated.
• The United States must be judicious in applying export controls to AI-related
technologies, targeting discrete choke points and coordinating policies with allies.
• The United States must broaden investment screening on AI-related technologies.

“The United States mustbe judicious in applying
export controls to AI-related
technologies, targeting discrete
chokepoints and coordinating
policies with allies.”
On a technical level, AI poses particular challenges to control regimes given its dualuse, widespread, and largely open-source nature. Moreover, it builds on a host ofother technologies. Given the ubiquitous nature of AI, export controls on AI algorithms
carry substantial risk—improperly defined controls could inadvertently restrict theexport of significant numbers of commercial products and cause substantial harmto the U.S. technology industry. While some AI algorithms are clearly candidates forexport controls, such as those meant for use in battlefield applications, such softwareis largely already controlled under the Commerce Control List.9 Data is also a potentialp228

CHAPTER 14

target for controls—especially sensitive bulk data—but effective data controls face similar
challenges to AI algorithms.10 Looking across the AI stack, the hardware component of the
AI stack contains the most viable targets for traditional export controls.
Recommendation

Build regulatory capacity and fully implement ECRA and FIRRMA. The United States mustalso take steps to improve its capacity to design and implement effective technology
protection policies. In the near term, the Departments of Commerce, Treasury, and State
must ensure they have sufficient quantities of technically proficient personnel focusedon technology protection policies and better utilize external advisory boards staffed withtechnical experts in designing policies. The Department of Commerce must also finalize
its initial list of “emerging” and “foundational” technologies that must be controlled, as
mandated by ECRA more than two years ago, and work to comprehensively adapt U.S.
export control lists to address modern technology-focused security challenges.11 Doingso is a critical step to implementing both ECRA and FIRRMA. Finally, departments
and agencies should consider efforts to expedite and automate export licensing andCommittee on Foreign Investment in the United States (CFIUS) filing proceedings, which
could improve the effectiveness and reduce the economic costs of these regimes.12

Recommendation

Require investors from U.S. competitors to disclose transactions in a broader set ofsensitive technologies to CFIUS. The United States must amend CFIUS’ authorities andprocedures to enable it to better address modern challenges associated with sensitive,
dual-use technologies. Specifically, it must enhance its ability to monitor investments fromcompetitors in critical U.S. technology industries to prevent theft of IP and ensure that
the United States retains control of sensitive technologies. U.S. competitors are investingheavily in U.S. AI firms. From 2010 to 2017, China-based investors poured more than
$1.3 billion into U.S. AI startups, and AI remains among the top technology areas forventure capital investment in the United States by China-based firms.13 However, the U.S.
government has limited insight into these transactions. CFIUS is responsible for screening
foreign investments for national security risks, but it only requires firms to disclose
investments when the U.S. firm produces an export-controlled good—which very few AI
firms do.14 Therefore, many firms based in the U.S. competitor countries that invest in U.S.
AI companies have no obligation to report their investments to CFIUS. While CFIUS has
broad authority to unwind such transactions, it currently has no visibility before they are
consummated—creating a significant technology transfer risk.
CFIUS should increase disclosure requirements for investments in sensitive technologies
by firms from China and Russia. Congress should mandate that all investments originatingfrom “countries of special concern,” to include China and Russia, in national security–
relevant applications of AI and other “sensitive technologies” as defined by CFIUS, must
be disclosed to allow CFIUS the opportunity to review them prior to the completion of thetransaction. This list of sensitive technologies should be distinct and broader than the list of“emerging” and “foundational” technologies required under ECRA and include industries
key to U.S. national security that face persistent threats from adversarial capital, specificallyp229

TECHNOLOGY PROTECTION

national security–relevant applications of AI, semiconductors, telecommunications
equipment, quantum computing, and biotechnology, as well as other sectors identified inMade in China 2025. De-linking investment screening from export controls acknowledges
that these two tools can and should be applied in different ways, permitting more expansive
investment screening while maintaining targeted export controls focused on choke points.
Limiting the scope of the mandatory filing requirements for this broader set of technologies
to firms only from select U.S. competitors would prevent over-regulation and preserve
the free flow of capital, increase insight into China’s and Russia’s investments in criticaltechnologies, deter state-sponsored IP theft, and preserve U.S. leadership in AI for national
security purposes.15““CFIUS should increase
disclosure requirements
for investments in sensitivetechnologies for firms from
China and Russia.”

Utilize targeted export controls on key semiconductor manufacturing equipment (SME).
Where possible, the United States should use export controls to prevent competitorsfrom obtaining AI capabilities that would grant them strategic or military advantages.
The primary U.S. export control target to constrain competitors’ AI capabilities should be
sophisticated SME necessary to manufacture high-end chips. SME is a critical choke point
and an attractive target for export controls for the following reasons:

Recommendation• Advanced AI is increasingly dependent on high-end computing capabilities16;• China relies on international firms for its supply of high-end semiconductors; and• SME manufacturing is specialized and dominated by the United States and its allies.

p
230

“

CHAPTER 14“The primary U.S. export control
target to constrain competitors’
AI capabilities should be
sophisticated semiconductor
manufacturing equipment (SME)
necessary to manufacture highend chips.”
China is the only U.S. competitor attempting to cultivate a domestic, cutting-edge
microelectronics fabrication industry capable of producing advanced chips at scale.
Slowing the growth of China’s high-end semiconductor manufacturing ability would set
back its attempts to build a cutting-edge microelectronics industry capable of fabricating
chips most useful for advanced applications of AI for defense. Coupled with the efforts
to promote U.S. semiconductor leadership outlined in Chapter 13 of this report, this willfurther the Commission’s proposed U.S. policy goal of remaining two generations aheadof China in cutting-edge microelectronics design and fabrication. However, controls on
general-purpose semiconductors are unlikely to be effective given the larger number ofcountries capable of producing such chips. If implemented unilaterally, such controls
could harm the U.S. semiconductor industry.

Recommendationp231

Align the export control policies of the United States, the Netherlands, and Japan regarding
SME. The sophisticated photolithography tools needed to produce chips at the 16nm
node and below, particularly extreme ultraviolet (EUV) and argon fluoride (ArF) immersion
lithography tools, are the most complex and expensive type of SME. These tools are evenmore specialized than SME writ large, and the United States, the Netherlands, and Japan
control the entire market.17 The Departments of State and Commerce should work withthe governments of the Netherlands and Japan to align the export licensing processesof all three countries regarding high-end SME, particularly EUV and ArF immersion
lithography equipment, toward a policy of presumptive denial of licenses for exports ofsuch equipment to China. This would slow China’s efforts to domestically produce 7nm or
5nm chips at scale and constrain China’s semiconductor production capability of chips
at any node at or below 16nm—which the Commission assesses to be most useful foradvanced AI applications—by limiting the capability of Chinese firms to repair or replace
existing equipment.18

TECHNOLOGY PROTECTION

Utilize targeted end-use export controls and reporting requirements to prevent use of highend U.S. AI chips in human rights violations. The United States must take steps to preventand deter U.S. firms from wittingly or unwittingly enabling uses of AI that violate humanrights. List-based controls are ill-suited for this task given the commercial nature of mostAI equipment, as the vast majority of its uses are legitimate. However, end-use and enduser export controls could be more effective. Although end-use controls are unlikely toprevent the transfer of strategic technologies to U.S. competitors determined to obtainthem, they could prevent or deter U.S. firms from allowing certain key pieces of equipment,
particularly high-end chips, to be utilized in malicious AI applications. Reporting revealingthat U.S.-made chips are powering a supercomputer in Xinjiang, China, used for mass
surveillance of Uyghur populations and that firms in China have filed patents for facialrecognition specifically targeting Uyghurs illustrates the need to more closely monitor how
high-end U.S. enabling hardware is utilized.19

Recommendation““The United States must takesteps to prevent and deter
U.S. firms from wittingly or
unwittingly enabling uses of AI
which violate human rights.”

The Department of Commerce should prohibit the export of specific, high-performing AI
chips for use in mass-surveillance applications, compel U.S. firms that export such chips
to certify that the buyer will not utilize them to facilitate human rights abuses, and requirethat firms submit quarterly reports to Commerce listing all such chip sales to China. This
would not constitute a licensing requirement that would introduce uncertainty and causedelays, but rather a self-certification and semi-regular report from industry. Such an action
would demonstrate the U.S. commitment to ethical and responsible uses of AI, promote
ethical behavior among U.S. firms, and make it harder for bad actors to utilize advanced
U.S. chips for nefarious purposes.20

p
232

“

CHAPTER 14“China’s campaign to exploit
U.S.-based research violates
the research community’s core
principles of integrity, openness,accountability, and fairness.”Strengthening Research Protection.

The U.S. research enterprise should be protected as a national asset. China’s campaign to
exploit U.S.-based research violates the research community’s core principles of integrity,
openness, accountability, and fairness.21 U.S. response measures to counter the actions ofChina’s government are nascent.22 There is a need for more technically versed intelligencecollection and analysis on threats to the science and technology sector and a need todisseminate that information more broadly.23 Government agencies, law enforcement, andresearch institutions need ready access to tools and resources to conduct nuanced risk
assessment and build transparency around specific threats and tactics. The governmentand research institutions share responsibility for protecting core values and countering
malicious activities. Responses should be coordinated with like-minded allies and partners
to reinforce norms around openness of fundamental research, research integrity, andprotection of IP.
Strengthening the integrity of the research process will support the foundations of open
research. However, if not approached thoughtfully, U.S. policy actions to counter technology
transfer could harm U.S. competitiveness and global scientific progress. Countering the
CCP’s actions does not require severing most ties between research communities in China
and the United States. The United States benefits from collaboration by staying connectedwith cutting-edge work in China and welcoming their PhD-level top talent24 that comes tostudy at U.S. universities and remains in the United States at rates of 85% to 90% after
graduating.25

Recommendationp
233

Build capacity to protect the integrity of the U.S. research environment. Congress should startby passing the Academic Research Protection Act (ARPA) and establishing a governmentsponsored center of excellence on research security.26 The ARPA legislation would createa dedicated National Commission on Research Protection, improve dissemination of
open-source intelligence relating to foreign threats, and facilitate the sharing of studies
and practices between government and research organizations.

TECHNOLOGY PROTECTION

Coordinate research protection efforts internationally with allies and partners. China’s
efforts to acquire foreign technology extend far beyond the United States.27 The Office of
Science and Technology Policy, the Department of State, and the Department of Justice
should coordinate with allies and partners to further information-sharing on detrimental
academic collaboration with entities affiliated with China’s People’s Liberation Army
(PLA) and develop multilateral responses to mitigate the harm from these actions. Such
diplomacy should seek to reinforce global norms around commitment to open fundamental
research, as formalized in the United States in National Security Decision Directive 189.28
The United States should strive to build a coalition committed to this principle and toresearch integrity, sidelining those who do not abide by the values that underpin innovationand global science cooperation.

“

Recommendation“The United States should strive
to build a coalition committed
to this principle and to research
integrity, sidelining those who
do not abide by the values that
underpin innovation and global
science cooperation.”
Bolster cybersecurity support for research institutions. Protection of research data andIP from cyber-enabled theft is perhaps the most important measure and the mosteasily achieved layer of security. This is particularly salient for AI, when theft of training
data or trained models essentially provides access to a final product. Federal grantmaking agencies should ease the ability of research institutions to maintain a baseline
level of cybersecurity by issuing clear guidance, establishing incentives, and sharing
state-of-the-art best practices and resources.

Recommendation

Agencies such as the Department of Homeland Security (DHS) and FBI should
increase support to information-sharing constructs and provide timely and actionable
alerts on cyber threats and intrusions.29 In addition, the government should broker
commercial cloud credits for universities to support secure data storage for researchgroups and laboratories conducting research known to be of high interest to foreign
adversaries.
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CHAPTER 14

Recommendation

Counter foreign talent recruitment programs. China’s national plan for AI development
directs use of foreign talent recruitment programs as a means to create a “high ground”
for China’s AI experts.30 These problematic programs have received increasing attention
in recent years. Rather than offering legitimate competition for scientific talent throughattractive job offers, many are constructed in a manner that contradicts U.S. norms ofresearch integrity, violates rules around disclosure, and creates vectors for technology
transfer.31
The programs often employ a model of “part-time” recruitment, in which participants retain
positions in the United States while accepting an affiliation with an institution in China.32
This often involves signing contracts that create conflicts through requirements to attributepatents to an institution in China, even if the research was conducted with U.S. funding.
Participants often train other talent recruitment program members and replicate U.S.funded work at an institution in China.
We commend recent action by Congress to limit the detrimental impact of these programsby mandating standardized disclosure requirements for federally funded research thatwill require comprehensive disclosure of conflicts of interest, conflicts of commitment,
and all outside and foreign support.33 This should be strengthened by a standardization
and unification of grant application and documentation processes in machine-readable
formats. Together, these measures would enable effective oversight, automated fraud““Disclosure and grantstandardization should becomplemented with mandated
and resourced compliance
operations at each researchfunding agency—creating alayer of accountability to enforce
disclosure policies and deterbad actors.”p
235

TECHNOLOGY PROTECTION

detection, and data sharing across the federal research funding agencies. Disclosure andgrant standardization should be complemented with mandated and resourced compliance
operations at each research funding agency—creating a layer of accountability to enforce
disclosure policies and deter bad actors.
Strengthen visa vetting to limit problematic research collaborations. Some U.S. universities
and researchers are unknowingly entering into collaborative research arrangements withresearchers from universities in China with close ties to the PLA and conducting researchthat directly contributes to China’s military and security capabilities.34 China’s militarycivilian fusion strategy and pursuit of technological leadership has been supported by apush from PLA-affiliated research institutions to send personnel abroad. Visiting scholars
or students have been found to downplay ties to the military or deliberately obscure
affiliation by using alternate names for their home institutions.35

Recommendation

The United States should guard against the entrance of researchers with problematic
affiliations through implementation of a special review process for visa applications fromadvanced-degree students and researchers with ties to research institutions affiliated withforeign military and intelligence organizations of designated countries of concern.36 Thisshould be accompanied by adequate resources to enable heightened review and pairedwith penalties that ban entry to visa applicants found to have intentionally not disclosed or
improperly disclosed their military and intelligence affiliations.

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Chapter 14 - Endnotes
Outline of the National Innovation-Driven Development Strategy, Central Committee of theCommunist Party of China and the PRC State Council (May 19, 2016) (translation by CSET on Dec. 11,2019), https://cset.georgetown.edu/research/outline-of-the-national-innovation-driven-developmentstrategy/.
1

Deputy Assistant Attorney General Adam S. Hickey of the National Security Division Delivers
Remarks at the Fifth National Conference on CFIUS and Team Telecom, U.S. Department of Justice(April 24, 2019), https://www.justice.gov/opa/speech/deputy-assistant-attorney-general-adam-shickey-national-security-division-delivers-0.

2

3
China Theft of Technology is Biggest Law Enforcement Threat to U.S., FBI Says, The Guardian (Feb.
6, 2020), https://www.theguardian.com/world/2020/feb/06/china-technology-theft-fbi-biggest-threat(quoting William Evanina, director of the National Counterintelligence and Security Center).

A recent JASON study on the issue found that “[a]ctions of the Chinese government and its
institutions that are not in accord with U.S. values of science ethics have raised concerns about
foreign influence in the U.S. academic sector . . . there are problems with respect to researchtransparency, lack of reciprocity in collaborations and consortia, and reporting of commitments andpotential conflicts of interest, related to these actions.” JASON, Fundamental Research Security,
MITRE Corporation at 39 (Dec. 2019), https://www.nsf.gov/news/special_reports/jasonse13curity/JSR19-2IFundamentalResearchSecurity_12062019FINAL.pdf.

4

In 2018 the U.S. National Counterintelligence and Security Center stated: “The threat to U.S.
technology from Russia will continue over the coming years as Moscow attempts to bolster an
economy struggling with endemic corruption, state control, and a loss of talent departing for jobs
abroad.” Foreign Economic Espionage in Cyberspace, National Counterintelligence and Security
Center at 8 (2018), https://s3-us-west-2.amazonaws.com/cyberscoop-media/wp-content/uploads/201
8/07/26114025/2018ForeignEconomic-Espionage-Pub_FINAL.pdf.

5

6
Robert Farley & J. Tyler Lovell, China’s Air Force Is Being Held Back by Its Terrible Jet Engines, The
National Interest (April 3, 2020) https://nationalinterest.org/blog/buzz/chinas-air-force-being-heldback-its-terrible-jet-engines-140252.

See Pub. L. 115-232, Title XVII, Subtitle B, 132 Stat. 1636, 2208, as amended by Pub. L. 116-6,
Division H, Title II, Section 205 Consolidated Appropriations Act, 2019, 133 Stat. 13, 476; Pub. L. 115232, Title XVII, Subtitle A, 132 Stat. 1636, 2174.

7

Of particular note, ECRA requires the Department of Commerce to identify “emerging andfoundational technologies that are essential to the national security of the United States” that are
not otherwise controlled, but to date Commerce has not identified a single technology under thisprovision. This has left gaps in the U.S. approach to protecting its advantages in high-tech sectors,
including AI, and created uncertainty for industry. See Pub. L. 115-232, Title XVII, Subtitle B, 132
Stat. 1636, 2208, as amended by Pub. L. 116-6, Division H, Title II, Section 205 Consolidated
Appropriations Act, 2019, 133 Stat. 13, 476.

89Carrick Flynn, Recommendations on Export Controls for Artificial Intelligence, Center for Security
and Emerging Technology (Feb. 6, 2020), https://cset.georgetown.edu/research/recommendationson-export-controls-for-artificial-intelligence/.
10
There is also room to work with allies and partners to create standards for securely transferring key
data sets and limiting their distribution to certain trusted nations; see Chapter 15 of this report for
additional details on this topic.

See Chris Darby, et al., Mitigating Economic Impacts of the COVID-19 Pandemic and Preserving
U.S. Strategic Competitiveness in Artificial Intelligence, NSCAI at 16 (May 19, 2020), https://www.
nscai.gov/white-papers/covid-19-white-papers/.
11

12

p
237

The Chapter 14 Blueprint for Action contains more details on this recommendation.

TECHNOLOGY PROTECTION

Chinese venture capital investment in the U.S. increased substantially after 2014 but has stalledsince 2018. Nevertheless, AI remains one of the top sectors for Chinese venture capital investment in
the U.S. Michael Brown & Pavneet Singh, China’s Technology Transfer Strategy, Defense Innovation
Unit Experimental (Jan. 2018), https://admin.govexec.com/media/diux_chinatechnologytransferstudy_
jan_ 2018_(1).pdf; Adam Lysenko, et al., Disruption: US-China Venture Capital in a New Era ofStrategic Competition, (Jan. 2020), https://publications-research.s3-us-west-2.amazonaws.com/RHG_
Disruption_US+China+VC_ January2020.pdf; Mercedes Ruehl, et al., Chinese State-Backed Funds
Invest in U.S. Tech Despite Washington Curbs, Financial Times (Dec. 2, 2020), https://www.ft.com/
content/745abeca-561d-484d-acd9-ad1caedf9e9e.

1314
As a result, CFIUS disclosure requirements disproportionately impact investments from U.S. allies;of the 94 mandatory CFIUS filings in 2019, 14 were from Japan, 12 were from Canada, 11 were fromthe U.K., and only three were from China. Annual Report to Congress, CFIUS at 33-36 (2019), https://home.treasury.gov/system/files/206/CFIUS-Public-Annual-Report-CY-2019.pdf.

This would require an amendment to CFIUS’ authorizing legislation, the draft text of which can befound in the Legislative Text Appendix of this report. Specifically, it would amend Section 721(a) of
the Defense Production Act of 1950 (as amended and codified in 50 USC § 4565[a]) and grant the
Department of the Treasury new authorities to alter mandatory filing requirements and define a list of“sensitive technologies” distinct from export control lists.

15

OpenAI estimates that since 2012, the amount of compute used in the largest AI training runs isdoubling every 3.4 months. See Dario Amodei & Danny Hernandez, AI and Compute, OpenAI (May16, 2018), https://openai.com/blog/ai-and-compute/.

16

17
The Dutch firm ASML has a monopoly on EUV lithography tools, which are the most advanced type,
and ArF immersion lithography tools are only produced by ASML and the Japanese firm Nikon.
18
The Wassenaar Arrangement lists lithography equipment capable of making chips with features of
45nm or below as a controlled item. However, because the Wassenaar Arrangement is not binding,states parties are not obligated to comply with this as a legal restriction. See List of Dual-Use Goods
and Technologies and Munitions List, Wassenaar Arrangement Secretariat at 72 (Dec. 2018), https://www.wassenaar.org/app/uploads/2019/consolidated/ WA-DOC-18-PUB-001-Public-Docs-Vol-II-2018List-of-DU-Goods-and-Technologies-and-Munitions-List-Dec-18-1.pdf.
19Paul Mozur & Don Clark, China’s Surveillance State Sucks Up Data. U.S. Tech Is Key to Sorting It,
New York Times (Nov. 24, 2020), https://www.nytimes.com/2020/11/22/technology/china-intel-nvidiaxinjiang.html; Leo Kelion, Huawei Patent Mentions Use of Uighur-spotting Tech, BBC (Jan. 13, 2021),
https://www.bbc.com/news/technology-55634388. Reporting indicates firms in China may be in theprocess of altering these patents to remove references to specific ethnic groups.
20This action would build on recent State Department guidance regarding best practices fortransactions linked to foreign government end-users for products or services with surveillancecapabilities. See U.S. Department of State Guidance on Implementing the “UN Guiding Principles”
for Transactions Linked to Foreign Government End-Users for Products or Services with Surveillance
Capabilities, U.S. Department of State (Sept. 30, 2020), https://www.state.gov/key-topics-bureau-ofdemocracy-human-rights-and-labor/due-diligence-guidance/.
21
JASON, Fundamental Research Security, MITRE Corporation (Dec. 2019), https://www.nsf.gov/
news/special_reports/jasonsecurity/JSR-19-2IFundamentalResearchSecurity_12062019FINAL.pdf.
22
Promising efforts have been initiated through the National Counterintelligence Task Force and the
Office of Science and Technology Policy’s Joint Committee on Research Environments, as well as
among universities, to build communities of interest to share best practices and conduct internal
audits around disclosure policies and cybersecurity. See National Science and Technology Council,
The White House (last accessed Jan. 1, 2021), https://www.whitehouse.gov/ostp/nstc/, and theAcademic Security and Counter Exploitation Program launched by the Texas A&M University System,
Academic Security and Counter Exploitation Program, Texas A&M University (last accessed Jan. 11,2021), https://asce.tamus.edu/.

Chapter 5 of this report recommends that the Intelligence Community (IC) should prioritize
and accelerate collection of scientific and technical intelligence to better understand adversarycapabilities and intentions.

23

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CHAPTER 14

Chapter 14 - Endnotes
The Commission supports measures to strengthen the ability of the United States to attract andretain top AI talent coming from China and elsewhere. See Chapter 10 of this report.

24

25
Remco Zwetsloot, U.S.-China STEM Talent “Decoupling,” Johns Hopkins Applied Physics
Laboratory at 13 (2020), https://www.jhuapl.edu/assessing-us-china-technology-connections/
dist/407b0211ec49299608551326041488d4.pdf.

See H.R. 8346, Academic Research Protection Act, 116th Congress (2020), https://www.congress.
gov/bill/116th-congress/house-bill/8346. The legislation sought to establish a National Commission on
Research Protection; establish an open-source intelligence clearinghouse relating to foreign threats
to academic research overseen by the Director of National Intelligence (DNI); improve guidance fromthe Departments of State and Commerce to ensure academic institutions are meeting export-control
responsibilities; and develop a Federal Bureau of Investigation (FBI) outreach strategy on threats tothe academic community.
26

Notably, two-thirds of overseas professional associations that transfer technology to China arelocated outside the United States, mainly distributed among U.S. allies and partners. Ryan Fedasiuk& Emily Weinstein, Overseas Professionals and Technology Transfer to China, Center for Security
and Emerging Technology at 11 (July 2020), https://cset.georgetown.edu/research/overseasprofessionals-and-technology-transfer-to-china/.

27

The directive defines fundamental research as “Basic and applied research in science andengineering, the results of which ordinarily are published and shared broadly within the scientificcommunity, as distinguished from proprietary research and from industrial development, design,
production, and product utilization, the results of which ordinarily are restricted for proprietary or
national security reasons.” National Policy on the Transfer of Scientific, Technical, and Engineering
Information, Executive Office of the President (Sept. 21, 1985), https://fas.org/irp/offdocs/nsdd/nsdd189.htm.

28

Such as the Research and Education Networks Information and Sharing Analysis Center (RENISAC). REN-ISAC (last accessed Jan. 2, 2021), https://www.ren-isac.net/.

29

30
William C. Hannas & Huey-meei Chang, China’s Access to Foreign AI Technology, Center for
Security and Emerging Technology at 9-10 (Sept. 2019), https://cset.georgetown.edu/wp-content/
uploads/CSET_China_ Access_To_Foreign_ AI_Technology.pdf.

p239

TECHNOLOGY PROTECTION

The Office of Science and Technology Policy defines foreign government talent recruitment
programs as “an effort directly or indirectly organized, managed, or funded by a foreign government
to recruit science and technology professionals or students (regardless of citizenship or national
origin).” Enhancing the Security and Integrity of America’s Research Enterprise, White House Office of
Science and Technology Policy at 18 (July 2020), https://trumpwhitehouse.archives.gov/wp-content/
uploads/2020/07/Enhancing-the-Security-and-Integrity-of-Americas-Research-Enterprise.pdf.
3132David Zweig & Siqin Kang, America Challenges China’s National Talent Programs,
Center for Strategic and International Studies at 5 (May 2020), https://csis-website-prod.
s3.amazonaws.com/s3fs-public/publication/20505_zweig_ AmericaChallenges_v6_FINAL.
pdf?bTLm4WdtG93lAVmxLdlWsgkgeNQDQUAv.

See Pub. L. 116-283, sec. 223, William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021).

33

34
Glen Tiffert, Global Engagement: Rethinking Risk in the Research Enterprise, The Hoover Institution(July 2020), https://www.hoover.org/sites/default/files/research/docs/tiffert_globalengagement_
full_0818.pdf. A subsequent study of a larger database of research papers conducted by Nature
identified more than 12,000 papers from the years 2015 to 2019 that were co-authored by researchers
in the U.S. and at one of China’s “Seven Sons” universities. Furthermore, Nature found that “among
those, 499 authors had a dual affiliation with a U.S. institution and a Seven Sons university and werelisted on papers declaring grant funding from the NIH or the U.S. National Science Foundation.” Nidhi
Subbaraman, US Investigations of Chinese Scientists Expand Focus to Military Ties, Nature (Sept. 9,2020), https://www.nature.com/articles/d41586-020-02515-x.

Alex Joske, Picking Flowers, Making Honey: The Chinese Military’s Collaboration with Foreign
Universities, Australian Strategic Policy Institute (Oct. 2018), https://www.aspi.org.au/report/pickingflowers-making-honey.

35

36
The Commission recommends this as an update to Presidential Proclamation 10043 that suspends
F or J visas to study or conduct research for Chinese nationals affiliated with the Chinese government
military-civil fusion strategy. 85 Fed. Reg. 34353, Suspension of Entry as Nonimmigrants of Certain
Students and Researchers from the People’s Republic of China, Executive Office of the President(May 29, 2020), https://www.federalregister.gov/documents/2020/06/04/2020-12217/suspension-ofentry-as-nonimmigrants-of-certain-students-and-researchers-from-the-peoples-republic.

p240

CHAPTER 15

Chapter 15:
A Favorable International
Technology Orderp
241

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

Coordination and Alignment
with Allies and Partners

Develop an
International
S&T Strategy

Launch an
International
Digital Democracy
Initiative
Implement a
Comprehensive
U.S. National Plan

Reorient the
Department ofState and U.S.
Foreign Policy

Build an Emerging
Technology
Coalition

Cultivate an
International
Emerging
Technology
Research Hubp242

CHAPTER 15

The United States cannot compete with and counter the global
technology ambitions of authoritarian rivals if it acts alone. Likeminded countries must work together to advance an internationalrules-based order, protect free and open societies, and unleash
economic innovation. The authoritarian challenge to the global
technology order encompasses five distinct but related elements:• A rising challenge to U.S. and Western technology firms for global market share,
impacting the prosperity and global economic position of the United States and its allies
and partners;• China’s increasing influence and strategic leverage over countries that utilize
technologies and infrastructure built and developed in China;• The prospect of authoritarian consolidation in states that gain easy access to digital
tools that can strengthen repressive rule;“• The prospect of democratic backsliding in states with governments that may be tempted
to utilize digital tools in ways that undermine liberal values; and• A threat to the cohesion of democratic allies as an influential bloc of states with thecapacity to shape global technology norms and standards.1“The United States mustpursue a comprehensive
strategy in close coordination
with our allies and partners for
AI innovation and adoption that
promotes values critical to free
and open societies.”p243

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

The United States must pursue a comprehensive strategy in close coordination with ourallies and partners for artificial intelligence (AI) innovation and adoption that promotes
values critical to free and open societies. Furthermore, the United States must collaborate
with its closest allies and partners to develop principles for employing AI tools ethicallyand responsibly, defend the integrity of international technical standards, promote digitalmarkets, leverage comparative expertise to develop privacy-preserving technologies,
and share practices and resources to defend against authoritarian attacks on digital
infrastructure and democratic values.
To achieve these goals, the Commission proposes that the White House request the
Department of State to lead an effort with and other key agencies to:
Develop and implement an International Science and Technology Strategy (ISTS) to help
coordinate AI and emerging-technology policies government-wide and with our closest
allies and partners; apply the tools of foreign assistance, technical expertise and guidance,and development finance; and foster collaborative R&D. The ISTS should serve as theinternational component of the National Technology Strategy (see Chapter 9 of this report).
The ISTS should be centered around four big initiatives:

Recommendation• Build an Emerging Technology Coalition of allies and partners to promote the design,development, and use of emerging technologies according to democratic norms
and values; coordinate policies and investments to counter the malign use of thesetechnologies by authoritarian regimes; and provide concrete, competitive alternatives tocounter the adoption of digital infrastructure made in China.
• As part of the Emerging Technology Coalition, launch an International Digital
Democracy Initiative with allies and partners to align international assistance efforts
to develop, promote, and fund the adoption of AI and associated technologies thatcomports with democratic values and ethical norms around openness, privacy, security,
and reliability.
• Implement a comprehensive U.S. national plan to support international technology
efforts around technical standards, foreign assistance, development finance, and export
controls.
• Enhance the United States’ position as an international emerging technology
research hub for collaborative R&D efforts by formalizing a partnership between
the U.S. National AI Research Institutes and multilateral initiatives like the Global
Partnership on AI (GPAI), creating a Multilateral AI Research Institute (MAIRI) in the
United States with key allies and partners, and catalyzing international collaboration andtalent exchanges.
Build an Emerging Technology Coalition. The United States should lead an Emerging
Technology Coalition (ETC) of like-minded nations either as part of a larger democracy
summit or as a stand-alone endeavor. The immediate step for the ETC should be toorganize its efforts to synchronize policies around the following seven critical areas:

Recommendationp244

CHAPTER 15• Developing and operationalizing standards and norms, in support of democraticvalues and the development of secure, reliable, and trusted technologies;• Promoting and facilitating coordinated and joint R&D on AI and digital
infrastructure that advances shared interests and benefits humanity;• Promoting democracy, human rights, and the rule of law through joint efforts tocounter censorship, malign information operations, human trafficking, and illiberal usesof surveillance technologies;• Exploring ways to facilitate data-sharing among allies and partners through enabling
agreements, common data archival procedures, cooperative investments in privacyenhancing technologies, and addressing legal and regulatory barriers;• Promoting and protecting innovation, particularly through export controls, investment
screening, supply chain assurance, emerging technology investment, trade policy,
research and cyber protections, and intellectual property alignment;• Developing AI-related talent, by analyzing labor market challenges, harmonizing
skills and certification requirements, and increasing talent exchanges, joint training, andworkforce development initiatives;• Launching the International Digital Democracy Initiative.2
Recommendation

ID DI S trategy.

p245

Launch an International Digital Democracy Initiative (IDDI). As part of the ETC, the UnitedStates, with its allies and partners, should launch an IDDI to align international assistanceefforts to develop, promote, and fund the adoption of AI and associated technologies thatcomport with democratic values and ethical norms around openness, privacy, security,
and reliability.

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

The IDDI will be critical for enabling nations around the world to adopt secure, trusted, andopen digital ecosystems,3 empowering communities to use AI and digital technologies inways that strengthen democracies, promote sustainable development, and advance sharedvalues like privacy, human rights, and the rule of law. IDDI further provides an opportunity
for the United States and like-minded allies and partners to counter authoritarian usesof AI, particularly by providing alternatives to digital infrastructure projects that are used
for illiberal ends, endanger the social cohesion among and between democracies, andthreaten collective security.4 As international digital and telecommunications infrastructure
investment needs continue to grow5 and China continues to use digital development toexport authoritarianism and expand influence, the United States and its allies and partners
must join forces to coordinate a strategy that maximizes the impact of government assistanceefforts and also catalyzes private-sector investment to address shared challenges.

““... the United States and its
allies and partners must join
forces to coordinate a strategy
that maximizes the impact ofgovernment assistance efforts
and also catalyzes private sector
investment to address sharedchallenges.”

Implement a comprehensive U.S. national plan to support international technology efforts.
The ISTS should include an integrated government-wide plan for using and bolstering
the tools of U.S. foreign policy—including technical and foreign assistance, development
financing, and export controls—to advance the ETC, the IDDI, and stand-alone projects.
As demonstrated below, the plan should include methods to shape international technicalstandards; coordinate and expand programs of the Department of State, the UnitedStates Agency for International Development, the U.S. International Development FinanceCorporation, and other federal agencies; and use targeted export controls to preserve key
U.S. and allied technical advantages and also further transparency and accountability. It
will require significant, dedicated appropriations to achieve meaningful results.6

Recommendationp246

CHAPTER 15

U.S . National
Plan to Suppor t
International
Technology Ef for ts.

Recommendationp
247

Enhance the United States’ position as an international emerging technology research hub.
The United States must maintain its leadership in international R&D by further establishingitself as a hub of international research into and involving emerging technologies to foster
AI collaboration and coordination with key allies and partners. These efforts will facilitate
critical support to the ETC and IDDI by developing digital technologies and best practices
that comport with democratic values; enhance U.S. contributions to existing and futureinternational efforts like GPAI; and provide avenues for the United States and allies—
particularly European allies—to pool resources to address commercial gaps in R&D and“A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R“The United States must maintainits leadership in internationalR&D by further establishingitself as a hub of international
research into and involving
emerging technologies to foster
AI collaboration and coordination
with key allies and partners.”

overcome challenges to collaboration around cross-border data-sharing. Making theUnited States an international emerging technology research hub has three components:
C omponents ofInternational Digital
Research Hub.

• First, the United States should provide formal research support to key international
efforts such as GPAI and the Organisation for Economic Co-operation andDevelopment,7 particularly through the National Science Foundation (NSF)’s National
AI Research Institutes.8 The important research undertaken by the National AI
Research Institutes—run by the NSF and other U.S. agencies—and by other UnitedStates departments and agencies is an incredible resource that should support theseinternational efforts and advance AI and digital goals of the U.S. and like-minded
partners.

p248

CHAPTER 15• Second, the United States should work with key allies and partners to establish theMultilateral AI Research Institute (MAIRI). MAIRI will facilitate joint efforts to develop
technologies that advance responsible, human-centric, and privacy-preserving AI/
machine learning (ML) that better societies and allow allies to pool their talents andresources. It will provide a model for equitable, multilateral research, facilitate AI R&D
that builds on like-minded countries’ strengths, and foster a global AI workforce for the
next generation. MAIRI will be key to a U.S.-led effort to promote values of free and open
societies, win the global technology competition, unleash AI innovation and economic
prosperity, and develop AI applications that benefit humanity. MAIRI members willchampion agreed-upon research integrity principles, leverage trusted infrastructure
and research resources, and seek to be a part of a federated network of global research
institutes. NSF should be the anchor partner, but MAIRI should be structured to enable
participation of other federal agencies, like the Departments of State and Energy.9 The
United States should fund the initial startup costs, including acquisition of MAIRI’s
physical center located in the United States.
• Third, the United States should leverage existing O and J visa programs to facilitate
foreign researchers’ involvement in joint projects. Sustained, strong collaboration
between the United States and allies and partners is critical to winning this technocompetition and unleashing innovation and entrepreneurship across like-mindedcountries. There is no substitute for shoulder-to-shoulder research for building
relationships, exchanging ideas and expertise, and sparking future collaboration.10
Recommendation

Reorient U.S. foreign policy and the Department of State for great power competition in
the digital age. New outward-facing digital foreign policy initiatives are only part of theequation for ensuring the long-term success of global technology policy. The United States
must make inward-focused reforms to the Department of State as well. There is currently
no clear lead for emerging technology policy or diplomacy within the State Department,
which hinders the Department’s ability to make strategic technology policy decisions. It
also creates confusion for allies and partners, who regularly express uncertainty regarding
which senior official should be their primary point of contact for issues related to key topics
such as AI, 5G, quantum computing, biotechnology, or new emerging technologies.
Competitive diplomacy in AI and emerging technology arenas is a strategic imperativein an era of great power competition and necessitates an intensified reorientation of the
Department of State. The United States must redesign the internal structure, focus, andculture of the State Department to adapt U.S. diplomacy for the digital age and empower
diplomats to advance American interests at the intersection of technology, security,commerce, and human rights. Supporting these efforts and succeeding in U.S. diplomacy
will require targeted appropriations from Congress.
The Commission recommends the following immediate actions to reorient U.S. diplomacy:• First, the Deputy Secretary of State for Management and Resources (D/MR)
should have responsibility to prioritize reorienting and reorganizing the Department
for technology diplomacy. Past administrations have used the D/MR position to leadon strategic priorities and ensure execution. The D/MR should provide direction for
immediate and long-term planning around technology diplomacy, including policy
development, coordination, and resourcing. The D/MR should also have a lead role inoversight and implementation of the ISTS.

p
249

“

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R“The United States must redesign
the internal structure, focus, andculture of the State Department
to adapt U.S. diplomacy for thedigital age ...”• Second, the State Department should expedite and prioritize efforts to staff,
resource, and build out the newly created Bureau of Cyberspace Security andEmerging Technologies (CSET Bureau). The CSET Bureau, approved in early January
2021, would be led by an official with the title of Ambassador-at-Large and Coordinator.
The bureau would have a critical role as the focal point for U.S. diplomatic efforts
around security challenges associated with emerging technologies and would provide
an accountable home for AI advocacy within the Department.11 The Department,with congressional support, must ensure the CSET Bureau is adequately staffed andresourced. Quickly standing up CSET is critical to building the Department’s technology
diplomacy capacity, improving technology policy coordination across the Department,
and regularly elevating technology issues to the attention of senior leaders. The
Department should assess where CSET should be placed to best achieve thoseobjectives but must ensure its creation is not further delayed.
• Third, the State Department should enhance its presence in foreign and U.S.
technology hubs with a cadre of dedicated technology officers at U.S. missions tostrengthen diplomatic advocacy, improve technology scouting, and inform policy andforeign assistance.
• Fourth, AI-related technology modules should be incorporated into Foreign
Service Institute training courses at multiple levels to ensure U.S. diplomats areequipped to lead in an environment being transformed by emerging technology.
• Fifth, Congress must appropriate funds necessary for urgent State Department
needs both to augment the U.S. diplomatic corps and to support critical State
Department programs focused on AI and emerging technologies to advance U.S.
interests.
These steps are essential, but not sufficient, to further U.S. interests in tech diplomacy.
Eventually, the D/MR role should transition into a permanent Under Secretary for Science,
Research and Technology (State/Q). State/Q would lead a reorganization of the Department,
combining offices and bureaus to further a robust, coordinated approach to science andtechnology diplomacy and foreign assistance in the context of great power competition.12p250

CHAPTER 15

Chapter 15 - Endnotes
The threat to allied cohesion also extends to the military realm, insofar as building divergent orincompatible digital systems poses challenges for interoperability or creates risks for U.S. forces
operating in allied countries. See Daniel Kliman, Why the United States Needs a Digital Development
Fund, Center for a New American Security at 2 (Oct. 10, 2019), https://www.cnas.org/publications/commentary/why-the-united-states-needs-a-digital-development-fund (“Over the long term, China’s
digital investments could render some developing countries off-limits to U.S. forces, constricting thegeography of American military access.”).

1

Detail on these critical areas can be found in the Chapter 15 Blueprint for Action andits associatedAnnex.

2

USAID’s Digital Strategy defines the “digital ecosystem” as the “stakeholders, systems, andenabling environments that together empower people and communities to use digital technology togain access to services, engage with each other, or pursue economic opportunities.” This includes“a sound enabling environment and policy commitment; robust and resilient digital infrastructure;capable digital service providers and workforce; and, ultimately, empowered end-users of digitallyenabled services.” Digital Strategy 2020-2024, USAID at 4 (June 2020), https://www.usaid.gov/usaiddigital-strategy.

3

The Chinese government’s global infrastructure projects and its widespread state influence within
its private sector have enabled Chinese firms to provide surveillance and smart-city technologies tohundreds of cities globally, particularly in developing countries, bolstering autocratic regimes andenabling Chinese geopolitical coercion and government data collection. See, e.g., Hugh Harsono,
China’s Surveillance Technology is Keeping Tabs on Populations Around the World, The Diplomat(June 18, 2020), https://thediplomat.com/2020/06/chinas-surveillance-technology-is-keeping-tabson-populations-around-the-world/; Testimony of Steven Feldstein before the U.S.-China Economic andSecurity Review Commission, Hearing on China’s Strategic Aims in Africa (May 8, 2020), https://www.
uscc.gov/sites/default/files/Feldstein_Testimony.pdf.

4

To support the G20, the Global Infrastructure Hub has forecasted global telecommunications
infrastructure investment needs at $8.9 trillion over the next approximately 20 years, with current
trends falling short of these needs by $1 trillion. Forecasting Infrastructure Investment Needs andGaps, Global Infrastructure Hub (last accessed Jan. 13, 2021), https://outlook.gihub.org/.

5

p
251

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

Detailed recommendations for U.S. agencies and Congress can be found in the Chapter 15 Blueprint
for Action.

6

GPAI was launched in 2020 to “foster responsible development of AI grounded in these principles
of human rights, inclusion, diversity, innovation and economic growth.” Current members include
Australia, Brazil, Canada, the European Union, France, Germany, India, Italy, Japan, Mexico, theNetherlands, New Zealand, Poland, Singapore, Slovenia, South Korea, Spain, the United Kingdom,
and the United States, with the OECD and UNESCO as Permanent Observers. GPAI bridges “thegap between theory and practice,” particularly through research and technical expertise shared via
multi-stakeholder working groups. About GPAI, GPAI (last accessed Jan. 6, 2020), https://www.gpai.
ai/about/; UNESCO Joins Global Partnership on Artificial Intelligence as Observer, UNESCO (Dec. 10,2020), https://en.unesco.org/news/unesco-joins-global-partnership-artificial-intelligence-observer.

7

8

Artificial Intelligence at NSF, NSF (Aug. 26, 2020), https://www.nsf.gov/cise/ai.jsp.

For example, the Department of Energy may provide critical expertise on undertaking appliedresearch with industry or through its national laboratories, particularly on high-performance andquantum computing, while the Department of State can provide foreign policy expertise and support
initiatives on data-sharing and AI research clouds with allies and partners.

910
Detailed recommendations for each of these components can be found in the Chapter 15 Blueprint
for Action.

Secretary Pompeo Approves New Cyberspace Security and Emerging Technologies Bureau, U.S.
Department of State (Jan. 7, 2021), https://2017-2021.state.gov/secretary-pompeo-approves-newcyberspace-security-and-emerging-technologies-bureau//index.html.

11

12
These components of the State Department should include key functions of the CSET Bureau; the
Bureau of Oceans, Environment and Science; the Office of the Science and Technology Adviser; theCoordinator for Cyber Issues; and the Center for Analytics.

p252

CHAPTER 16

Chapter 16:
Associated Technologiesp253

A S S O C I AT E D T E C H N O L O G I E S

AI and Beyond: Identifying and Prioritizing
Critical Emerging Technologies

Artificial
Intelligence
Semiconductors
Biotechnology
Quantum
Computing
Robotics
and Autonomy
5G and Advanced
Networking
Advanced
Manufacturing
EnergySystemsp254

CHAPTER 16

The Commission’s work ends where it began, with the conclusionthat artificial intelligence (AI) will transform virtually every aspect of
our existence. However, leadership in AI is not an end unto itself; itis a necessary, but not sufficient, condition for the overarching goalof preserving U.S. leadership in technology. That reality presents achallenge for U.S. strategy: how to prioritize investments in AI andother key emerging technologies and support specific projects
that will build on and amplify cross-technology strengths. The
United States must view its efforts to lead in AI through the broader
lens of competition across a range of emerging technologies, and,
therefore, also support a comprehensive strategy to sustain U.S.
leadership in key associated technologies.

““The United States must viewits efforts to lead in AI throughthe broader lens of competition
across a range of emerging
technologies ...”
Leadership in AI relies on and drives leadership across a suite of emerging technologies.
AI sits at the center of the constellation of emerging technologies, enabling some and being
enabled by others.1 For instance, 5G and quantum computing are poised to enable new
growth in AI capabilities, while AI stands to transform the biological sciences, producing
significant technological breakthroughs and turning the biotechnology sector into one ofthe primary drivers of overall economic competitiveness.2

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A S S O C I AT E D T E C H N O L O G I E S

AI is at the center of a constellation of emerging technologies, enabling some andenabled by others. The United States Government must view its efforts to lead in AI
as part of a broader strategy to compete in each of these technologies:

The United States
Government must:
Define and prioritize the key emerging
technologies needed to ensure U.S.
national competitiveness, including U.S.
leadership in the following technologies
and platforms:

AssociatedTechnologies.

• Advanced biofabrication capabilities• Quantum chip fabrication• 5G spectrum sharing• Robotics software• Additive manufacturing• Energy storage technologies

China is pursuing a comprehensive technology leadership strategy. China’s strategic
investments in key sectors through its Made in China 2025 initiative threaten U.S.
technological prowess, economic prosperity, and national security.3 In addition toinvestments in AI, China is seeking to become a world leader in quantum, 5G, andbiotech, among other areas, and sees its strategies to lead in AI and each of these othertechnologies as mutually reinforcing. It has made clear which technologies it views as key
national priorities and is investing heavily in a wide range of sectors that it assesses are
essential to overall technical leadership.
The United States has neither identified, nor prioritized leadership in, the technologies thatare central to national competitiveness. The United States must develop a unified list of
the technologies that will underpin national competitiveness in the 21st century because
the first-mover advantage of developing and deploying technologies like microelectronics,
biotechnology, and quantum computing will make it difficult for the United States to catchup to China. The lack of such a list results in disparate funding and policy approaches totechnology protection and promotion across the U.S. government. The absence of clear
priorities also makes it more difficult to effectively marshal private-sector investment in
key technologies. In critical sectors with strong network effects like telecommunications,a winner-take-all dynamic raises the stakes even further for rapidly developing a leading
technology platform.4

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Ensuring U.S. leadership in the manufacturing of key emerging technology platforms
will be an essential component of national competitiveness. Identifying and supporting
research in priority technologies is a necessary but insufficient step to maintain national
competitiveness in emerging technologies. The United States will also have to invest in the
production of strategic physical elements of these technologies to create game-changing
platforms, maximize U.S. competitiveness, and reduce dependencies that create national
security vulnerabilities. Such investments are often expensive, but a strategic approach
does not require manufacturing every advanced component domestically and will pay
tremendous long-term dividends. The need to support advanced manufacturing applies
to nearly every key emerging technology sector, including semiconductors, quantum
computing, biotechnology, telecommunications equipment, and others, and is reflected in
the following recommendations.
Technology leadership will require major new investments in underlying digital infrastructure.
It is impossible to ignore the state of America’s underlying digital infrastructure when
considering a strategy to preserve overall U.S. leadership in technology. The sophistication
and reach of the core U.S. digital infrastructure that underpins connectivity, namely
high-speed internet and telecommunications networks, significantly lag behind those ofmany other developed nations.5 Additionally, U.S. physical infrastructure remains largely
disconnected; no U.S. cities are ranked among the top 10 in smart-city connectedness,and only one is in the top 30.6 Maximizing citizens’ access to the digital economy, ensuring
they have the requisite digital skills, and more closely connecting the physical and digitalworlds will be necessary to fuel future growth. Boosting the digital connectivity of physicalU.S. assets will not only enhance their effectiveness and reliability, but also generate newsources of data that enable novel, potentially revolutionary uses of AI in areas ranging
from energy grid management and urban planning to transportation. As the UnitedStates considers options to modernize U.S. physical infrastructure, prioritizing its digitalconnectivity will provide substantial long-term benefits and buttress U.S. technology
competitiveness and national security.
Identifying and Prioritizing Technologies Central to National Competitiveness.
While the United States should by no means adopt China’s centrally planned and statedirected economic model, it must start by developing better strategic planning, forecasting,and prioritization of emerging technologies to ensure long-term competitiveness. The
government should:
Recommendationp
257

Define and prioritize the key emerging technologies that are needed to ensure U.S. national
competitiveness. As part of its National Technology Strategy (see Chapter 9 of this report),
the White House should publish a list of critical and emerging technologies in which U.S.
leadership is essential. It should develop detailed implementation plans for each sector todetermine how the government should best work with industry to promote U.S. leadership,assess which specific subsectors are crucial to national security, and determine what

“

A S S O C I AT E D T E C H N O L O G I E S“As part of its National
Technology Strategy (seeChapter 9), the White Houseshould publish a list of criticaland emerging technologies
in which U.S. leadershipis essential.”

regulatory steps or incentives are necessary to create the required investment environment.
These plans should promote investment in specific platforms that will have a force multipliereffect on U.S. technology leadership, identify key choke points where competitors could
potentially be blocked with minimal impact on U.S. industry, and promote supply chain
resiliency. The creation and maintenance of such a list and the associated implementationplans will help produce a national consensus across government, industry, and academia
about which sectors are most important in the emerging techno-economic competition.
The result will be an important message to Congress regarding where the country mustprioritize and expend resources, as well as a powerful demand signal to industry.
Many similar lists exist throughout the government, but there has been no effort to unify
them into a single, authoritative document accompanied by a strategic vision and detailed
follow-through actions designed to ensure long-term U.S. leadership.7 However, thesignificant overlap between these lists demonstrates an emerging national consensus onwhich technologies are most critical to U.S. national competitiveness. Table 5 illustrates
the initial slate of technologies that the Commission recommends including as part of abroader technology leadership strategy, as well as whether or not those technologies havebeen included on select, existing U.S. government lists of critical technologies. As an initialstep, the Commission recommends that the White House designate these technologies ascritical through an Executive Order and direct Departments and Agencies to prioritize andcoordinate them accordingly.

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CHAPTER 16

U.S. Government Lists of Critical Technologies
NSCAIProposed
CriticalTechnology List

2018
National
DefenseStrategy

DoD Listof Critical
Emerging
Technologies

Commerce
ANPRM onEmerging
Technologies

PCAST Listof Industriesof the FutureS.3832 Endless
FrontierAct

WH NatStrategy
for
C&ET

Artificial
Intelligence
Biotechnology
Quantum
Computing
Semiconductors
and Advanced
HardwareAutonomy andRobotics
5G andAdvanced
Networking
Advanced
Manufacturing
EnergySystems

Actions to Promote Technologies and Platforms Essential
to U.S. Leadership and National Security.
After reaching consensus on the set of emerging technologies essential to overall U.S.
technology leadership, the Executive Branch should assess each sector and identify
specific platforms that meet the following criteria:• Have potential applications of strategic and national security importance;• Could have a significant impact on overall U.S. technical leadership andcompetitiveness, either alone or when combined with existing U.S. technical strengths;
and• Require government action to spur or protect its development.
Such platforms could require government support for several reasons. In some instances,a market failure may lead to underinvestment by the private sector in an area of strategic
importance to national security. In other instances, seizing a market opportunity may only
be possible if the federal government focuses the private sector, academia, and researchorganizations on a specific goal. The government must tailor its approach to the contextp259

A S S O C I AT E D T E C H N O L O G I E S

by increasing funding, implementing regulatory changes, or taking other steps aimed atpromoting innovation and protecting advantages that fit the circumstances.
The Commission has already presented recommendations to support U.S. leadershipin key technology platforms within several of the aforementioned strategic technologies.
For example, Chapter 11 of this report recommends establishing a National AI Research
Resource, which would create an essential platform to sustain and extend U.S. leadershipin AI. Additionally, in Chapter 13 of this report, the Commission provided a series ofrecommendations for promoting U.S. leadership in microelectronics, including specificactions to incentivize the construction of a leading-edge merchant fabrication facility
domestically.
The recommendations below build on the Commission’s previous work and provide
further actions the U.S. government could take to promote U.S. leadership in the key
associated technologies and platforms that the Commission assesses to be of greatest
strategic importance—specifically, biotechnology, quantum computing, 5G and advanced
networking, autonomy and robotics, advanced and additive manufacturing, and energy
systems.8
Biotechnology.
Biology is now programmable, and AI’s ability to identify ways to optimize this programming
will enable transformational biotechnology breakthroughs. AI was crucial in the rapid
development of COVID-19 vaccines, allowing researchers to finalize the genetic sequenceof a vaccine candidate only two days after the virus’ full genetic sequence was first posted
online.9 Computer vision techniques applied to medical imagery have also enabled moreaccurate and efficient diagnoses.10 And recently, an AI network made substantial progressover the last year toward solving one of biology’s most daunting challenges: determining
a protein’s 3D shape from its amino-acid sequence.11 Tools such as these will become““Biology is now programmable,
and AI’s ability to identify ways
to optimize this programming
will enable transformational
biotechnology breakthroughs.”p260

CHAPTER 16

even more powerful in combination with synthetic biology and gene editing. Together they
will enhance human health by allowing deeper studies of the building blocks of life andenabling the quicker discovery and fabrication of more advanced drugs and materials. As
AI fuels rapid new developments in the biological sciences and biotechnology becomesa greater driver of the overall world economy, the strategic consequences of ceding
leadership in biotechnology will increase significantly—a fact that the COVID-19 pandemic
illustrates in clear and stark terms. The government should:
Recommendation

Prioritize the development of an advanced domestic biotechnology R&D ecosystem. As
part of a national bioeconomy strategy, the United States should support the developmentof biotechnology platforms that maximize researchers’ ability to utilize AI to drive new
biological breakthroughs and help transition advanced research into physical products
at scale. This will necessitate support for both world-class biodata resources to fully
harness the power of AI and biomanufacturing platforms to rapidly realize the benefits
from analytical breakthroughs:• Biodata: The United States should fund and prioritize efforts to build a world-classbiobank containing a wide range of high-quality biological and genetic data sets
securely accessible by researchers. GenBank, the leading U.S. genetic database, which
is run by the National Institutes of Health, is currently underfunded, underutilized, andpoorly curated. The goal should be to create a genetic database that is well-curated andeasy for researchers to access and use; contains a significant number and broad rangeof whole human, animal, and plant genomes; and aggregates open and proprietarydata sets across government and the private sector. It should also contain de-identified
metadata about corresponding phenotypes whenever possible and include strong
privacy protections for human genetic data. This will require significant levels offunding; China National GeneBank, the equivalent facility in China that is operated
by the BGI Group (formerly the Beijing Genomics Institute), required approximately$117 million in initial funding.12 Establishing such an entity in the United States would
enhance and democratize biotechnology innovations by pooling existing data resources
and facilitating new levels of AI-enabled analysis of genetic data while also reducing
U.S. researchers’ reliance on BGI or other Chinese entities for access to large-scale
genomic databases for research.
• Biomanufacturing: The United States should support efforts to diversify and expand
the biotechnology industry beyond its current vertically integrated models andencourage the development of multiple standardized, merchant biofabrication
facilities. Doing so is necessary to ensure U.S. biomanufacturing capabilities keep pacewith AI’s transformative impact on the bioeconomy. Expanding access to advanced
biofabrication tools among startups and laboratories would allow firms to rapidly design
new molecules and materials via the cloud and place immediate orders for fabrication.
Such efforts should include R&D funding and incentives to support advanced biotech
manufacturing initiatives through entities such as the Biomedical Advanced Researchand Development Authority (BARDA),13 with appropriate stewardship, and the expansionof existing relevant programs such as BioMADE.14 Congress should prioritize suchinitiatives in future health-related spending bills. Given that up to 60% of the physicalinputs to the global economy could be produced via synthetic biology, there is a clear
and pressing need for the United States to retain leadership in biomanufacturing moving
forward.15

p
261

“

A S S O C I AT E D T E C H N O L O G I E S“... quantum computers havethe potential to outperformtheir classical counterparts on
certain classes of problems
related to machine learning andoptimization, the simulationof physical systems, and thecollection and transfer ofsensitive information.”

Quantum Computing.
As the pace of innovation predicted by Moore’s Law becomes increasingly difficult for
semiconductor manufacturers to maintain due to the physical limits of microchip design,
leadership in next-generation computer hardware will be essential to preserving long-term
U.S. advantages in strategic technologies like AI.16 Although classical computers will likely
remain the most economical way of performing day-to-day computational tasks in the near
future, quantum computers have the potential to outperform their classical counterpartson certain classes of problems related to machine learning (ML) and optimization, thesimulation of physical systems, and the collection and transfer of sensitive information.
For example, quantum computers may be able to efficiently optimize military logistics or
discover new materials for weapon systems.17 Each of these applications creates novel
national security threats and opportunities at the intersection of AI and quantum computing.
The government should:
Transition from basic research to national security applications of quantum computing andincentivize domestic fabrication. The United States is a global leader in research of quantum
computers, but it risks losing its edge in applications to national security. Recognizingthat advances in quantum computing may drive advances in AI, the United States must
establish trusted sources of materials and components for quantum computers, invest in
the development of hybrid quantum-classical algorithms, and focus on fielding of national
security applications. Offering access to both classical and quantum computers throughRecommendationp262

CHAPTER 16

the National AI Research Resource will facilitate the development of hybrid quantumclassical algorithms that leverage noisy intermediate-scale quantum computers. Publicly
announcing specific government use cases of quantum computers will signal that a market
exists for national security applications and encourage further investment by the private
sector.
5G and Advanced Networking.
5G networks will form the connective tissue between AI platforms, which means maintaining
access to trusted and robust 5G networks is a critical component of overall leadership in
AI. Huawei is pursuing global dominance in 5G, and there is no single supplier that cancompete with it in terms of both price and quality. Due to the urgency of the issue, theUnited States should pursue several complementary approaches concurrently to ramp up
deployment of 5G domestically and provide a credible alternative to Huawei. As a starting
point, any comprehensive effort should include support for dynamic spectrum sharing.18
The government should:““Expanding spectrum sharing
efforts is critical to ensuringthat DoD maintains accessto spectrum essential for
operational effectiveness while
broadening commercial access tospectrum for 5G networks.”

Recommendationp
263

Bolster and accelerate U.S. 5G network deployment through mid-band spectrum
sharing. Expanding spectrum-sharing efforts is critical to ensuring that the Departmentof Defense (DoD) maintains access to spectrum essential for operational effectivenesswhile broadening commercial access to spectrum for 5G networks. A multi-agency effort
is needed to expand sharing arrangements and licenses and permit additional portionsof the mid-band to be simultaneously utilized by DoD and commercial carriers. Throughthis portfolio approach, the United States stands the best chance of accelerating its 5G
deployment at a pace that can support the widespread adoption of AI.

A S S O C I AT E D T E C H N O L O G I E SAutonomy and Robotics.
Autonomous systems are already unlocking value across global markets. In the private
sector, they enable products ranging from expert advisory systems and self-driving
vehicles to manufacturing. In the realm of national security, autonomous systems generate
opportunities to reduce the number of warfighters in harm’s way, increase the pace andquality of decisions, and create entirely new military capabilities.19 The ability to design
and produce the hardware and software for advanced robotics is an essential part ofautonomous systems. The government should:
Incentivize the development of world-class software platforms for robotic and autonomous
systems. The future of autonomy and robotics will manifest in almost unlimited shapes andsizes as firms develop and tailor robots for different use cases and environments. The
U.S. trails nations such as China, Japan, and South Korea in the deployment of robots
and robotic hardware and must work to improve its capabilities in such areas as materials
design and energy storage for robots.20 However, U.S. expertise in software development
lends itself to creating a world-class digital platform for many classes of robotic hardware.
The software powering robotic systems will be built upon several core capabilities rooted
in AI: It will need to be able to sense its environment, reason, and operate in the world
around it.21 In creating cutting-edge software for these types of capabilities, there is an
opportunity for U.S. firms to win the market for the software platforms that power the next
wave of industrialization.22 To promote U.S. leadership in the development of software
for autonomous systems, the U.S. government should fuel industry’s ongoing efforts
by supplementing the basic R&D, standard-setting, and data-sharing programs led by
National Institute of Standards and Technology (NIST)’s Intelligent Systems Division.23 It
should also incentivize early adoption of automation and create markets for autonomous
systems in areas already ripe for them, such as mail sorting, that will yield data andexperience relevant for achieving scale and addressing adjacent markets.24 Combined,a multi-pronged approach along these lines would position industry to compete more
effectively in the market for autonomous system software, a strategically important areaaligned with existing U.S. technical strengths.

Recommendation

Advanced and Additive Manufacturing.
The capacity to produce high-tech goods domestically is critical to national security, bothto maintain access to finished goods and as a driver of innovation. In terms of access,the United States must strive for self-reliance in industries that are critical to national
security or that would take too long to regenerate in the event of protracted conflict.25
Innovation also benefits from a tight feedback loop between technological design andproduction, which allows for more rapid iteration.26 This link is particularly important in
the defense sector, where feedback from the manufacturing process back into the R&D
cycle helps bring technology from lab to military operations. Longer-term disruptions to
the manufacturing industry through new techniques such as additive manufacturing also

p
264

“

CHAPTER 16“The capacity to produce
high-tech goods domesticallyis critical to national security,
both to maintain access tofinished goods and as a driverof innovation.”pose threats and opportunities for national security. For example, additive manufacturing
may enable a step-change in domestic manufacturing capabilities, but it also creates
new threats by potentially democratizing the production of firearms and other goods withmilitary applications.27 The government should:

Recommendation

Accelerate additive manufacturing production of legacy parts across the DoD. Additive
manufacturing and 3D printing have the potential to transform manufacturing. They arecapable of rapid, high-quality, and complex production, and they are flexible enough that3D printers may be able to be located near the point of need for just-in-time production.28
Although current additive manufacturing techniques struggle to replicate the quality ofadvanced traditional manufacturing techniques, AI has already shown the ability to enablesignificant improvements in their accuracy.29 The Federal Government should proactively
support initiatives that advance the development of additive manufacturing techniques
and also provide practical benefits by easing the production of legacy items.30 The DoD
should announce a goal of identifying all legacy parts in active weapon systems that arecapable of being produced via additive manufacturing and 3D printing and doing so by
2025.
Energy Systems.
Cheap and reliable access to energy is critical to U.S. national security, whether it be toensure military readiness, facilitate the response to a domestic crisis, or keep the economy
functioning smoothly. As an input to nearly every sector, the price of energy directly impacts
economic output and is a key determinant of U.S. national competitiveness. Furthermore,
dependence on foreign countries for energy resources and technologies would put theUnited States in a position of vulnerability, especially if those resources or technologies
are controlled by strategic competitors. Although the United States is at the forefront of the
exploration, extraction, and processing of oil and gas and possesses significant domesticp265

A S S O C I AT E D T E C H N O L O G I E S

reserves, China is far and away the leading producer of renewable energy and is investingheavily in advanced energy storage technologies, such as batteries and their constituent
materials.31 To remain competitive, in these critical sectors U.S. industry will need to achieveaggressive cost targets in terms of kilowatts/hour and energy density. This is especiallytrue in markets with the most substantial growth potential, such as long-duration stationary
storage devices and battery packs for electric vehicles.32 The government should:
Develop and domestically manufacture energy storage technologies to meet U.S. market
demand by 2030. Developing new technologies to more effectively store electricalenergy so it is readily available whenever and wherever needed would drive advances inelectricity transmission and distribution. It would also offer advantages to the United States
both economically and strategically. To accelerate breakthroughs in energy storage,33
the Department of Energy has set the ambitious goal of developing and domesticallymanufacturing storage technologies capable of meeting the entirety of U.S. market
demand by 2030.34 Congress should fully fund the federal R&D and establish incentives
for commercialization needed to achieve the Department of Energy’s Energy Storage
Grand Challenge roadmap by 2030.35

Recommendation““Cheap and reliable accessto energy is critical to U.S.
national security, whether it beto ensure military readiness,
facilitate the response to adomestic crisis, or keep the
economy functioning smoothly.”p266

CHAPTER 16

Chapter 16 - Endnotes
Recognizing this connection, Congress included AI and “associated technologies” as they relate tonational security within the scope of the Commission’s mandate.

1

The Commission’s first Interim Report identified biotechnology, quantum computing, and 5G as key
emerging technologies associated with AI. See Interim Report, NSCAI at 50 (Nov. 2019), https://www.
nscai.gov/previous-reports/.

2

Made in China 2025 includes the following sectors: new-generation information technology,
high-grade machine tooling and robotics, aviation and aerospace equipment, marine engineering
equipment and high-tech ships, advanced rail transportation equipment, new energy automobiles,
electric power equipment, agriculture equipment, new materials, and biomedicine and high-tech
medical devices. See Alice Tse & Julianna Wu, Why ‘Made in China 2025’ Triggered the Wrath ofPresident Trump, South China Morning Post (Sept. 11, 2018), https://multimedia.scmp.com/news/
china/article/made-in-China-2025/index.html.

3

According to McKinsey, “Platforms are the back-end technology capabilities, whether provided byindividual systems or by assemblies of multiple systems, that power products.” Ross Frazier, et al.,Products and Platforms: Is Your Technology Operating Model Ready?, McKinsey Digital (Feb. 28,
2020), https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/products-andplatforms-is-your-technology-operating-model-ready.

4

The United States ranks 18th among 37 Organisation for Economic Co-operation and Development(OECD) countries in fixed and mobile broadband subscriptions per 100 inhabitants and eighthin average broadband speed. Broadband Portal, OECD (July 2020), https://www.oecd.org/sti/broadband/broadband-statistics/ (see “Penetration and data usage” table “1.2 Fixed and mobile
broadband subscriptions per 100 inhabitants” [Dec. 2019] and “Speeds” table “5.2 Akamai average
speed” [Q1 2017]).

5

Smart City Index, IMD, 8 (Oct. 2019), https://www.imd.org/research-knowledge/reports/imd-smartcity-index-2019/#:~:text=The%20Top%2010%20smartest%20cities,and%20Dusseldorf%20(10th).

6

For example, the White House published its National Strategy for Critical and Emerging Technologies
in October 2020, which included a list of critical and emerging technologies as identified by
Departments and Agencies. The document does not provide detail on how each technology isessential to national competitiveness and lacks a specific plan for promoting and protecting U.S.
advantages in each. See National Strategy for Critical and Emerging Technologies, The White Houseat A-1 (Oct. 2020), https://www.whitehouse.gov/wp-content/uploads/2020/10/National-Strategy-forCET.pdf?utm_source=morning_brew. In their 2018 report, Michael Brown and Pavneet Singh argue
that the lack of a unified list of critical technologies harms the ability of the United States to protect
against technology transfer. See Michael Brown & Pavneet Singh, China’s Technology Transfer
Strategy, Defense Innovation Unit Experimental at 37 (Jan. 2018), https://admin.govexec.com/media/
diux_chinatechnologytransferstudy_jan_ 2018_(1).pdf.

7This includes all technologies other than AI on NSCAI’s proposed critical technologies list with the
exception of semiconductors, which are addressed separately in Chapter 13 of this report. Elementsof AI-enabled biotechnology are also separately addressed in Chapter 1 of this report. Additional
recommendations to promote U.S. leadership in biotechnology, quantum computing, and 5G can befound in the Chapter 16 Blueprint for Action.

8

9Hannah Mayer, et al., AI Puts Moderna Within Striking Distance of Beating COVID-19, Harvard
Business School (Nov. 24, 2020), https://digital.hbs.edu/artificial-intelligence-machine-learning/aiputs-moderna-within-striking-distance-of-beating-covid-19/; Noah Weiland, et al., Modern Vaccine Is
Highly Protective Against Covid-19, the F.D.A. Finds, New York Times (Dec. 18, 2020), https://www.
nytimes.com/2020/12/15/health/covid-moderna-vaccine.html.
10
Junfeng Gao, et al., Computer Vision in Healthcare Applications, Journal of Healthcare Engineering(March 4, 2018), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5857319/.
11
Ewen Callaway, ‘It will Change Everything’: DeepMind’s AI Makes Gigantic Leap in Solving Protein
Structures, Nature (Nov. 30, 2020), https://www.nature.com/articles/d41586-020-03348-4.
12Zhuang Pinghui, China Opens First National Gene Bank, Aiming to House Hundreds of Millions
of Samples, South China Morning Post (Sept. 22, 2016), https://www.scmp.com/news/china/
article/2021623/chinas-noahs-ark-first-national-gene-bank-opens-shenzhen.

p267

A S S O C I AT E D T E C H N O L O G I E S

Biomedical Advanced Research and Development Authority (BARDA), U.S. Department of Healthand Human Services (Dec. 4, 2019), https://www.hhs.gov/about/agencies/orgchart/aspr/barda/index.
html.

13

14
BioMADE is a public-private partnership with DoD, operated under Manufacturing USA, thatis focused on building a sustainable bioindustrial manufacturing ecosystem and enhancing U.S.
bioindustrial competitiveness. See BioMADE (Bioindustrial Manufacturing and Design Ecosystem),
Manufacturing USA (last accessed Jan. 9, 2021), https://www.manufacturingusa.com/institutes/
biomade.

Michael Chui, et al., The Bio Revolution, McKinsey Global Institute at 43 (May 13, 2020), https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-bio-revolutioninnovations-transforming-economies-societies-and-our-lives.

15

16
Steve Blank, What the GlobalFoundries’ Retreat Really Means, IEEE Spectrum (Sept. 10, 2018),
https://spectrum.ieee.org/nanoclast/semiconductors/devices/what-globalfoundries-retreat-reallymeans.
17
Pontus Vikstål, et al., Applying the Quantum Approximate Optimization Algorithm to the TailAssignment Problem, Physical Review Applied, vol. 14, iss. 3 (Sept. 3, 2020), https://doi.org/10.1103/
PhysRevApplied.14.034009; He Ma, et al., Quantum Simulations of Materials on Near-term Quantum
Computers, npj Computational Materials (July 2, 2020), https://doi.org/10.1038/s41524-020-00353-z.
18
The 5G Ecosystem: Risks & Opportunities for DoD, DoD Defense Innovation Board (April 2019),
https://media.defense.gov/2019/Apr/03/2002109302/-1/-1/0/DIB_5G_study_04.03.19.pdf.
19
Summer Study on Autonomy, DoD Defense Science Board (June 2016), https://dsb.cto.mil/reports/2010s/DSBSS15.pdf.
20
Maximiliano Dvorkin & Asha Bharadwaj, Which Countries and Industries Use the Most Robots,
Federal Reserve Bank of St. Louis (Nov. 7, 2019), https://www.stlouisfed.org/on-the-economy/2019/
november/robots-affecting-local-labor-markets.
21
Advanced materials (such as biological components), brain-computer interfaces, and small andefficient power supplies are additional areas of potential innovation that connect robotics to AI andother associated technologies described in this chapter.
22
For example, core capabilities might include gripping physical objects, which robotics maker ABB
and several firms in the U.S. and Europe are currently pursuing. See Jonathan Vanian, Industrial
Robotics Giant Teams Up with a Rising A.I. Startup, Fortune (Feb. 25, 2020), https://fortune.
com/2020/02/25/industrial-robotics-ai-covariant/.

Intelligent Systems Division, NIST (last accessed Jan. 6, 2021), https://www.nist.gov/el/intelligentsystems-division-73500.

23

One specific area to expand demand for autonomous systems could be drastically scaling the
U.S. Postal Service’s Autonomous Mobile Robot pilot program from 25 sorting facilities to all sorting
facilities by 2025. Autonomous Mobile Robots and the Postal Service, USPS Office of Inspector
General (April 9, 2018), https://www.uspsoig.gov/sites/default/files/document-library-files/2019/RARCWP-18-006.pdf.

24

Critical Technology Accessibility, National Academies Press (2006), https://www.nap.edu/
read/11658/chapter/1; see also Assessing and Strengthening the Manufacturing and Defense
Industrial Base and Supply Chain Resiliency of the United States, Interagency Taskforce in Fulfillment
of Executive Order 13806 at 46 (Sept. 2018), https://media.defense.gov/2018/oct/05/2002048904/1/-1/1/assessing-and-strengthening-the-manufacturing-and%20defense-industrial-base-and-supplychain-resiliency.pdf (identifying 10 risk archetypes threatening America’s manufacturing and defense
industrial base).

25

26
Strategy for American Leadership in Advanced Manufacturing, National Science & Technology
Council (Oct. 2018), https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/10/AdvancedManufacturing-Strategic-Plan-2018.pdf; Gregory Tassey, Rationales and Mechanisms for Revitalizing
US Manufacturing R&D Strategies, NIST (Jan. 29, 2010), https://www.nist.gov/system/files/
documents/2017/05/09/manufacturing_strategy_paper_0.pdf.

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Chapter 16 - Endnotes
3D Opportunity for Adversaries, Deloitte (Aug. 22, 2017), https://www2.deloitte.com/us/en/insights/
focus/3d-opportunity/national-security-implications-of-additive-manufacturing.html.
27

28
Audit of DoD’s Use of Additive Manufacturing for Sustainment Parts, DoD Inspector General (Oct.
17, 2019), https://media.defense.gov/2019/Oct/21/2002197659/-1/-1/1/DODIG-2020-003.pdf.
29Mark Anderson, 3D Print Jobs Are More Accurate with Machine Learning, IEEE Spectrum (Feb. 19,
2020), https://spectrum.ieee.org/tech-talk/artificial-intelligence/machine-learning/3d-print-jobs-newsaccurate-machine-learning.
30
For instance, in August 2020, the DoD printed the first metal part for a B-52 jet engine. Kyle
Mizokami, The Old School Engine That Powers the B-52 Gets a 3D-Printed Upgrade, Popular
Mechanics (Aug. 10, 2020), https://www.popularmechanics.com/military/aviation/a33535790/airforce-3d-print-metal-part-turbofan-engine/.

p269

A S S O C I AT E D T E C H N O L O G I E SRobert Rapier, Ten Countries That Dominate World Fossil Fuel Production, Forbes (July 14, 2019),
https://www.forbes.com/sites/rrapier/2019/07/14/ten-countries-that-dominate-fossil-fuel-production;
Country Rankings, International Renewable Energy Agency (last accessed Jan. 6, 2021), https://www.
irena.org/Statistics/ View-Data-by-Topic/Capacity-and-Generation/Country-Rankings.

31

32Energy Storage, U.S. Department of Energy (last accessed Jan. 6, 2021), https://www.energy.gov/
oe/energy-storage.

The field of energy storage includes a broad technology base such as batteries (both conventional
and advanced), electrochemical capacitors, flywheels, power electronics, control systems, andsoftware tools for storage optimization and sizing.

33

34
Energy Storage, U.S. Department of Energy (last accessed Jan. 6, 2021), https://www.energy.gov/
oe/energy-storage.

Energy Storage Grand Challenge: Roadmap, U.S. Department of Energy (Dec. 2020), https://www.
energy.gov/sites/prod/files/2020/12/f81/Energy%20Storage%20Grand%20Challenge%20Roadmap.
pdf.

35

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Blueprints for Action

PART ONEp271

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E

Chapter 1: Emerging Threats in the AI Era

273

Combating Malign Information Operations Enabled by AI

273

Preparing for AI-Enabled Cyber Conflict

278

Chapter 2: Foundations of Future Defense

291

Chapter 3: AI and Warfare

337

Chapter 5: AI and the Future of National Intelligence349

Chapter 6: Technical Talent in Government

357

Chapter 7: Establishing Justified Confidence in AI Systems

379

Chapter 8: Upholding Democratic Values: Privacy, Civil Liberties,
and Civil Rights in Uses of AI for National Security

395

The following Blueprints for Action cover Part I of NSCAI’s Final Report. Part I, “Defending America in the AI
Era,” (Chapters 1-8) outlines what the United States must do to defend against the spectrum of AI-related
threats from state and non-state actors, and recommends how the U.S. government can responsibly use AI
technologies to protect the American people and our interests. These Blueprints for Action complement theCommission’s Final Report and mirror its organizational structure.
The Blueprints for Action serve as more detailed roadmaps for Executive and Legislative branch actions toretain America’s AI leadership position. They identify who should take a particular action––Congress, theWhite House, or an executive branch department or agency. The Commission provides estimated increases
in funding or appropriations as part of its recommendations. All recommendations that include funding
figures should be considered estimates for consideration by Congress and/or the Executive branch.
This report does not contain a separate Blueprint for Action for Chapter 4. This is because given the importance of the
topic, the Commission chose to detail its arguments, recommendations, and the specific actions required to implement
them directly in this chapter. Additionally, further detail on how the United States should adapt its TEVV policies to maintainconfidence in AI systems can be found in Chapter 7 and its associated Blueprint for Action, and recommendations on
relevant changes to DoD organizational structure can be found in Chapter 3.

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Chapter 1: Emerging
Threats in the AI Era
Blueprint for Action: Number One

Combating Malign Information Operations Enabled by AI.

The use of AI to produce, manipulate, and promote malign information marks a disruptive
evolution in the use of information as a tool of statecraft, a weapon of war, and a threat todemocracy.1 The following recommendations represent a strategic, organizational, andoperational framework that the U.S. government should adopt to adequately defend andcounter malign information operations in the AI era, including by employing AI-enabledtechnologies.
Recommendation

Recommendation: A National Strategy for the Global Information Domain
Expanding upon the principles of information statecraft outlined in the 2017 National
Security Strategy,2 the President should issue a new national strategy for the global
information domain that more fulsomely addresses how AI and associated technologies
are defining new fronts in this area. The strategy should:• Acknowledge that the network-connected world is dissolving barriers between
societies.
• Prioritize the global information domain as an arena for competition.
• Detail how adversarial state and non-state actors are attempting to define and control
the global information domain in order to shape global opinion and achieve strategic
advantage.
• Account for the critical role of AI-enabled malign information in achieving these goals.
• Designate malign information operations as a national security threat with its own set ofpriority actions to defend, counter, and compete against them.
• As necessary, update critical infrastructure designations and require relevant
departments and agencies to update sector-specific plans to reflect emerging
technologies.
• Establish organizational structures for U.S. national security agencies to defend, counter,and compete against the threat.
Action for the President:• Issue a supplemental National Strategy for the Global Information Domain.

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Action for Congress:• Congress should direct the Executive Branch to transmit a National Strategy for the
Global Information Domain that categorizes the global information domain as an arenaof competition vital to the national security of the United States.
Organizational Framework
The proliferation of malign information has exposed an Achilles heel in the U.S. national
security apparatus. Previous major reorganizations could not foresee contemporary digitaltechnology and society’s profound dependence upon it. They could not anticipate the useof ICT platforms and tools, bots, and AI-enabled technologies to spread false information.
They do not account for the role that the commercial sector and civil society play indefending against malign information, and enabling its spread. Individual agencies suchas the Department of Homeland Security (DHS), the Federal Bureau of Investigation (FBI),and the Office of the Director of National Intelligence (ODNI) have stretched their mandates
to confront the threat. They rely on narrow sets of outdated tools, and are hampered bycultures shaped by the Cold War and counter-terrorism paradigms.
Recommendation: Create a Joint Interagency Task Force (JIATF) and Operations Center.

Recommendation

Action for the President:• Direct creation of a JIATF and operations center to lead and integrate government
efforts to counter foreign-sourced malign information in real time.
o The Presidential action should direct the Secretaries of State, Defense, Justice, andHomeland Security, the Attorney General and the Director of National Intelligence,to create a JIATF and stand-up an operations center to counter foreign-sourced
malign information.
o The JIATF should integrate efforts of key offices, bureaus, and divisions within eachof these agencies, as well as the broader intelligence community (IC) and law
enforcement establishment.
o The JIATF should have the responsibility to survey the landscape of relevant public
and private actors, coordinate among them, and act in real time to counter foreign
information campaigns.
o The JIATF should draw on existing authorities to create an operations center withmodern, AI-enabled digital tools and expert staff to expose, attribute, and respondeffectively.
o The Presidential action should also direct these officials, as part of the JIATF, tocreate a mechanism to share and exchange critical information with key companies
in the private sector that run internet and social media platforms where maligninformation proliferates.

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Action for the Secretaries of State, Defense, Justice, Homeland Security, and the Directorof National Intelligence:• Establish the JIATF and Operations Center.
o These agency heads should direct immediate development of a plan to create
the JIATF and operations center with a focus on identifying those offices, bureaus,
and divisions within their agencies and the broader IC and law enforcement
establishment that are essential to the mission of countering foreign-sourced
malign information.
■■

As part of this effort, the JIATF should leverage the authority provided by
Congress in the FY2020 NDAA to stand-up a Foreign Malign Influence
Response Center within ODNI.3
Components that will be critical to the JIATF include, among others, theCentral Intelligence Agency’s Open Source Enterprise and the National
Counterintelligence and Security Center.4 Leadership will need to ensure
involvement of relevant components from the FBI, the National Security
Agency, across the Department of Defense, and the Global Engagement
Center (GEC) at the Department of State.

• The JIATF would lead and integrate existing and new national strategic efforts against
foreign malign information operations by providing analysis, sharing information withgovernment and commercial partners, and driving whole-of-government action,subject to Presidential direction, to advance U.S. information objectives.
• The Commission proposes that the operations center component of the JIATF be
modeled on the National Counterterrorism Center (NCTC), as a proven model for
providing real-time situational awareness of and response to evolving national security
threats.
• To exchange information and coordinate with internet and social media platforms on
malign information threats, the Commission proposes creation of an associated industry
consortium that includes an information sharing and analysis center (ISAC). The
consortium, supplemented by the ISAC, would allow the JIATF to exchange information
with industry, monitor malign information across ICT platforms, and improve U.S.
government response to malign information threats. In developing the ICT consortiumand ISAC, JIATF should look to the Global Internet Forum to Counter Terrorism as amodel.5
Action for the Director of National Intelligence:• Appoint a Malign Information Threat Executive (MITE) to lead the JIATF.
o In July 2019, ODNI created the Election Threat Executive position responsible
for coordinating across the IC on issues related to election security.6 The threatof foreign malign information operations demands that this position be elevated,renamed, and expanded beyond the subject of elections.
o The MITE role should also serve as a liaison function between the White House/
National Security Council and the JIATF to ensure alignment and responsiveness tothe national security strategy.

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Action for Congress:• Appropriate $30 million per year to support the operations of the JIATF.
Operational Framework
Efforts by the U.S. Government and private sector to counter terrorist propaganda offer apotential roadmap for how the United States can go on the offensive to counter and compete
against malign information. The creation of the Global Coalition to Defeat the Islamic Stateof Iraq and Syria (ISIS) has shown how a burden-sharing model can be deployed tosuccessfully counter and defeat a shared threat.7 The United States and its allies will only
succeed if they can develop and deploy personnel as well as an advanced set of tools to
assist in their effort to counter and compete against malign information operations. Efforts
need to be made to encourage innovation as well as harness commercially available
technologies to go on the offensive.
Recommendation: The Department of State should lead a global effort to counter
disinformation.

Recommendation

Action for the President:• Designate the Under Secretary of Public Diplomacy and Public Affairs at the
Department of State to lead the international fight against malign information
operations.
Action for the Department of State:• Build an International Task Force to Counter and Compete Against Disinformation.
Modeled after the Global Coalition to Defeat ISIS, the Department of State should
build a similar task force to counter malign information. The International Task Forceto Counter and Compete Against Disinformation should be led by the Departmentof State’s Under Secretary for Public Diplomacy and Public Affairs, with the GEC
coordinating its daily activities.8 The task force will be in charge of directing, leading,synchronizing, integrating, and coordinating efforts by allies to recognize, understand,expose, and counter foreign state and non-state propaganda and malign information
efforts. The GEC should leverage the work of the Technology Engagement Team (TET)to share and test technologies to detect and disrupt the creation, manipulation, anddissemination of malign information from state and non-state actors. See the Chapter
15 Blueprint for Action for more detail on creating a task force as part of the Emerging
Technology Coalition proposed by the Commission.
Recommendation: The Defense Advanced Research Projects Agency (DARPA) should
coordinate multiple research programs to detect, attribute, and disrupt AI-enabled maligninformation campaigns and to authenticate the provenance of digital media.

Recommendation

The government should sponsor research to develop technologies to detect, attribute,and disrupt malign influence operations, including influence campaigns, psychologicaloperations on social media platforms, and manipulated and synthetic media. In parallel,p276

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the government should develop alternative technologies to authenticate the provenanceof digital media and head off the risk that other approaches will not be successful. These
efforts should be led by DARPA.
Action for Congress:• Appropriate $60 million to $80 million in additional funding for DARPA to sponsor
multiple research projects to develop technologies to detect, attribute, anddisrupt malign influence operations that rely on AI-generated content, and todevelop alternative technologies to authenticate the provenance of digital media.9
DARPA has existing authority to fund such research with the scope outlined in thisrecommendation, but will require dedicated appropriations to carry out the effort and asecurity review of the best innovation vehicles to sponsor the research.
Action for DARPA:• Sponsor further research as described above using innovation vehicles, such as
challenge competitions, or any other deemed necessary by DARPA to develop
and transition these technologies to accountable agencies and departments for
maximum employment.
Recommendation

Recommendation: Create a task force to study the use of AI and complementary
technologies, including the development and deployment of standards and technologies,
for certifying content authenticity and provenance.
In response to the challenges of misinformation, efforts are underway to develop standards
and pipelines aimed at certifying the authenticity and provenance of audiovisual content.10
These efforts make use of technologies, including encryption and fragile watermarking, tosecure and track the expected transformations of content via production and transmission
pipelines. These efforts offer the opportunity to mitigate malign information campaigns that
seek to corrupt or spoof highly trusted sources of information across our digital ecosystem.
This technology area is ripe for public-private partnership, as several private organizations
are already forming to fight disinformation.11
Actions for the Office of Science and Technology Policy (OSTP):• Establish a task force to study the use of AI and complementary technologies forcertifying content authenticity and provenance.
o OSTP should establish an interagency task force to assess the use of AI andcomplementary technologies to certify content authenticity and provenance, toinclude an evaluation of technical standards and production and transmission
pipelines.
o The task force should make recommendations on methods to improve content
certification, which may include public-private initiatives, legislation, and changes
to federal policy. In addition, the task force should assess options for federal
regulation of content certification by non-governmental organizations.

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Recommendation: Executive Branch departments and agencies should utilize Other
Transaction Authorities (OTAs), creative investing, and the Small Business Innovation
Research (SBIR) program to deploy capital to companies that offer technical solutions thatwill assist the United States Government in identifying, countering, and defending against
malign information operations.

Recommendation

The U.S. Government has an array of mechanisms that are not currently leveraged to
deploy capital to companies that create strategic technology to unleash AI, machinelearning (ML), and associated technologies in this counter-information operations fight.12
Action for all U.S. departments and agencies:• Explore the use of the SBIR program and OTAs to acquire technology solutions thatwill assist the United States Government in identifying, countering, and defending
against malign information operations.
The United States must prepare for both the present and future threat of increasingly
automated and AI-enabled cyber conflict. The expanding threats of mutating malware andAI-powered tools are combining with traditional cyber threats to automate, optimize, andultimately transform the precision, speed, stealth, scale, and effectiveness of cyber-attackand espionage campaigns.13 To defend the U.S. from current and future cyber threats,
we must move to develop AI-enabled cyber defenses and to mitigate proliferating cybervulnerabilities.

Chapter 1: Emerging
Threats in the AI Era
Blueprint for Action: Number Two

Preparing for AI-Enabled Cyber Conflict.

Section 1: Developing AI-enabled defenses against cyber attacks.
Recommendation: Develop and deploy machine-speed threat detection and mitigation.

Recommendation

Detecting and reacting to unknown threats on a network is difficult, but not impossible,
for self-learning AI systems that have been trained to differentiate between normal andanomalous network behavior.14 To address deficiencies highlighted by the SolarWinds
attack, autonomous defenses are needed to defend the U.S. Government’s systems.

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Actions for the Department of Homeland Security and Department of Defense:• Expand machine speed threat information sharing, behavior-based anomaly
detection, and cyber threat mitigation to all government networks containing
sensitive information and critical functions.
o DHS must improve the National Cybersecurity Protection System (NCPS), while
DoD must also accelerate its efforts to harness AI-enabled cyber defenses andsensors. At a minimum, the objective of these new defenses should be to flag or
potentially block never-before-seen connections and communications missed
by currently deployed intrusion detection and prevention technologies such as
EINSTEIN.15 To fully take advantage of new capabilities, these defenses should also
aim to accelerate recovery from cyber attack by automatically generating coursesof action for federal agencies to assure secure continuity of operations. These
defenses should assist recognition of insider threats as well as externally launchedattacks, and use machine speed information sharing to prepare other public andprivate networks to defend themselves against detected threats.
o DoD and DHS must also assess and mitigate security risks posed by introducing
and enhancing threat detection systems. These systems will require precautions
against their elevated system access being used to deliver malware or abused byother cyber threats. AI-enabled system components designed to mitigate new andunknown threats likewise will need defenses against adversarial techniques.
o To minimize cost overruns in altering a multibillion-dollar project, DHS should
reprogram $10 million to investigate the best means to accelerate and set up AIenabled threat detection systems. This study would be tasked to look for synergies
with existing intrusion detection software and infrastructure, seek to address any
remaining key deficiencies found by GAO in the National Cybersecurity Protection
System, and to develop a final budget proposal for Congress.16 This study likewise
should aim to address how previous intrusion detection systems failed to detect theSolarWinds cyber attack.
Recommendation

Recommendation: Execute large, instrumented, and realistic tests to gather data and train
AI-enabled cyber defenses.
AI-enabled cyber defenses require training to recognize potential threats, and sensors
to detect them. By experimenting with larger networks in realistic conditions, the UnitedStates can train more robust AI-enabled cyber defense capabilities.
Action for Congress:• Fund the Defense Advanced Research Projects Agency (DARPA) to sponsor
additional secure, instrumented, and realistic research on AI-enabled cyberdefenses.
o DARPA funding should be increased by $20 million, to be divided between asecurity review, and other programmatic costs for the additional research. DARPA
should be left free to determine the structure of further research, with an innovation
vehicle such as a challenge competition or any other that DARPA deems necessary.
• Expand the National Institute of Standards and Technology AI testbed program.

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o For FY2021, NIST requested a $25 million increase, for measurement tools andtestbeds to accelerate the development and adoption of interoperable, secure, andreliable AI technologies.17 Since then, NIST has been authorized for $64 million in
additional AI R&D responsibilities including AI testbeds. To ensure NIST can meetits new responsibilities in addition to its prior ones, Congress should meet NISTs
authorized funding increase for AI R&D.
Actions for DARPA:• Structure and standardize an innovation vehicle, such as a challenge competition,or any other DARPA deems necessary, to increase insight about options for new AIenabled cyber defenses.
o DARPA should aim to encourage the prototyping of new means of AI-enabled cyberdefense and test the efficacy of these defenses against intelligent opponents and
AI-enabled cyber threats. DARPA should structure new research to broaden insighton the importance of real-life factors such as cyber-attack externalities, differencesin risk tolerance between threat actors, and differences in network infrastructure
between defenders.18• Bring broader fields of expertise to bear for cyber defense research.
o Cyber expertise is not the only expertise relevant to cybersecurity and the efficacy
of cyber operations.19 The new research should involve experts from other fields
such as economics, game theory, and behavioral psychology to improve scoring
metrics, improve the human components of cyber strategy, and propagate insightfurther within government. With these improved metrics and insights, future
investments can be more directly aligned with mission assurance.
• Conduct a security review to determine the rules and bounds of new cyberresearch initiatives.
o DARPA must conduct a thorough security review about the second-order effectsof sponsoring research with public-facing results and without strong information
security measures, to mitigate against potential adversaries acquiring informationthat can be weaponized against us. International competition in this area is getting
so intense that the organization must consider using a vetted closed-challenge
competition or initiative as opposed to an open-challenge competition format.
Actions for NIST:• Expand the NIST AI testbed program to generate data for AI-enabled cyberdefenses in differing IT infrastructure environments.
o Larger-scale testing is necessary to generate the data required for AI-enabled cyberdefenses. By scaling testbeds within NIST, there will be the opportunity to generate
this data, and to evaluate the performance of varying network architectures atstrengthening network security.
o Training data often reflects a broad sampling of common scenarios and does notitself necessarily convey the costs of different types of compromises without further
labeling.20 NIST should create optimized data sets for training cyber defenses tominimize expected costs of network disruption, compromise, and damage rather
than merely trying to identify cyber threats and vulnerabilities with high accuracy.
To develop these data sets, NIST will need to hire or contract multidisciplinary talent
to develop better metrics.
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Recommendation

Recommendation: Ensure the robustness of AI-cyber defenses.
To make AI-based cyber defenses stronger, their supporting supply chains and data must
be defended, while the algorithms themselves must be protected from malware, trained
against adversarial techniques, and red teamed to the point of failure. This approach canbe found in the Chapter 7 Blueprint for Action.
Section 2: Ensuring resilience against AI-enabled cyber attacks.
Many of the defenses required to protect against AI-enabled cyber threats are also
required to defend against less advanced cyber threats. To provide this protection, theCommission endorses specific Cyberspace Solarium Commission recommendations,which are instrumental in enhancing U.S. defenses against AI-enabled cyber threats.21
Recommendation: Improve incentives for information and cyber security.

Recommendation

AI cannot defend inherently indefensible digital infrastructure against escalating offensive
AI-enabled cyber capabilities. Even if vulnerabilities are known and easily patchable,that is no guarantee that they will be closed without a further impetus to action. Similarly,
while new instrumented digital infrastructure is required to accelerate AI-enabled cyberdefenses, those that build it must be careful to ensure new vulnerabilities don’t outweighthe benefits of these defenses. In both cases, incentives must be realigned in the public
and private sector to assure gaps are closed and new infrastructure is secure.
Action for Congress:• Establish liability for final goods assemblers for damage stemming from incidents
that exploit known and unpatched vulnerabilities, incentivize reporting, andamend the Sarbanes-Oxley Act to include cybersecurity reporting requirements.22
o The Cyberspace Solarium Commission made recommendations to incentivize
timely vulnerability patching. In addition to these recommendations, companies
should be incentivized to improve their cybersecurity, and participate in newvulnerability disclosure programs via selectively reducing legal liability and productrecalls for companies that can mitigate and patch controlled vulnerabilities within alimited, but rule-defined, time period. The overall structure of liability reform should
aim to minimize perverse incentives to avoid liability by concealing failure. Grid,
critical infrastructure, and medical device companies should be the primary targets
for improvement.
o To harmonize and clarify cybersecurity oversight and reporting requirements for
publicly traded companies, Congress should amend the Sarbanes-Oxley Act toexplicitly account for cybersecurity.23

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Action for the Executive Branch• Incentivize information technology security through Federal Acquisition
Regulations and Federal Information Security Management Act authorities.24
o Zero-trust networking and robust code should become key priorities for government
contracts related to information technology, and especially for contracts related to
AI. Contractors should not be paid more for additional lines of code when adding
them generates new vulnerabilities without additional functionality. Code should be
subjected to AI-enabled vulnerability review.
• Task CISA to develop an IT infrastructure “Cash for Clunkers” incentive plan, to
submit to Congress for FY2022.
o This program would support the replacement of vulnerable outdated equipmentwith modern alternatives through targeted federal subsidies. CISA should
coordinate the effort by setting the program’s strategy, prioritizing devices andcritical digital infrastructure for replacement, and determining subsidy levels forthe systems to be replaced. CISA must develop the plan so as to minimize perverse
incentive to acquire vulnerable infrastructure before the plan is funded, and oncethe plan is developed, Congress must implement it as quickly as possible to reduce
perverse incentives for companies to hold out on replacing vulnerable devices andinfrastructure in the meantime.
Section 3: Disrupting adversary AI-enabled cyber-attacks and capabilities.
Recommendation: Develop additional, impactful non-kinetic options to respond to

Recommendation

adversarial cyber and information operations.

Modern information operations have enormous overlap with cyber operations. As AIenabled cyber capabilities spread in the presence of wide-open societal vulnerabilities,
the United States needs to have additional tools to counter proliferating threat actors, andto establish deterrence in the cyber and information domains.
Action for Congress:• Expedite the establishment of the Bureau of Cyberspace Security and Emerging
Technologies (CSET) within the U.S. Department of State.
o The CSET Bureau will be essential for strengthening norms in cyberspace,engaging other countries on information technology standards, assisting
allied cyber defense, and improving international cyber law enforcement.
Recommendations to expedite the Bureau’s buildout and ensure that it has a clear
mandate to coordinate strategy on the full range of emerging technology issues, inaddition to critical cybersecurity needs, can be found in the Chapter 15 Blueprint for
Action.
• Strengthen the U.S. Government’s ability to take down botnets by enacting Section
4 of the International Cybercrime Prevention Act.25

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o Botnets are already a present threat, and may become more powerful with advancesin AI, not just directly spreading malware, but harvesting both computational powerand data to put toward further offensive training in ways that were not previously
possible. To enable the U.S. Government to better work with private industry andinternational partners, Congress, in consultation with the Department of Justice,
should enact Section 4 of the International Cybercrime Prevention Act.26 Thislegislation would provide broader authority to disrupt all types of illegal botnets, notjust those used in fraud.27
Actions for Cyber Command, the Department of Homeland Security, the Federal Bureau
of Investigation, and the National Security Agency:• Expand current cyber threat inoculation initiatives.
o Machine speed information sharing is a key piece of enabling AI-cyber defenses.
To contribute to the readiness of U.S. defense and critical infrastructure, efforts
should be made to accelerate sharing of the most recent malicious code captured
in the wild through appropriate interagency channels, including through a Joint
Collaborative Environment. U.S. Cyber Command should ensure and accelerate
coordination with DHS, the FBI, NSA, and stakeholders in the private sector in therelease of threat information, particularly with owners and operators of systemicallyimportant critical infrastructure.28
Section 4: Coordinating and Strategizing a Response.
Recommendation

Recommendation: Reform the U.S. Government’s strategy, structure, organization, andauthorities for handling AI-enabled cyber threats.
The U.S. must organize and align authorities to fully implement the cyber security mission
and fully capitalize on machine speed information sharing defenses. Technology alone
isn’t enough: Cyber threat intelligence, joint planning, and response must be integrated
into the same organization to keep pace with AI cyber threats.
Actions for the Executive Branch:• Issue an updated National Cyber Strategy with the following components.
o First, the strategy should build on the layered deterrence framework put forward by
the Cyberspace Solarium Commission with a focus on making the framework morerobust against the ways AI will transform cyber conflict.29■

To support the strategy, the Department of Defense, in partnership with the
Department of State and the IC, should also develop a multitiered signaling
strategy and promulgate a declaratory policy that addresses the use of AI incyber operations.30

o Second, to inform the strategy, the Department of Homeland Security should run astudy to develop regulatory recommendations for the most cost-effective means ofdefending digital devices and infrastructure. This study should investigate, but not
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■

■

■

■

■

■

■

■

Standards requiring critical private and public sector networks to keep theirdata encrypted at rest and in transit
Multifactor authentication requirements for critical private and public sector
networks
Air gapping requirements for select sensitive, but still unclassified, networks
Analog defenses for cyber physical infrastructure to prevent the most lethal
failures regardless of how much network access cyber attackers gain, or how
advanced their methods of attack become
Federated machine learning techniques that lower espionage and privacy risk
via enabling data to be partitioned or remain decentralizedSpecialized, narrow purpose computation hardware that can’t be repurposedby malware for attacks
Ways to harness AI to lock down and constrain hardware toward its intended
purpose on vulnerable networks that can’t yet be patched or replaced
Ways to use cloud computing and virtual machines to reduce vulnerability of
AI and cyber systems to advanced persistent threats• Accelerate the establishment of a Joint Cyber Planning and Operations Center,modeled after the National Counterterrorism Center.31
o This planning office under the Cybersecurity and Infrastructure Security Agency isnecessary to coordinate cybersecurity planning and readiness across the federal
government and between public and private sectors. To properly stand-up such a
collaborative environment, the Executive branch must submit to Congress a list of
authorities and data sharing issues that will require additional authorities or funding.
• Develop and implement an information and communications technology industrial
base strategy.32
o This strategy must increase support to supply chain risk management efforts, andprovide better defense to the hardware supply chains, data, and algorithms thatcompose the “AI stack.”
Action for Congress:• Establish a Bureau of Cyber Statistics to inform both cyber defense policy and AIenabled cyber defenses.33
o Large accurate data sets with relevant data are especially useful for training AIenabled cyber defenses that minimize the costs of cyber attacks and false alarms,
rather than just the number of attacks and false alarms. To that end, Congressshould establish a Bureau of Cyber Statistics, within the Department of Commerce,or another department or agency, that would act as the government statisticalagency that collects, processes, analyzes, and disseminates essential statistical dataon cybersecurity, cyber incidents, and the cyber ecosystem to the American public,
Congress, other federal agencies, state and local governments, and the private
sector.34

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Recommendation

Recommendation: Coordinate with the Private Sector to Increase Resilience Against AIEnabled Cyber Attacks.
Action for Congress:• Create or Designate Critical Technology Security Centers.35
o Congress should direct and appropriate funding for the Department of Homeland
Security, in partnership with the Department of Commerce, Department of Energy,
Office of the Director of National Intelligence, and Department of Defense, tocompetitively select, designate, and fund up to three Critical Technology Security
Centers.
o These Centers would be designed to centralize efforts directed toward evaluating
and testing the security of devices and technologies that underpin our networks andcritical infrastructure.

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E M E R G I N G T H R E AT S I N T H E A I E R A• Authorize, establish, and fund a joint collaborative environment for sharing andfusing threat information.36
o Sharing and fusing threat information is an instrumental step in improving the speed
and capability of potential AI-enabled cyber defenses.37 Congress must ensure that
Executive branch agencies have necessary authorities to bring their data together
in support of these efforts. Likewise, Congress must create incentives—including
liability protection—to attract the private sector to participate in threat information
sharing programs.
o To achieve these goals, the Commission endorses the Cyberspace Solarium
Commission recommendation for Congress to establish a ‘Joint Collaborative
Environment,’ a common, cloud-based environment in which the federal
government’s unclassified and classified cyber threat information, malware
forensics, and network data from monitoring programs are made commonly
available for query and analysis—to the greatest extent possible.38

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Blueprint for Action: Chapter 1 - Endnotes
For the purposes of this section, “malign information” includes both disinformation—false information
or intentionally misleading facts communicated with the intent to deceive—and misinformation—false information not necessarily meant to deceive. See Daniel Fried & Alina Polyakova, Democratic
Defense Against Disinformation, Atlantic Council at n.1 (Feb. 2018); https://www.atlanticcouncil.org/
wp-content/uploads/2018/03/Democratic_Defense_ Against_Disinformation_FINAL.pdf. For a broader
discussion, see Laura Rosenberger, Making Cyberspace Safe for Democracy, Foreign Affairs (May/
June 2020), http://www.foreignaffairs.com/articles/china/2020-04-13/making-cyberspace-safedemocracy. For a study of how AI might be used to counter disinformation, see William Marcellino, etal., Human-machine Detection of Online-based Malign Information, RAND Europe (2020), https://www.
rand.org/content/dam/rand/pubs/research_reports/RRA500/RRA519-1/RAND_RRA519-1.pdf.

12
National Security Strategy of the United States, The White House at 34 (Dec. 18, 2017), https://
trumpwhitehouse.archives.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905-2.pdf.
3

Pub. L. 116-92, The National Defense Authorization Act for FY2020, 133 Stat. 1198, 2129-30 (2019).

4
National Counterintelligence and Security Center, Office of the Director of National Intelligence (lastaccessed Feb. 8, 2020), https://www.dni.gov/index.php/ncsc-home.

About, Global Internet Forum to Counter Terrorism (last accessed Oct. 2, 2020), https://www.gifct.
org/about/.
5

Press Release, Office of the Director of National Intelligence, Director of National Intelligence Daniel
R. Coats Establishes Intelligence Community Election Threats Executive, (July 19, 2019), http://www.
dni.gov/index.php/newsroom/press-releases/item/2023-director-of-national-intelligence-daniel-rcoats-establishes-intelligence-community-election-threats-executive.

6

7
Brett McGurk, America Should Build an International Coalition Now, The Atlantic (March 29, 2020),
https://www.theatlantic.com/ideas/archive/2020/03/america-should-build-international-coalitionnow/608983/.

Though this overall Blueprint for Action uses the term “malign information” to broaden beyond
disinformation to include misinformation, it will likely be easier to organize a task force aroundcountering disinformation.

8

Funding level should depend upon the number of programs DARPA has the capacity to execute inthis area.

910
See, e.g., Paul England, et al., AMP: Authentication of Media via Provenance, arXiv (June 20, 2020),
https://arxiv.org/abs/2001.07886.
11
See Creating the Standard for Digital Content Attribution, Content Authenticity Initiative (lastaccessed Feb. 19, 2020), https://contentauthenticity.org/; Overview, Project Origin (last accessed
Feb. 19, 2021), http://www.originproject.info/about.
12
These could be SBIRs, OTAs, or other modern vehicles with minimal red tape. Recently publishedreports on countering malign influence have issued wide-ranging recommendations including:deploying special operations forces to areas “vulnerable to political warfare,” building “rapidreaction information cells to track and counter” malign influence operations, and promoting civilsociety to “combine the values of accurate media with engagement skills and an understanding of
how propagandists prey on polarization, inflaming divides.” These recommendations are alreadybeing put into action by Special Operations Command in Africa, using commercially available
services to combat and attribute malign information operations about COVID-19 and other issues
on the continent. The General Services Administration has awarded IST Research a Phase III SBIR
contract to “support operations in the information environment for the special operations and Federal
Government community.” Additionally, the U.S. Air Force and U.S. Special Operations Command
have contracted with Primer to “automatically identify and assess suspected disinformation” using ML
technology. See David Ronfeldt & John Arquilla, Whose Story Wins: Rise of the Noosphere, Noopolitik,
and Information-Age Statecraft, RAND at 72 (July 2020), https://www.rand.org/content/dam/rand/
pubs/perspectives/PEA200/PEA237-1/RAND_PEA237-1.pdf (citing or quoting other experts orreports); Dave Nyczepir, SOCOM Looks to Combat Disinformation in Africa on New Governmentwide
Contract, FedScoop (July 27, 2020), https://www.fedscoop.com/socafrica-disinformation-ist-research/;
IST Research Awarded Five-year, $66 Million GSA Contract, IST Research (July 23, 2020), http://www.

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globenewswire.com/news-release/2020/07/23/2066650/0/en/IST-Research-Awarded-Five-year-66Million-GSA-Contract.html; SOCOM and US Air Force Enlist Primer to Combat Disinformation, Cision
PR Newswire (Oct. 1, 2020), https://www.prnewswire.com/news-releases/socom-and-us-air-forceenlist-primer-to-combat-disinformation-301143716.html.
Nicholas Duran, et al., 2018 Webroot Threat Report, Webroot (2018), https://www-cdn.webroot.
com/9315/2354/6488/2018-Webroot-Threat-Report_US-ONLINE.pdf; Gary J. Saavedra, et al.,
A Review of Machine Learning Applications in Fuzzing, arXiv (Oct. 9, 2019), https://arxiv.org/
pdf/1906.11133.pdf; Isao Takaesu, Machine Learning Security: Deep Exploit, GitHub (Aug. 29, 2019),
https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/DeepExploit; Catherine
Stupp, Fraudsters Used AI to Mimic CEO’s Voice in Unusual Cybercrime Case, Wall Street Journal(Aug. 30, 2019), https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusualcybercrime-case-11567157402; Implications of Artificial Intelligence for Cybersecurity: Proceedingsof a Workshop, National Academies of Sciences, Engineering, and Medicine (2019), https://doi.
org/10.17226/25488; Ben Buchanan, et al., Automating Cyber Attacks: Hype and Reality, Center for
Security and Emerging Technology (Nov. 2020), https://cset.georgetown.edu/research/automatingcyber-attacks/; Nektaria Kaloudi & Jingyue Li, The AI-Based Cyber Threat Landscape, ACM
Computing Surveys at 1-34 (Feb. 2020), https://dl.acm.org/doi/abs/10.1145/3372823; Dakota Cary &Daniel Cebul, Destructive Cyber Operations and Machine Learning, Center for Security and Emerging
Technology at 5-23 (Nov. 2020), https://cset.georgetown.edu/research/destructive-cyber-operationsand-machine-learning/.

13

14
Max Heinemeyer, Dissecting the SolarWinds Hack without the Use of Signatures, Darktrace (Jan.
7, 2021), http://www.darktrace.com/en/blog/dissecting-the-solar-winds-hack-without-the-use-ofsignatures/.
15
See EINSTEIN, U.S. Cybersecurity & Infrastructure Security Agency (last accessed Feb. 8, 2021),
https://www.cisa.gov/einstein.

Gregory C. Wilshusen, DHS Needs to Enhance Efforts to Improve and Promote the Security ofFederal and Private-Sector Networks. Government Accountability Office, (April 24, 2018), http://www.
gao.gov/assets/700/691439.pdf. See also Information Security: DHS Needs to Enhance Capabilities,
Improve Planning, and Support Greater Adoption of Its National Cybersecurity Protection System,
Government Accountability Office (Jan. 28, 2016), https://www.gao.gov/assets/680/674829.pdf.

16

17
President’s FY 2021 Budget Request to Congress for the National Institute of Standards andTechnology, National Institute of Standards and Technology (2020), http://www.nist.gov/system/files/
documents/2020/02/11/FY2021-NIST-Budget-Book.pdf.
18
Implications of Artificial Intelligence for Cybersecurity: Proceedings of a Workshop, National
Academies of Sciences, Engineering, and Medicine at 17-19 (2019), https://doi.org/10.17226/25488.
19
Implications of Artificial Intelligence for Cybersecurity: Proceedings of a Workshop, National
Academies of Sciences, Engineering, and Medicine at 12-19 (2019), https://doi.org/10.17226/25488.
20
Implications of Artificial Intelligence for Cybersecurity: Proceedings of a Workshop, National
Academies of Sciences, Engineering, and Medicine at 15 (2019), https://doi.org/10.17226/25488.

Report, U.S. Cyberspace Solarium Commission (March 2020), https://www.solarium.gov/report.
[hereinafter CSC Report]

21

22This recommendation modifies an existing Cyberspace Solarium Commission recommendation inorder to reduce the risk of creating perverse incentives to avoid enforcement. See recommendation
4.2 and 4.4.4, CSC Report at 76, 83.
23

See recommendation 4.4.4, CSC Report at 83.

24

See recommendation 4.4.3, CSC Report at 82.

25

See recommendation 4.5.3, CSC Report at 87.

26
S. 3288, International Cybercrime Prevention Act of 2018, 115th Congress (2018), https://www.
congress.gov/bill/115th-congress/senate-bill/3288/text.

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Blueprint for Action: Chapter 1 - Endnotes
Report of the Attorney General’s Cyber Digital Task Force, U.S. Department of Justice at 124 (July2, 2018), https://www.justice.gov/archives/ag/page/file/1076696/download.

27

p
289

28

See recommendation 6.1.2, CSC Report at 114.

29

See recommendation 1.1, CSC Report at 32.

30

See recommendations 1.1.1 and 1.1.2, CSC Report at 32.

31

See recommendation 5.4, CSC Report at 87.

32

See recommendation 4.6, CSC Report at 88.

E M E R G I N G T H R E AT S I N T H E A I E R A

33

See recommendation 4.3, CSC Report at 78.

34

CSC Report, 78.

35

See recommendation 4.1.1, CSC Report at 75.

36

See recommendation 5.2, CSC Report at 101.

The President’s National Infrastructure Advisory Council detailed a similar recommendation to
make cyber intelligence more actionable. Transforming the U.S. Cyber Threat Partnership, President’s
National Infrastructure Advisory Council at 8 (Dec. 12, 2019), https://www.cisa.gov/sites/default/files/
publications/NIAC-Working-Group-Report-DRAFT-508.pdf.

37

38

CSC Report at 102

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Chapter 2: Foundationsof Future Defense
Blueprint for Action

The Department of Defense (DoD) lags far behind the commercial sector in integrating new
and disruptive technologies such as Artificial Intelligence (AI) into its operations. Technical,
bureaucratic, and cultural challenges must be overcome to adopt AI to maintain the U.S.
military advantage. By 2025, the DoD must put in place the foundations for widespread AI
adoption, by: 1) Building the technical backbone; 2) Training and educating warfighters;3) Accelerating adoption of existing digital technologies; 4) Democratizing development ofAI; and 4) Investing in next-generation capabilities. To the maximum extent possible, these
efforts should be coordinated with the Intelligence Community (IC) and other partners
across the national security community.1
Recommendation

Recommendation: Drive Change through Top-Down Leadership.
Maintaining the defense advantage in an AI-enabled future will require top-down leadershipto overcome organizational barriers and create strategic change. Critically, civilian andmilitary leaders across the DoD and the IC must coordinate more closely, aligning priorities,
resources, and policies to speed technology adoption and research breakthroughs.
Actions for the Department of Defense and the Office of the Director of National Intelligence:• Establish a Steering Committee on Emerging Technology, tri-chaired by the
Deputy Secretary of Defense, the Vice Chairman of the Joint Chiefs of Staff, and
the Principal Deputy Director of National Intelligence.2
o The Secretary of Defense and Director of National Intelligence should issue a
directive immediately establishing the senior oversight committee listed above.
o The Steering Committee on Emerging Technology provides a forum to drivechange, focus, and action on emerging technology that otherwise would not beprioritized. It will enhance intelligence analysis related to emerging technology;
connect strategic vision to organizational change; focus concept and capability
development on emerging threats; guide defense investments that ensure
America’s strategic advantage against near-peer competitors; and provide theauthority to drive technology adoption and application by the Department.
• Assign the tri-chaired Steering Committee on Emerging Technology responsibility
for overseeing the development of a Technology Annex to the next National
Defense Strategy.3p291

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Action for Congress:• In the National Defense Authorization Act (NDAA) for FY2022, establish a Steering
Committee on Emerging Technology and National Security Threats and designatethat it be tri-chaired by the Deputy Secretary of Defense, the Vice Chairman of the
Joint Chiefs of Staff, and the Principal Deputy Director of National Intelligence.
Recommendation: Build the Technical Backbone

Recommendation

Integration of AI into DoD operations requires urgent investment in a modern digital
ecosystem that will enable ubiquitous development and fielding at all levels—from theheadquarters to the tactical edge. It is essential to establish a technical foundation that:
1) Provides access to leading cloud technologies and services for scalable computing;2) Enables the sharing of data, software, and capabilities through well-documented andhardened application programming interfaces (API) with proper access controls; and 3)
Gives all DoD developers and scientists access to the tools and resources they need todrive new AI capabilities. To this end, the figure below depicts the ecosystem managed
as a multilayer stack of services, accessed through common interfaces and providing
shared access to essential AI building blocks of data, algorithms, tools, trained AI models,and compute. This should be realized through a federated approach, building on existing
resources and pathfinder efforts.4
The key elements that comprise the envisioned AI digital ecosystem are:• Contributors and Users. A diverse, distributed network that includes development
teams working at the tactical edge and at headquarters levels; private sector partners
contributing trained models and applications; academic researchers working on open
challenge problems; researchers working within a DoD lab; or international allies orpartners co-developing interoperable AI capabilities.
• Common Interfaces. A service-oriented architecture where resources at each levelof the stack are accessed and maintained through common APIs based on industrystandard protocols.
• Authentication. Enhancing both the sharing and the safeguarding of resources througha uniform policy and practice for managing authoritative, shared user attributes acrossclassification levels to control who will build, use, or share AI building blocks.5• Applications. Discoverable and accessible AI solutions ready for fielding throughprovisioned platform environments.6• Platforms. Environments that support development, testing, fielding, and continuous
updating of applications to diverse sets of contributors and users.7 These platforms
include workflows and processes supporting the DevSecOps8 life cycle, MLOps9 formachine learning pipelines, and digital engineering.10• Software. Federated software architecture11 linking distributed repositories hosted acrossthe Department by mission components, their software factories, and service labs,
making software discoverable through a catalog.12 Includes AI algorithms, data analysis
tools, and tools supporting test and evaluation, verification and validation (TEVV)13
as well as processes and tools to support continuous Authorization to Operate (ATO)
frameworks and reciprocity.14
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BLUEPRINT FOR ACTION: CHAPTER 2• Data. Federated and secured data architecture linking distributed repositories acrossthe department hosted by mission components, service labs, and enterprise programs,
making data discoverable through a catalog.15 With appropriate access controls, this will
facilitate finding, accessing, and moving desired data across the Department16 including
data sets, associated data models, and trained AI models along with supporting
documentation.17• Hardware Infrastructure. Networking and communications backbone to transportecosystem resources, particularly data, and provide seamless access and interchange
between cloud computing and storage services.
AI Digital
Ecosystem.

OPERATORDEVELOPER
IN THE FIELDDEVELOPER
AT AN OFFICERESEARCHERACCESS TO
ALL LEVELSBY COMMONINTERFACESAUTHENTICATIONAAPPLICATIONSWorkflows
DevSecOps

Datasets
TrainedAI Models

A

A

A

A

A

A

SOFTWARE CATALOG

PLATFORMDATA CATALOG

Algorithms
Tools

A

SOFTWARE REPOSITORIESDATA REPOSITORIESHARDWARE INFRASTRUCTURE
Network
ComputingStorage
Devices

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293

A

A

F O U N D AT I O N S O F F U T U R E D E F E N S E

To accelerate the process of building on existing resources and pathfinder efforts, and toincrease interoperability in the short term, DoD should determine a governance structureand develop necessary policies and guidance, draft a reference design, and make thetechnical investments in the network and in platform environments. Implemented correctly,
the digital ecosystem will ensure force-multiplying common access and interoperability.
The Blueprint for Action framework outlined below marries top-down coordination anddirection with bottom-up mission implementation to realize an enterprise-wide ecosystem
in a manner that does not slow or stymy innovation, but rather incorporates new capabilities
at the speed of innovation and mission requirements.
Actions for the Department of Defense:• Establish Digital Ecosystem Leadership and Governance.
o The Secretary of Defense should direct the establishment of an enterprise-wide
digital ecosystem to support capability development to maintain the technologicalsuperiority of the United States military.
■■

To ensure senior leader oversight and sustained resourcing, the Secretaryshould assign the Steering Committee on Emerging Technology the
responsibility to oversee the implementation and sustainment of theecosystem.
The Secretary should assign the DoD Chief Information Officer (CIO) as the
Executive Agent responsible for the ecosystem design, development, andoperation.

o The Steering Committee on Emerging Technology, coordinating with the DoD
CIO, DoD Comptroller, Director of Cost Assessment and Program Evaluation, andappropriate acquisition and programming representatives from the military services,
should produce a funding plan18 for the ecosystem within 90 days of the Secretary’s
direction.
o The DoD CIO should form and chair an enduring digital ecosystem implementation
working group19 to establish and maintain an open architecture, an evolving
reference design, governance structure, and processes to include management
and authorization for ecosystem functions and growth. The Steering Committee
on Emerging Technology will ensure strategic direction and coordination,and pathfinder organizations will provide bottom-up and mission-oriented
implementation.20■

The working group should report to the Steering Committee on Emerging
Technology, add members when appropriate, and include representatives
from:21• The Office of the DoD Chief Data Officer (CDO)• Component CIOs and CDOs
• The Joint Artificial Intelligence Center (JAIC)• The Office of the Under Secretary of Defense for Research & Engineering(OUSD (R&E))

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BLUEPRINT FOR ACTION: CHAPTER 2• The Office of the Under Secretary of Defense for Acquisition &Sustainment (OUSD (A&S))• The Office of the Under Secretary of Defense for Intelligence & Security(OUSD (I&S))• Service Acquisition Executives• The Office of the Director of Operational Test and Evaluation (DOT&E)• The Director of the Defense Advanced Research Projects Agency(DARPA)• Digital ecosystem pathfinders, including but not limited to, the Air Force’s
PlatformOne, Kessel Run, Space CAMP, the Navy’s Black Pearl, the Army’s
CReATE, ADVANA, and the Army Futures Command Software Factory22• Develop and Mandate Participation in a Digital Ecosystem Governed by an Open
Architecture and Reference Design.
o Within 12 months of the Secretary’s direction to establish the ecosystem, the DoD
CIO should work with the implementation working group to develop and publish an
open, interoperable architecture23 built on common interfaces based on industrystandard protocols along with an evolving reference design.24■■

The open architecture and reference design should be owned by the DoD CIO
and reviewed quarterly and updated through the working group.
An unclassified version of the open architecture and reference design shouldbe published publicly for commercial capability providers.

o The Secretary of Defense should issue a memorandum that requires all new joint
and service programs to participate in the digital ecosystem and adhere to the open
architecture.25 This should include a requirement that all existing programs develop
a plan to participate and become interoperable with the digital ecosystem wherever
possible by 2025.
■

Through the Joint Requirements Oversight Council (JROC), the Vice Chairmanof the Joint Chiefs of Staff (VCJCS) and USD (R&E)26 should ensure that all
joint and service programs designed to meet joint capability needs adhere to
the digital ecosystem open architecture.27

o The DoD CDO, acting in coordination with the DoD Data Council, should ensurethat the Data Strategy Blueprints for Action Annex developed by each Component
under the DoD Data Strategy adhere to the digital ecosystem open architecture.
o The USD (A&S) should update the guidance governing the formatting requirements
for deliverable data in contracts to be well-documented,28 “non-proprietary formats
designed for interoperability.”29o The Steering Committee on Emerging Technology should lead an effort with the
IC to assess additional ways to accelerate implementation and leverage the digital
ecosystem, including designating service providers to proliferate applications
across the enterprise and make them available for integration into complex mission
solutions.30 Wherever possible, the digital ecosystem’s open architecture should
leverage and interoperate with proven solutions from the IC such as the Information
Technology Environment recommended in Chapter 5 of this report.

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F O U N D AT I O N S O F F U T U R E D E F E N S E• Establish a Strategic Data Node.
o The DoD CDO should make it a priority to create a linked, large-scale, cloud-based
data repository (i.e., a node within the digital ecosystem) adherent to the dataservice interfaces specified in the ecosystem’s open architecture. This would be a
critical step to enable distributed development efforts by providing AI development
teams secure access to authoritative data from diverse mission sets and functional
areas, and serve as a prototype for the digital ecosystem reference design.31■

The CDO should create this strategic data node by integrating digital
ecosystem interoperability into the DoD ADVANA system32 and prioritize
construction of enterprise data sets as recommended below.

• Expand the Network and Communications Backbone to the Digital Ecosystem.
o The Department should fully fund its network and communications modernization
effort as outlined in the DoD Digital Modernization Strategy,33 require the DoD CIO
to factor this into their list of highest priorities, and hold the DoD CIO accountablefor expediting critical upgrades.
• Create a Marketplace to Promote Democratization of AI Building Blocks.
o The DoD CIO, in accordance with the digital ecosystem governance and reference
design addressed above, should create an AI marketplace for strategic exchanges ofthe essential AI building blocks.34 The marketplace should include:
■

■

■

■

SoftEx – GitLab-like35 software repository system36 hosting AI algorithms,
TEVV tools,37 hardened AI software stacks, etc.
DataEx – a federated data repository system38 of AI-ready data sets,
documentation, and associated data models.39
ModelEx – a federated repository system of trained models40 generated fromvarious types of AI approaches and techniques, including statistical machinelearning.41
CloudEx – a cloud-agnostic, networked marketplace for pre-negotiatedcomputing and storage services from a pool of vetted cloud providers.42

o Trusted partners (inside and outside government) should be able to develop
solutions and products within secured environments of the ecosystem, offering
monetized access to users.43• Develop Prototypical Platform Environments within the Digital Ecosystem.
o The DoD CIO should work closely with the digital ecosystem pathfinder community
to build a set of tailorable development environments for training AI systems
using: data-driven statistical machine learning; the latest simulation and modelingcapabilities to support reinforcement learning (e.g., digital twinning within an
accurate world model); and complementary TEVV services.44
o The DoD CIO should work closely with the digital ecosystem pathfinder community
to implement a set of prototypical platform environments45 that support
development, testing, fielding, and continuous update of AI-powered applications
for diverse categories of contributors and users.46

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Action for Congress:• Prioritize funding for the Department’s digital ecosystem and associated activities.
o The Armed Services Committees should use the FY2022 NDAA to direct the
Department to develop a resourcing plan for the digital ecosystem that establishes,sustains, and incentivizes use of its various components as enterprise-wide,
enduring resources.
o The Committees should also authorize the obligation of funds to begin work on theecosystem.
Recommendation

Recommendation: Train and Educate Warfighters
Warfighters need the following capabilities to effectively build and use AI-enabled systems:• Data-informed decision-making: Data-informed decision-making uses data to generate
insights and act on them. Data-driven organizations often make decisions more quickly,
at lower levels in the organization, and with a stronger empirical foundation thanorganizations that rely primarily on intuitive or experience-based decision-making.47• Computational thinking: Service members need to better understand how to use
information processing agents to perform beneficial calculations that could not be donequickly or efficiently by a human, rather than just representing human thinking in adigital format.
• Maker culture: Service members of all ranks and occupations need regular contact with
AI-enabled machines, and should be able and encouraged to experiment with andparticipate in the development of new tools.
• Human-machine teaming: Military leaders need to understand how to effectively
provide input to machines, interpret machine outputs, and critically, when to trust or not
trust machine outputs.48• Organizational transformation: Leaders need to understand when and how to integrate
AI-related tasks into their organization’s priorities, allocate resources needed to build
and maintain the AI stack, oversee the deployment and scaling of new systems, and how
to effectively interact with and support the careers of their technical experts.
Component 1: Integrate Digital Skill Sets and Computational Thinking into Military Junior
Leader Education.
Military junior leaders need to understand enough about AI to manage and operate AIenabled organizations responsibly and effectively. Commanding and leading AI-driven
systems and humans are very different fields. Leadership is even more complex inorganizations that combine human and AI elements. The below skill sets will equip juniorleaders with the fundamental skills needed.
Problem Definition and Curation. Military leaders need to understand problem curation,or the process of discovering the causal mechanisms that lead to problems, associatedissues, stakeholders, and potential minimum viable products.49 Poor problem definitionp297

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and curation can lead to projects that attempt to solve the incorrect problem, wasting
significant amounts of time and money. This is particularly true for AI. Not all problems
can be solved using the type of probabilistic reasoning performed by many algorithms, orwith limited data sets. Also, many problems with potential AI solutions can be solved withmuch easier, less-resource-intensive techniques. Military leaders that understand problem
curation will be better able to identify problems with potential AI solutions, and, just as
importantly, problems that AI will not help solve. This would not only help with the use of AI
but would also make junior leaders generally more productive.
A Conceptual Understanding of the AI Stack. The AI stack is a model that “provides a
streamlined approach to visualize, plan, and prioritize strategic investments in commercial
technologies and transformational research to leverage and continuously advance AI acrossoperational domains, and achieve asymmetric capability through human augmentation
and autonomous systems.”50 A conceptual understanding of the AI stack would reinforce
the importance of building structural solutions to data collection, management, curation,
installation of sensors, and other underappreciated topics, and reduce attempts to add AI
at the end of a project. It will also help military leaders better understand what part of theiradversaries’ AI to target to degrade its effectiveness.
Data Collection and Management. Junior leaders need to understand how to collectand manage data and to use systems that do the same in a manner that prepares it for
exploitation, and to operate in an environment where adversaries are doing the same. They
also need to understand the causes, effects, and ethical implications of data bias. Training
junior leaders to collect and manage data with the same degree of responsibility andexpertise that they use for medical care and equipment maintenance would accelerate thegovernment’s ability to create AI solutions, and to employ data-informed decision-making.
Understanding Probabilistic Reasoning and Data Visualization. Junior leaders need toknow enough about probabilistic reasoning and data visualization to understand theoutputs of their AI systems and their implications for a particular situation or environment.
This is critically linked to understanding when to trust and not trust a system’s outputs,
and other aspects of commanding and leading AI-driven systems. Notably, this does not
require leaders to perform computational statistics, just to understand their output, a muchless demanding task.
Data-informed Decision-making. To make data-informed decisions, leaders need tounderstand system thinking and critical thinking. System thinking combines all of theabove to create an empirical but incomplete understanding of factors influencing adecision, and how both their system affects their AI and how their decision will affecttheir system. Critical thinking will help leaders understand the limits of AI, and the limits ofdata-informed decision-making processes that are based on imperfect information. This
report references data-informed rather than data-driven decision-making because militaryleaders should never be bound by the imperfect information in front of them. Their criticalp298

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thinking, judgment, and intuitive understanding of both their system and their environment
will always have a critical role to play, even as it is informed by decision-making aids.
Action for Congress:• Require the military services to integrate digital skills and computational thinking
into pre-commissioning and entry-level training.
o The Armed Services Committees should use the FY2022 NDAA to require themilitary services to integrate understanding problem curation, the AI life cycle, datacollection and management, probabilistic reasoning and data visualization, anddata-informed decision-making into existing, pre-commissioning or entry-leveltraining for junior officers and training for non-commissioned officers within one
year of the passage of the legislation.
Action for the Military Services:• Integrate digital skills and computational thinking into pre-commissioning andentry-level training.
o The military services need to integrate understanding problem curation, the AI
life cycle, data collection and management, probabilistic reasoning and datavisualization, and data-informed decision-making into pre-commissioning or
entry-level training for junior officers and training for both junior and senior noncommissioned officers. The military services can accomplish this by creating
new modules or courses, or by integrating this training into existing training andeducation for commissioned and non-commissioned officers. Whenever possible,
this training should include the use of existing AI-enabled systems and tools.
Component 2: Integrate Emerging and Disruptive Technologies into Service-level
Professional Military Education.
While it is critical that military junior leaders better understand digital technology, militaryleaders must also understand how technology will affect warfare, their roles in theirorganizations, and how they should integrate new technology, both as they increase inrank and responsibility and as technology changes.
Action for Congress:• Require the military services to integrate emerging and disruptive technologies
into service-level Professional Military Education.
o The Armed Services Committees should use the FY2022 NDAA to direct the DoD
to require emerging and disruptive technologies courses for officers within one year.
The Armed Services Committees should also require the DoD to develop a training
plan that incrementally builds the necessary skills in its officer corps.
Action for the Military Services:• Integrate emerging and disruptive technologies into service-level Professional
Military Education.

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F O U N D AT I O N S O F F U T U R E D E F E N S Eo Course materials should address AI and other militarily significant emerging
technologies, as identified by the military services and the USD (R&E), incoordination with the national laboratories, Federally Funded Research andDevelopment Centers (FFRDCs), and University Affiliated Research Centers(UARCs).
o Course materials should include an introduction to the latest technology, thebenefits and challenges of adapting new technologies, how organizations
successfully and unsuccessfully adopt these technologies, and ethical issues
surrounding the uses of emerging technologies, including the impact of biases inthese technologies.
o As officers progress in rank, such courses should increasingly build the knowledge
base, vocabulary, and skills necessary to better understand new threats/challenges,
develop operational and organizational concepts, and incorporate technology into
operations and operational support.
o Military services should establish a mechanism that audits these courses annually
to ensure that emerging technologies have been properly identified, and that thenomenclature, lexicon, definitions, and course content match changes in emerging
technologies.
Component 3: Create Emerging and Disruptive Technology Coded Billets in the Departmentof Defense.
It is crucial that the DoD incentivize and increase the skill needed to introduce and field
emerging and disruptive technologies within the military officer corps. The joint qualification
process can serve as a model. The DoD already designates that certain critical billets mustbe filled by Joint Qualified Officers51 and different levels of joint qualification.52 To do this,the DoD should create emerging and disruptive technology designated billets for officers
that require an emerging and disruptive technology qualification prior to assignment anda process for military leaders to become emerging and disruptive technology qualified.
Emerging and disruptive technology qualified officers would add value in a number ofareas for the services, including: 1) Assisting with acquisition of emerging technology; 2)
Helping integrate technology into field units; 3) Developing organizational and operational
concepts; and 4) Developing training and education plans.
Action for Congress:• Require the Department of Defense to create emerging and disruptive technology
critical billets that must be filled by emerging technology certified leaders.
Actions for the Department of Defense:• Create billets that require officers to become emerging and disruptive technology
certified before serving in the positions.
o The Office of the USD (R&E) should define emerging and disruptive technologies.
o Billets include, but are not limited to, positions that develop military doctrine and/or operating concepts; positions within Force Structure, Resources, and Assessmentp300

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directorates; positions within Force Development directorates; and leadership
positions at the operational and tactical levels within the military services.
• Create a process for officers to become emerging and disruptive technology
certified.
o The process to become emerging tech certified would resemble the joint
qualification system.
o Officers should become emerging technology qualified by serving in emerging
technology focused fellowships,53 emerging technology focused talent exchanges,
emerging technology focused positions within government, and completing
educational courses focused on emerging technologies.
Organizing D o D for
AI Adoption.

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Recommendation: Accelerate Adoption of Existing Digital Technologies

Recommendation

The Department must have an integrated approach to AI and other emerging technologies
that ensures the U.S. military can continuously identify, source, field, and update
capabilities faster than our competitors. This requires more targeted investment in dualuse technologies, ensuring system adaptability through a more agile budget and oversightprocess, and streamlining the acquisition process to shed those rules and regulations
whose benefits are outweighed by the burdens imposed on the system. Critically, theDefense Acquisition System must shift away from a one-size-fits-all approach to measuring
value from the acquisition process. Adherence to cost, schedule, and performancebaselines is rarely a proxy for value delivered, but is particularly unsuited for measuring
and incentivizing the iterative approaches inherent in AI and other software-based digitaltechnologies. Unless the requirements, budgeting, and acquisition processes are aligned
to permit faster and more targeted execution, the U.S. will fail to stay ahead of potential
adversaries.
Component 1: Adopt Proven Commercial AI Applications for Core Business Processes.
Commercial AI applications for business processes can generate labor and cost savings,
speed administrative actions, and inform decision-making with superior insights. To realize
these benefits, DoD should initiate the digital transformation of its core administrative
functions.
Efforts to apply business AI depend on the availability of clean, organized data. Significant
resources are required to access, clean, and label enterprise data from the range of legacy
business platforms.
DoD should create opportunities for bottom-up identification of AI use cases by
incentivizing DoD organizations to deploy proven commercial applications tailored to their
specific mission needs. Promising categories of commercial AI include: 1) Knowledge
management applications such as intelligent search tools that index, retrieve, and display
an agency’s digital information, as well as collective intelligence and coaching tools that
accumulate and exchange tacit knowledge across an agency’s workforce; 2) AI-enabled
tools that analyze business information to identify patterns, develop insights, and inform
decision-making; and 3) Robotic Process Automation (RPA) tools including desktop
assistants, bots, and other personal productivity applications that automate individual
office functions.
Actions for the Department of Defense:• Prioritize construction of enterprise data sets across core DoD businessadministration areas.54
o The Deputy Secretary of Defense should:p302

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Assign the DoD CDO responsibility for working with institutional stakeholders
to develop enterprise data sets for human resources, budget & finance,
acquisition, logistics, retail, real estate, and health care.
Place special priority on the CDO building an enterprise data set that supports
portfolio management of investments in emerging technologies, spanningbudget requests, acquisition, contracting, and invoicing.55
Assign the JAIC to support the DoD CDO in developing new methods forgenerating higher quality data for each core business administration areaat the point of origin. This would include applying data tags that allow AIenabled cross domain analyses.56 As part of this effort, the JAIC should
also look to develop or procure AI tools that continuously extract tagged
information for analysis from enterprise data sets.
Ensure sufficient funding is included as part of the FY2023 budget request to
provide data engineering services.

o The Secretary of Defense should issue a department-wide directive mandating thereview and streamlining of policies and regulations wherever possible to increase
and accelerate data sharing across agencies, with proper protections, building on
the JAIC’s Gamechanger AI prototype57 to analyze and modernize the framework
within which data access rules are enforced.
• Launch a department-wide initiative to incentivize rapid deployment ofcommercial AI solutions for business functions.
o The Deputy Secretary of Defense should assign the JAIC, in its role as the
Department’s AI accelerator,58 to administer allocation of matching funds, monitor
and assess results, and disseminate best practices and lessons learned for the
deployment of AI solutions for knowledge management, business analytics,
and RPA across the Department, defense agencies, Services, and CombatantCommands.59
o The Secretary of Defense should issue a DoD directive mandating added flexibility
and/or streamlining of policies and regulations wherever possible to increase andaccelerate acquisition and deployment of commercial AI software, building on the
JAIC’s Gamechanger AI prototype to analyze and modernize the existing rules
framework.60
o The Deputy Secretary of Defense should establish a $100 million fund under the
management of the JAIC to accelerate procurement and integration of commercial
AI solutions for business applications. This would be used to provide matching
incentive funds for agencies contracting with commercial AI vendors with approved
solutions. The Deputy Secretary should also:
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Consider leveraging the defense-wide review process detailed below to
identify and reprogram sufficient funds to stand-up this fund by the beginningof FY2022.
Ensure sufficient funding is included as part of the FY2023 budget request to
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Action for Congress:• Congress should provide $125 million as part of the FY2023 defense
appropriations act for developing enterprise-wide data sets, and $100 million for
the fund to accelerate procurement and integration of commercial AI solutions for
DoD business functions.
Component 2: Network Digital Innovation Initiatives to Scale Impact.
Too often the Department’s enthusiasm for innovation comes at the expense of impact
and scale. Dozens of innovation offices across the Department and Services develop,
transfer, and apply cutting-edge technology for national security uses.61 However, many of
the initiatives that are focused on bridging the gap with the technology sector, to include
AFWERX, NavalX, Army Applications Laboratory (AAL), and the Defense Innovation Unit
(DIU), operate in silos and are limited in their ability to scale solutions. These pockets ofsuccessful bottom-up innovation have achieved some promising results, but disparate
activities cannot translate to strategic change without top-down leadership to synchronize
efforts and overcome organizational barriers.62 The Department should “network”
programs that work to source cutting-edge technology solutions under the banner of“digital innovation initiatives” to execute a “go-to-market strategy” for digital technologythat is supported at the highest levels of the Department.
Actions for the Department of Defense:• Designate an Executive Agent to coordinate the Department’s digital innovation
initiatives.
o The Secretary of Defense should designate USD (R&E) as Executive Agent for the
Department’s digital innovation initiatives63 and direct that USD (R&E) to coordinate
closely with USD (A&S), DoD CIO, and DoD CDO to carry out the responsibilities
associated with this role.64
o As Executive Agent, USD (R&E) should facilitate access to resources,65 provide
strategic guidance, and offer other forms of institutional support to enable
innovation organizations to execute their current mandates more effectively, without
infringing on autonomy or inhibiting bottom-up experimentation.66 USD (R&E)
should work with the DoD CIO, the DoD CDO, and USD (A&S) as well as otherinstitutional stakeholders as appropriate, to:■■■Develop a common digital platform for digital innovation initiatives to sharedata and procurement and development best practices,67 track ongoingprojects, connect with DoD program offices, and identify other means ofcollaboration.
Harness business AI tools to eliminate stovepipes and gain sharedunderstanding of the digital innovation community, including investments andcustomers.68
Identify and implement other metrics for the digital innovation initiatives toreport as necessary, so long as they are lightweight and automated to the
maximum extent possible.

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BLUEPRINT FOR ACTION: CHAPTER 2• Develop a “go-to-market” strategy for digital technology.
o USD (R&E) and USD (A&S) should issue a joint memo outlining a “go-to-market”
strategy for digital technology to guide innovation organizations to pursue common
objectives, based on the Technology Annex described below. This approach would
coordinate efforts for effect and reduce duplication of effort, while preserving room
for trial and experimentation with unexpected technologies or applications that
could inform new operational concepts.69
o The Department should back this strategy with significant resources and top-down
support. As described further in Chapter 11 of this report, DoD should set a target
of increasing its contracts with early-stage technology firms by four times overthe five-year Future Years Defense Program. To meet this goal, the Department
should increase the procurement budgets of innovation organizations and other
DoD entities to which innovation organizations hand off successful prototypes for
production, as appropriate.
o USD (R&E) should conduct annual investment portfolio reviews of digital innovation
initiatives to assess alignment with strategy70 and report findings to the Steering
Committee on Emerging Technology.
• Optimize operations to enable transition and scaling of AI solutions.
o USD (R&E), in partnership with USD (A&S), should assist innovation organizationsin providing contracted vendors access and resources to build, deploy, and assure
AI solutions often and at scale.71 In developing vendor contracts and planningcustomer journeys, digital innovation initiatives should consider the methods andmeans to:
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Ensure that data access and data security requirements are included incontracts for AI systems that depend on data for training or operations.
Provide consistent access to end users as part of AI development processes
and throughout the life cycle of the AI algorithm; and capture in contract
terms.
Include AI testing and evaluation consideration as part of every development
agreement.
Dedicate people and processes to onboard non-traditional vendors, migratethem onto the right networks and sandbox environments, and assist them insecuring ATO.72
Connect prototype contract recipients with DoD customers early in thetechnology development process and match program dollars with additional
funding (SBIR, dedicated scaling funds, etc.) wherever possible.73
Identify new opportunities for defense primes to team with non-traditionalfirms to adopt AI capabilities more quickly across existing platforms.74

o USD (R&E) should work with USD (A&S) to develop common reporting requirements
to measure the impact of digital innovation initiatives, building off of ongoing
efforts at DIU.75 Collection of this data should be automated to the maximum extent
possible and communicated routinely to Congressional defense committees.
Reporting should consider:p305

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Expansion of NSIB: Number of awards made to companies with no previous
DoD experience and percentage of these that receive follow-on contacts; ornumber of companies that receive recurring government revenue for first timeand funding stability over consecutive quarters.
Rate of Transition: Number of companies that receive follow-on production
contracts.
Rate of Scaling: Number of prototype contract recipients that reach $10
million, $50 million, $100 million, $500 million, and $1 billion in total DoD
contracts annually.
Reach of Products: Number of users76 that are benefiting from the product in
one year, three years, 10 years, etc.
User Experience: User feedback on the product (scale 1-10).
Company Acquisition Process Experience: Company feedback on the newacquisition process (scale 1-10).
Operational/Enterprise Impact: Actual or projected operational or fiscal returnon investment (e.g., initiative addressed an operational gap; innovative RPA
reduced production time or man-hours).

Component 3: Expand Use of Specialized Acquisition Pathways and Contracting
Approaches.
AI technologies are incompatible with the lengthy, linear processes typical of traditional
DoD capabilities acquisition.77 Recent policy reforms such as the rollout of the Adaptive
Acquisition Framework78 (AAF) and associated resources—such as the Contracting
Cone79 —are positive steps to move the Department away from a “one-size-fits-all”
approach to acquisition. However, use of the specialized pathways and authorities80
within the Framework is inconsistent and disincentivized.81 The traditional acquisition
process remains the default for most acquisition professionals—many of whom are neither
incentivized nor properly equipped to make use of the full resources at their disposal
through the Framework.
To accelerate delivery of AI-enabled technologies to the warfighter and increase theiroperational relevance, DoD must build the capacity to use the full breadth of acquisition
pathways and contracting approaches.82 Acquisition professionals must have a sufficientunderstanding of digital and emerging technologies in order to thoughtfully apply thesetools. Given the speed of advancements in AI and other software-based technologies, thisrequires a shift to a continuous learning mindset and a different approach to training for
acquisition professionals in which the target metric for success is not course completion,
but rather the ability to apply what is learned and impact mission outcomes. DoD should
coordinate acquisition workforce training initiatives relative to digital and emerging
technologies ongoing across the Department and continuously assess acquisition
workforce capability needs. Importantly, the DoD must also ensure acquisition personnel
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as a community of experts that illustrate how specialized authorities can be used to deliver
best of breed technologies.
Actions for the Department of Defense:• Accelerate training of acquisition professionals and senior leaders on the AAF,
Contracting Cone, and Digital Technologies.
o The Secretary of Defense should develop a set of best practices in the use of
new acquisition pathways83 and direct USD (A&S) and Component Acquisition
Executives to train the right acquisition professionals and DoD senior leaders andexecutives on the AAF, the Contracting Cone, and best practices for the use of
these flexibilities, within one year.
o USD (A&S) should also work closely with USD (R&E), the JAIC, the Service
Acquisition Executives, and the Component Acquisition Executives to implementa coordinated approach to training acquisition professionals and senior leaders
on cross-functional specialties relative to emerging technologies. The approachshould amplify and harmonize ongoing workforce training efforts84 related to AI,
data analytics, software, and digital engineering and look to leverage training or
courses that can be procured off-the-shelf or as a service.
• Leverage public-private talent exchanges to infuse technical expertise into theacquisition corps.85
o The Secretary of Defense should direct that acquisition professionals are consideredamong the highest priority to participate in public-private talent exchanges.86• Establish enterprise learning platforms, course catalogs, and knowledge
management tools for acquisition personnel and make them available Departmentwide.87
o USD (A&S) should invest in and scale appropriate learning platforms, course
catalogs, and knowledge management tools and create incentives for their use by
FY2022. These resources should catalog available training88 and best practices89
and make relevant experts and specialists discoverable for acquisition professionals
Department-wide.
• Continuously assess existing acquisition workforce capabilities and evolve training
for acquisition professionals.
o The Secretary of Defense should direct that USD (A&S) work with the Service
Acquisition Executives, Component Acquisition Executives, USD (R&E), and the
JAIC to ensure curricula and approach to training90 for acquisition professionals is
consistently and appropriately updated to support the Technology Annex to the
National Defense Strategy, as described below.
Action for Congress:• Authorize the use of a rapid contracting mechanism for the software acquisition
pathway.
o The Armed Services Committees should direct the Secretary of Defense to develop
a rapid contracting mechanism to support the AAF’s software acquisition pathway.91
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■

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A value-based price evaluation model.
An independent, non-advocate cost estimate developed in parallel withengineering and leveraging agile cost estimation best practices.
Performance metrics intended to measure value that can be automaticallygenerated by users and shared as requested by DoD officials andcongressional defense committees.

Component 4: Modernize the Budget and Oversight Processes for Digital Technologies.
The DoD’s budget process requires that funds be requested two years in advance of
their execution and focuses planning within the five-year Future Years Defense Plan
(FYDP). Resources are allocated to program elements (PEs) that are defined at the systemlevel92 and based upon cost buildups for pre-determined and highly specified system
requirements.93 In addition, the life-cycle-phased appropriation categories94 that govern
the DoD budget structure run counter to the iterative process inherent to AI and othersoftware-based technologies.95
This construct creates a paradigm unfriendly to the speed, adaptation, risk-taking, andjoint force cohesion necessary to compete in an AI-enabled threat environment. Seniorleaders champion the need to experiment96 and “fail fast,” but the budget process prevents
the allocation of funds without a justification clearly tied to program objectives. At thesame time, the DoD requirements process—responsible for formulating the basis of those
program objectives—assumes a linear and sequential relationship between requirements
and technology.97
To adapt faster than our adversaries, DoD must have a requirements and budget process
that: 1) Prioritizes joint force capabilities and aligns resources accordingly; 2) Enables
experimentation, iteration, and continuous development—especially for AI and digitaltechnologies where persistent user feedback is critical; and 3) Balances speed, scale,and risk depending on the technology or capability being delivered.
Implementation of the large-scale institutional changes required to achieve this vision willtake time and equal commitment from both DoD and Congress. In the near term, DoD
and Congressional leaders should focus on generating mutual trust by establishing pilot
programs to demonstrate the impact of reforms to the budget and requirements process
relative to AI. The inclusion of support for the Department’s Budget Activity 8 pilot programin the FY2021 defense authorization and appropriations acts represents positive progressto this effect.98
Below are recommended steps that DoD and Congress should take immediately and overthe longer term to create a modern budget and requirements process that supports theapplication of AI at speed and scale.

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Immediate Actions for the Department of Defense:• Reorient the Joint Requirements Oversight Council (JROC) process to focus on
Joint and Cross-Domain Capability.
o The Chairman of the Joint Chiefs of Staff should appoint the USD (R&E) Co-Chair
and Chief Science Advisor of the JROC.99
o The Chairman of the Joint Chiefs of Staff should direct that the JROC charterbe updated to reflect USD (R&E) as Co-Chair and Chief Science Advisor withresponsibility for:■■

Delivering technology assessments and trend reports that inform JROC
deliberations on future military requirements; andValidating the technical feasibility of requirements developed by the services
and ensuring they comply with the reference design for the digital ecosystemrecommended above.

• Make supplemental funding available to drive operational prototyping, scale, andtransition of AI technologies.
o The Secretary of Defense should establish a dedicated AI fund as a pilot underthe management of USD (R&E) to mature, operationally prototype, and transition
exceptionally promising AI-enabled technologies that align with applications
identified in the Technology Annex as described below. In doing so, the Secretary of
Defense should direct:■

Inclusion of approximately $200 million for the fund in the FY2022 budget
request.

o USD (R&E), in collaboration with the JAIC and the military services, should establish
clear metrics for success and a time horizon upon which to stand-up additional
similar funds for specific technologies or capabilities.
• Accelerate efforts100 to implement a portfolio management approach forrequirements and budget.
o The Deputy Secretary of Defense should produce a proposal for consideration in
the FY2022 defense authorization and appropriation bills to establish a pilot to test
a portfolio management approach for requirements and budgeting for at least one
joint capability area, such as Command and Control, in FY2023.101 The proposal
should:
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Establish a reasonable ceiling value for the portfolio.
Consider consolidation of program elements and the creation of a Program
Executive Office or other organizational entity empowered to resource andoversee programs designed to meet the joint capability need.
Request reprogramming authority to drive a “fail fast” mentality, promote
experimentation and early prototyping, and quickly integrate new capabilities.
Provide recommendations on adjusted reporting guidance and justification
documents, including metrics and mechanisms,102 that will allow Congress to
conduct appropriate approval and oversight.

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o The Deputy Secretary of Defense should also produce a separate proposal toestablish a pilot to test mission-focused budgeting and appropriations (e.g., a
Mission Element). The proposal should be developed in coordination with a
Combatant Command and organized around a high-priority operational challenge
as identified by the Joint Staff. It should:■■

Consider more flexible funding mechanisms, including reprogramming
authorities, applied across existing, relevant service programs to promote
digital modernization and integration of AI technologies, interoperability, andnew development or prototyping efforts for the specific operational challenge.
Provide recommendations on adjusted reporting guidance, and justification
documents, including metrics and mechanisms, that will allow Congress to
conduct appropriate approval and oversight.

Immediate Actions for Congress:• Update Title 10. Section 181 to designate USD (R&E) Co-Chair and Chief Science
Advisor to the JROC.
• Direct the Secretary of Defense to establish the dedicated AI fund.
o Congress should include a provision in the FY 2022 National Defense Authorization
Act directing the establishment of an AI fund under USD (R&E) and appropriate atleast $200 million to support it as a pilot.103• Support the continuation of the Budget Activity 8 pilot program in FY2022 anddirect the Department to add an S&T project to the pilot programs.
o Congress should continue to support the DoD software and digital technologies
pilot program designed to allow for flexibility in funding the full life cycle ofdevelopment, procurement, deployment, assurance, modifications, and continuous
improvement for digital technologies.104
o Congress should support DoD expanding the pilot in FY2022 to include a programthat explicitly supports an S&T development effort in order to effectively test
the impact of the single funding mechanism for the entirety of the AI life cycle,
including early-stage research and development.
Longer-term Actions for the Department of Defense and Congress:• Establish a single appropriation and budget structure for software and digitaltechnologies by FY2023.
o Congress should build on the BA 8 pilot and establish a single appropriation forsoftware and digital technologies that is exempt from the traditional programming orplanning process and can be used as a single source of funding for the full life cycleof capability delivery and continuous engineering.
o The Department and Congress should collaborate to develop and implement abudget structure and transparent oversight process for the new software and digitaltechnology appropriation that enables agile development of AI technologies andcapability portfolio management.105• Identify and implement successful portfolio- and mission-based budgeting
constructs at scale across DoD.
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o The Department and Congress should look to BA 8 as an example of how to apply asimilar approach to monitoring and scaling portfolio- and mission-based budgeting.
Based on metrics and oversight of the pilots over an appropriate timeline, DoD andCongress should determine what approaches to implement more broadly.
Recommendation

Recommendation: Democratize AI Development
An AI-enabled threat environment requires our forces to be able to develop and deploy
solutions nearly as quickly as threats arise. However, our forces frequently lack the
infrastructure, tools, talent, and support to solve their challenges locally and with modern
technology.106 The JAIC cannot develop and proliferate AI applications for every user groupor mission area within the DoD. To accelerate adoption of AI, the Department must create
the technical infrastructure and organizational structures that pair top-down strategy withbottom-up development.

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D emocratize
D evelopment.

AI delivery teams at
Combatant Commands

T A C
T I

Development teams
embedded with
operational units

C A
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S

Y

S

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JAIC
J O INT AI
CENTER
A I

D I

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I T

A

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C

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A C C E L E R A T O R

Subject matter expertiseStandards and governance
Development tools
Applications
Contracting vehicles

Component 1: Leverage the JAIC as the Department’s AI Accelerator.
The JAIC should serve as the Department’s “AI accelerator” and central node for AIrelated information. In this role, the JAIC would maintain critical situational awareness of
AI stacks across the Department (i.e., options, including applications, available within thedigital ecosystem that mission owners can leverage to enable local development efforts)

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and provide the expertise and resources necessary to enable distributed development
efforts.
Actions for the Department of Defense:• Designate the JAIC as the Department’s AI Accelerator.
o The Deputy Secretary of Defense should issue a memorandum107 designating therole of JAIC as the DoD enterprise’s AI accelerator with responsibility for:
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Developing tailorable AI applications to address high-level, cross-domainchallenges and shared problems, and making them available through thedigital ecosystem as enablers for development teams across the enterprise.
Administering a matching fund to incentivize integration of commercial AI
solutions for business functions across the Department.
Collecting best practices (including best-of-breed AI applications) fromindustry, academia, and across the enterprise and making them accessible forthe broader DoD developer community.108
Providing AI subject matter expertise and assistance to DoD Components to
inform strategy, policy, and technical approaches. This would include:• Participating as a member of the Steering Committee on Emerging
Technology.
• Contributing to the development of a reference design for the DoD AI
digital ecosystem and associated governance policies.109• Advising on integrating the appropriate governance frameworks forresponsible use of AI into policies and procedures.110• Advising on TEVV policies and capabilities for AI.
• In coordination with the Under Secretary of Defense for Policy, serving as
the Department’s lead for AI-related international engagement.

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Developing a common AI TEVV framework,111 in coordination with DOT&E andany other appropriate stakeholders, that integrates testing as a continuouspart of requirements specification, development, deployment, training, andmaintenance and includes run-time monitoring of operational behavior.112
Identifying, procuring, and orchestrating AI development tools and making
them available through the digital ecosystem software exchange113 describedabove to enable distributed development efforts.114
Making available enterprise-wide contracting vehicles (e.g., Blanket Orderingor Purchase Agreements) for talent115 and AI technical services116 andcontinuously onboarding new companies.117
Coordinating with USD (R&E) on AI-related elements of the go-to-market
strategy discussed above.
Integrating with nation-wide initiatives within other agencies and departments,
as directed by the President.

F O U N D AT I O N S O F F U T U R E D E F E N S E• Build technical support capability.
o The JAIC should grow and train a staff of resident experts118 that can provide
support to users across the enterprise akin to an “AI help desk,” to include providing
technical and policy consultation and advice; implementing solutions for small
problems; and facilitating connections of support (for larger problems).119
Component 2: Embed AI development capabilities in support of operations.
The Department must ensure operators are paired with technologists at every echelon.
Doing so will institutionalize user-centered; agile development, improve the speed andoperational relevance of solutions delivered; and build trust and confidence in AI-enabled
systems. Implementation of the actions below will create a networked support structure toenable bottom-up AI development extending from the tactical edge to the JAIC.120
Actions for the Department of Defense:• Establish integrated AI delivery teams at every Combatant Command (CCMD).
o The Secretary of Defense should direct each Combatant Commander to standup an AI delivery team dedicated to developing and deploying AI applications to
support operational units.121
o Teams should be staffed with the appropriate talent to manage the full life cycleof AI solutions, including in disciplines such as data science, AI testing and modeltraining, software engineering, product management, and full stack development.122
AI Delivery teams should be responsible for:123■■■

Finding, tailoring, and fielding applications from the digital ecosystem (e.g.,
those developed by other CCMDs, Service software factories, or the JAIC).
Developing additional sustainable mission applications as needed.
Contributing new and tailored applications to the digital ecosystem for use
across the CCMD(s) to meet common challenges.

• Integrate forward-deployed development teams with operational units.
o Each Combatant Commander should develop and implement a plan for the
integration of forward-deployed development teams to act as the local customer
interface for the AI delivery team with each operational unit.124 Forward-deployed
development teams should:■■

Work side-by-side with warfighters to identify problems and opportunities that
could be met with AI applications.
Leverage the digital ecosystem to provision development environments
and tools to produce “quick wins” to improve capabilities and generate
efficiencies.125

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Recommendation

Recommendation: Invest in Next Generation Capabilities
The DoD must have an enduring process that clearly identifies, prioritizes, resources, andtracks126 critical technologies over multiple time horizons. This will drive an investment
strategy that pursues technology applications that close key capability gaps and optimize
current operational concepts, and simultaneously makes bets on disruptive technologies
to enable transformative capabilities and operational concepts over the long term.
Component 1: Increase investments in Science & Technology (S&T) and AI R&D.
To compete and win in AI-enabled warfare, the propagation of technology from core AI
research to broad AI applications must expand drastically.127 Across the board, increases
in all lines of AI research128 are called for, with particularly large increases in researchfunding required to advance key areas, such as developing methods for human-machineteaming and deploying trusted AI applications through rigorous methods for TEVV.
Action for the Department of Defense:• Commit to building budgets that invest at least 3.4%129 of the annual DoD budget
in S&T and allocate at least $8 billion for research and development of core AI.
o Particular focus should go toward strengthening the AI research budgets atorganizations where AI expertise is centered, such as DARPA, the Office of Naval
Research, the Air Force Office of Scientific Research, the Army Research Office, andthe Service Laboratories.
Action for Congress:• Support DoD budget requests for amplified funding of AI R&D and AI-related
initiatives.

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Graphic 2.7: Budget Analysis Chart.
Graphic 2.7: Budget Analysis Chart.
F O U N D AT I O N S O F F U T U R E D E F E N S E

Enhanced AI R&D Investment, FY 2015-2030
Enhanced
AI R&D Investment, FY 2015-2030
Source: Govini and NSCAI
Source: Govini and NSCAIEnhanced
AI R&D Investment,
FY 2015-2030
Current AI_status quoCore AI_enhanced
Source:and
NSCAI Core AI_enhanced
Current Govini
AI_statusquo

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AI adoption_enhanced

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AI$1.5B/yr.
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$40
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NSCAI recommendation: increase Core AI
spendingfrom $1.5B to $8B
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increaseCore
spending from $1.5B to $8B per year by 2025.

2024
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2027
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Core AI spending is comprised of RDT&E programs to develop AI applications such as machine learning/deep learning, collaborative behavior,computer vision; human-machine teaming, automated reasoning, robotic autonomy, automated data fusion, and self-healing networks. Together with

Core AI spending is comprised of RDT&E programs to develop AI applications such as machine learning/deep learning, collaborative behavior,Core
AI spending
is comprisedof
RDT&Eenables
programs
to develop
AI applications
such as machinelearning/
commerciallysourcedAI applications,coreautomatedAI spending
adoption.
We define
AI adoption
as the andsumself-healingof AI-enabledand AI-enabling
computer vision;
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robotic
autonomy,
automateddata fusion,
networks.
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learning,
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behavior,
computervision;Wehuman-machineteaming,
automatedreasoning,
investments(defined
below).
commerciallysourcedAI applications, core
AI spending
enables AI adoption.
define AI adoption as thesum of AI-enabledand AI-enabling
robotic
autonomy,
automated data fusion, and self-healing networks. Together with commerciallyinvestments(defined below).
sourced AI applications, core AI spending enables AI adoption. We define AI adoption as the sum of
AI-enabled programs develop (in the case of RDT&E programs) and field (in the case of procurement programs) the gamut of DoD warfighting and
AI-enabled
andincorporating
AI-enabling(defined
below).
business
systems,
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applications
for analyzing,
maneuvering,and many other
AI-enabled
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the caseof RDT&E programs)
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programs)monitoring,
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andtasks.
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AI spendingis usuallya small
percentageforof analyzing,these programs,
their system’s
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and themany otherincorporationcore AI. is usuallytasks. While AIofprograms
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theof DoD warfighting and business systems, incorporating Core AI applications
forAI-enabling
analyzing,
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sensing,andothertasks.
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to support
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AI spendingis usuallya smallof these
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at scale.
criticallydependent
upon the incorporation of core AI.

AI-enabling programs include technologies such as cloud computing and advanced microelectronics
required to support the deployment of effective AI capabilities at scale.

Component 2: Retire Legacy Systems Ill-Equipped to Compete in AI-Enabled Warfare.
In the face of new budget realities, the Department must undergo an aggressive portfolio
rebalance to ensure sustained room in its budget for emerging technologies like AI.130 This
will require DoD to make hard decisions on where to divest, and identify opportunities
and timelines to upgrade or phase out legacy systems, as it continues to invest in new
systems. However, the Department must also approach new systems differently. Rather
than continuing to build large, monolithic platforms while competitors invest heavily in
attritable systems, the DoD should focus on speed. DoD should drive investments into
rapid prototyping and modular system design to develop and field new capabilities at a
rate that allows U.S. forces to continuously out-adapt the adversary.

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Actions for the Department of Defense:• Institutionalize an enduring defense-wide review and decision-making process,131
prioritized to the threat, to divest of legacy systems.
o The Secretary of Defense should direct the Service Secretaries, USD (A&S), theDefense Agencies, and DoD Field Activities to evaluate the relevance and resiliencyof all platforms and systems against emergent threats, and ruthlessly divest fromsystems and platforms deemed too costly or ineffective to equip with AI or makecompatible with AI-enabled systems and architectures.132
o The Service Secretaries and USD (A&S), in comparing the risk/reward tradeoffs
between new versus old technologies and operating concepts, should leverage AI
technologies as decision support tools.
o The Director of CAPE should enforce decisions to divest or reduce funding throughthe program review process.
o The Service Secretaries and USD (A&S) should explore options for updating legacy
systems with leading-edge technologies to buy time for required long-term
modernization projects.
• Evaluate AI alternatives prior to funding new major defense acquisition programs.
o The Secretary of Defense should issue a memorandum directing that all new major
defense acquisition programs must conduct a thorough evaluation of AI alternatives
as part of their analysis of alternatives prior to funding for major defense acquisition
programs.133
o USD (R&E) and the JAIC should provide support to program offices conducting suchanalysis by providing subject matter expertise informed by technology scouting andan awareness of the capabilities in the R&D pipelines across the S&T enterprise.
Action for Congress:• The Congressional defense committees should support the Department’s harddecisions when presented with evidence that divestment or defunding can enable
a more competitive force posture.
Component 3: Create an integrated technical intelligence program134 and a supporting
community of practice.
To effectively leverage scientific and technological breakthroughs for competitive
advantage, DoD must have a sophisticated technical intelligence program that monitorsdevelopments as they progress from basic research to prototype to fielded capabilities,
understanding the R&D roadmaps of the private sector wherever possible. This intelligencemust be global in scale, monitoring emerging technologies in near real time, especially inthe rapidly evolving field of AI. The intelligence must be actionable, informing prioritizationof resourcing and providing decision-makers the ability to continuously update technology
roadmaps for our national security agencies.

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Such a technical intelligence program should provide inputs to the proposed Technology
Annex to the National Defense Strategy135 in three main areas: 1) An understanding of thecurrent and future threat capabilities in the R&D, production, and sustainment pipelines of
our adversaries; 2) An understanding of the current and future friendly capabilities in the
R&D, production, and sustainment pipelines of the U.S. government and allied partners;and 3) An understanding of emerging military and dual use technologies worldwide
available for integration into national security capabilities.136
Actions for the Department of Defense:• Transform the Strategic Intelligence Analysis Cell.
o USD (R&E) should reconceive the Strategic Intelligence Analysis Cell (SIAC)137
as a robust analytic hub that marshals DoD, IC, and other technology scouting
capabilities for strategic effect.138 The SIAC Director should report directly to the
USD (R&E).
o SIAC should convene an interagency technology scouting community of practicefrom the service laboratories, OSD (including DARPA and DIU), innovation initiatives
within the military services (such as AFWERX and AAL), the Departments of Energyand Homeland Security, university-affiliated research centers, federally funded
research and development centers, CCMDs, and international security partners.
This community of practice should:
■

■

■

■

Establish a federated approach to provide USD (R&E) with inputs to produce
and continuously update the Technology Annex.
Conduct analytic exchanges and wargames to assess future technology
scenarios and include AI to the maximum extent possible.139
Develop rigorous technology forecasting capabilities, leveraging best practices
from academia and the private sector.
Engage with industry and update requirements for technology scouting tools
and data.

o In order to leverage private industry more effectively, SIAC should maintainknowledge of private market investments relevant to the technologies andcapabilities outlined in the Technology Annex.
o In order to locate existing DoD capability gaps and potential solutions, SIAC mustreceive technical details at all levels of classification on current programs ofrecord from OSD (A&S) and the armed service’s acquisition executives, as well as
technical details on RDT&E programs from OSD (R&E) and the technology scouting
community of practice described above.
o SIAC should establish a technology fellows program, inviting organizations in thetechnology scouting community to nominate personnel for short-term (three- to
12-month) assignments with SIAC where they would work side-by-side with SIAC
analysts. This program should:■

Build interdisciplinary teams of technologists and warfighters to conduct indepth investigations of emerging technologies, initiating direct contacts withacademia and industry in addition to passive data collection.

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■

■

■

Circulate personnel through the tech fellows’ program into key roles inexperimentation and concept development activities across OSD and themilitary services.
Develop personnel with greater understanding of emerging technologies
across the national security community.
Leverage hiring authorities from the Public-Private Talent Exchange Program
and the Intergovernmental Personnel Act to include fellows from industry,
academia, and other government agencies to enhance access to non-DoD
research and perspectives.140

o SIAC should acquire or develop research tools for use by the technology scouting
community of practice, including AI-enabled analysis of large commercial
databases, classified threat intelligence, and the technology investment portfoliosof the United States Government and its allies.
Actions for Congress:• Congress should appropriate an additional $10 million to USD (R&E)’s budget for
the technology fellows program and AI-enabled technology scouting tools anddata.
Component 4: Develop a Technology Annex to the National Defense Strategy.
To identify where and how to direct scarce resources, the DoD should formulate its
investment strategy as a classified Technology Annex to the National Defense Strategy(NDS) produced by the Department’s Chief Technology Officer, USD (R&E). The Appendix
should: 1) Identify emerging technologies and applications required to solve the operational
challenges outlined in the NDS; and 2) Outline a clear plan for pursuing these technologies
and applications. This plan should account for existing technologies, including dual-use
commercial technologies, and drive rapid integration of these technologies to close nearterm capability gaps.141 The plan should also help inform the agenda for DARPA and the
DoD labs, by identifying disruptive technology elements and applications that warrant
longer-term, exploratory investments. Finally, the plan must take into account industry’s
comparative advantage in available R&D capital and include a consistent and transparent
approach to messaging defense technology priorities to build and broaden the industrialbase.142
Actions for the Department of Defense:• Develop a Technology Annex to the National Defense Strategy.
o The Secretary of Defense, with support from the Director of National Intelligence,
should issue a memo directing the Steering Committee on Emerging Technology
to oversee the development of a comprehensive classified Technology Annex
as a component of the next NDS and assign USD (R&E) as the Executive Agent
responsible for producing the Technology Annex.
■

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319

The Technology Annex should identify emerging technologies andapplications that are critical to enabling specific capabilities for solving theoperational challenges outlined in the NDS.

F O U N D AT I O N S O F F U T U R E D E F E N S E

o The Steering Committee on Emerging Technology described above should
ensure that the Technology Annex sets clear guidance that drives prioritization
and resourcing, while allowing enough flexibility for subordinate organizations to
implement that guidance as best suits their mission. At a minimum, the Technology
Annex should include:■■■

Identified intelligence support requirements, including how the IC analyzes
the global environment and monitors technological advancements, adversarial
capability development, and emerging threats.
Identified functional requirements and technical capabilities necessary toenable concepts that address each challenge.
A prioritized, time-phased plan for developing or acquiring such technicalcapabilities that takes into account R&D timelines, a strategy for public private
partnerships, and a strategy for connecting researchers to end users for early
prototyping, experimentation, and iteration.
• This should include roadmaps for designing, developing, fielding, andsustaining the technologies and applications to address the operational
challenges outlined in the NDS.
• These roadmaps should account for and leverage existing commercialoff-the-shelf/dual-use technologies and identify areas where defensespecific solutions are needed.
• The roadmaps should use quantitative technological forecasting methods
developed in academia and industry to identify disruptive technologies.

■

■

■

■

■

■

■

Identified additional or revised acquisition policies and workforce training
requirements to enable DoD personnel to identify, procure, integrate, andoperate the technologies necessary to address the operational challenges.
A prioritized, time-phased plan for integrating technology into existing DoD
exercises that support the NDS.
Identified infrastructure requirements for developing and deploying technicalcapabilities, including data, compute, storage, and network needs; a resourced
and prioritized plan for establishing such infrastructure; and an analysis
of TEVV requirements to support prototyping and experimentation and a
resourced plan to implement them.
Identified joint capability and interoperability requirements and a resourced
and prioritized plan for implementation.
Consideration of human factor elements associated with priority technicalcapabilities, including user interface, human-machine teaming, and workflow
integration.
Consideration of interoperability with allies and partners, including areas forsharing of data, tools, and operational concepts.
Flexibility to adapt and iterate appendix implementation at the speed oftechnological advancement.

• Steward Implementation of the Technology Annex in Coordination with theIntelligence Community.

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o The Secretary of Defense should direct the Steering Committee on Emerging
Technology to steward implementation of the Technology Annex, to include
coordination with the IC; and establish a reporting structure and metrics to monitor
the implementation of each technology roadmap to ensure each effort is resourced
properly and progressing sufficiently.
o The Steering Committee on Emerging Technology should ensure common
technical requirements are developed to align with the digital ecosystem’s open
architecture and are adhered to for the acquisition of emerging technologies
identified in the Technology Annex. The standards should be coordinated across
DoD and the IC.143
o The Steering Committee should conduct (at least) an annual review of the
Appendix and ensure that guidance, policy, and implementation evolve at the paceof technological change.
Component 5: Clearly communicate defense technology priorities to industry.
DoD must leverage industry’s comparative advantage in available R&D capital as part ofits investment strategy. To do so effectively, the Department must adopt a consistent andtransparent approach to messaging defense technology priorities that enables Defense
primes and non-traditionals to plan and invest more to help meet DoD R&D needs, andfurther supports the Department’s efforts to attract venture-backed companies.
Action for the Department of Defense:• Publish unclassified emerging technology R&D objectives to support theTechnology Annex to the National Defense Strategy.
o The Secretary of Defense should direct USD (R&E) to produce unclassified
emerging technology R&D objectives and publish these objectives publicly. The
objectives should represent an unclassified component of the Technology Annex,
and be regularly updated as living documents.
■

■

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The R&D objectives should be tied to subsets or components of priority
technologies and applications on which the government envisions the private
sector playing a major role in building future capabilities.144 They should becommunicated with an appropriate level of detail to provide current defense
companies guidance to steer their internal R&D investments, communicate tostartups interested in working with the government where future opportunities
lie, and signal to venture capitalists where future DoD funding might flow.
USD (R&E) should incorporate these objectives into the go-to-market strategy,
coordinating digital innovation initiatives to act as surrogates to amplify thiscommunication, and where appropriate, execute these priorities.

F O U N D AT I O N S O F F U T U R E D E F E N S E

o The Secretary of Defense should direct the Steering Committee on Emerging
Technology to develop an appropriate approach to monitor industry-independent
R&D investments to gauge the effectiveness of these efforts. This should becoordinated with the DoD Office of General Counsel and relevant industry
associations.
o OUSD (R&E) should leverage public-private exchange programs,145 as wellas internal technical expertise from entities like DARPA and the interagency
technology scouting community, to bring both technical expertise and commercial
proficiency to the effort.146

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Blueprint for Action: Chapter 2 - Endnotes
See Chapter 9 of this report and its associated Blueprint for Action for a recommendation toestablish a Technology Competitiveness Council that could serve as a body for this kind of strategiclevel coordination.

1

This action is mirrored in the Chapter 3 and Chapter 5 Blueprints for Action. The Commission
acknowledges section 236 of the FY2021 National Defense Authorization Act, which permits the
Secretary of Defense to establish a steering committee on emerging technology and national security
threats composed of the Deputy Secretary of Defense; the Vice Chairman of the Joint Chiefs ofStaff; the Under Secretary of Defense for Intelligence and Security; the Under Secretary of Defense
for Research and Engineering; the Under Secretary of Defense for Personnel and Readiness; theUnder Secretary of Defense for Acquisition and Sustainment; the Chief Information Officer; and suchother officials of the Department of Defense as the Secretary determines appropriate. However, thestructure described in section 236 does not include leadership from the Intelligence Community, andwill thus not drive the intended action. See Pub. L. 116-283, William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021), https://docs.house.gov/
billsthisweek/20201207/CRPT-116hrpt617.pdf.

2

The Commission’s recommendation for the development of a Technology Annex to the National
Defense Strategy is discussed later in this blueprint.

3

For example, the DoD’s Joint AI Center (JAIC) is building a Joint Common Foundation (JCF) thataims to provide policies and tools that support an enterprise cloud-enabled AI environment. See
About the JAIC, JAIC (last accessed Feb. 2, 2021), https://www.ai.mil/about.html. Other digital
ecosystem pathfinders include, but are not limited to, Platform One, Kessel Run, Space CAMP, Black
Pearl, CReATE, ADVANA, and the Army Futures Command Software Factory.

4

See DoD Digital Modernization Strategy, U.S. Department of Defense at 30, 42-43 (July 12, 2019),
https://media.defense.gov/2019/Jul/12/2002156622/-1/-1/1/DOD-DIGITAL-MODERNIZATIONSTRATEGY-2019.PDF (describing how the DoD plans to deploy an end-to-end identity, credential,
and access management infrastructure). This is an essential function that must be implemented in
an interoperable way across the national security-wide digital AI R&D ecosystem. DoD plans include
a goal to “Improve and Enable Authentication to DoD Networks and Resources through CommonStandards, Shared Services, and Federation.” Id. at 30.

5

6Implemented as applications as a service (AaaS).

7

Implemented as platforms as a service (PaaS).

The digital ecosystem should incorporate DevSecOps processes and tools laid out in the DoDEnterprise DevSecOps Reference Design. See DoD Enterprise DevSecOps Reference Design,
U.S. Department of Defense (Aug. 12, 2019), https://dodcio.defense.gov/Portals/0/Documents/
DoD%20Enterprise%20DevSecOps%20Reference%20Design%20v1.0_Public%20Release.pdf.
For more information, see Understanding the Differences Between Agile & DevSecOps - from a
Business Perspective, GSA (last accessed Jan. 1, 2021), https://tech.gsa.gov/guides/understanding_differences_agile_devsecops/ (“DevSecOps improves the lead time and frequency of delivery
outcomes through enhanced engineering practices; promoting a more cohesive collaboration
between Development, Security, and Operations teams as they work towards continuous integrationand delivery.”).

8

For a short primer on MLOps, see 2021 Technology Spotlight - The Emergence of MLOps, Booz
Allen Hamilton (2021), https://www.boozallen.com/content/dam/boozallen_site/dig/pdf/white_paper/
the-emergence-of-mlops.pdf.

910
Notably, the Office of the Under Secretary of Defense for Research & Engineering (OUSD (R&E))
in 2020 outlined a similar vision for an enterprise-wide, shared digital ecosystem to implement
the Department’s Digital Engineering Strategy and accelerate broad adoption of model-based
system engineering. See Andrew Monje, Future Direction of Model-Based Engineering Across the
Department of Defense, U.S. Department of Defense (Jan. 27, 2020), https://ac.cto.mil/wp-content/
uploads/2020/05/RAMS-Monje-27Jan2020-Future.pdf.

A common software delivery platform used by industry and academia based on the features of Git(https://git-scm.com), GitHub (https://github.com), and GitLab (https://about.gitlab.com).
11

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12Implemented as software as a service (SaaS).

See Chapter 7 of this report. See also Tab 1 - Recommendation 6: “Expedite the development oftools to create tailored AI test beds supported by both virtual and blended environments” in Second
Quarter Recommendations, NSCAI at 14 (July 2020), https://www.nscai.gov/previous-reports/.

13

14
See Tab 1 - Recommendation 1: “Create an AI software repository to support AI R&D” in Second
Quarter Recommendations, NSCAI at 3 (July 2020), https://www.nscai.gov/previous-reports/;
see also Tab 1 - Recommendation 2: “Promote ATO reciprocity as the default practice within andamong programs, Services, and other DoD agencies to enable sharing of software platforms,
components, infrastructure, and data for rapid deployment of new capabilities” in Second Quarter
Recommendations, NSCAI at 5 (July 2020), https://www.nscai.gov/previous-reports/.

Implemented as data as a service (DaaS). See Tab 1 - Recommendation 3: “Create a DoD-wide AI
data catalog to enable data discovery for AI R&D” in Second Quarter Recommendations, NSCAI at 7
(July 2020), https://www.nscai.gov/previous-reports/.

15

The data services and resources provided by the digital ecosystem should support the DoD Data
Strategy. See Executive Summary: DoD Data Strategy, U.S. Department of Defense (Sept. 30, 2020),
https://media.defense.gov/2020/Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

16

17
For more information on AI documentation, see Chapter 7 of this report and the Appendix
containing the abridged version of NSCAI’s Key Considerations for Responsible Development &Fielding of AI. See also the Commission’s recommendation to produce documentation of the AI life
cycle in the section on “Engineering Practices” in Key Considerations for Responsible Development &Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).
18
As part of the funding plan, the Department should consider proposing expansion of the pilot for
consumption-based solutions outlined in Section 834 of the FY2021 NDAA to extend across the stackof managed services that compose the digital ecosystem. See Pub. L. 116-283, William M. (Mac)Thornberry National Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).
19
The DoD CIO should determine the appropriate structure for such a working group, and may decideto leverage or federate existing cross-functional working groups such as those for the DoD Enterprise
DevSecOps Initiative or Enterprise Infrastructure. Similarly, DoD CIO should work with pathfinderorganizations to determine whether they should be incorporated as part of the governance working
group or broken out as a separate community from which to draw best practices.
20
For example, contributions to the digital ecosystem would come from AI delivery teams at the
combatant command headquarters level, and from forward-deployed teams, as they leverage theecosystem for agile development of AI-driven capabilities.
21

The list included is intended as a general outline of key stakeholders; it is not exhaustive.

22
In recent years, the Department has made promising initial steps to establish managed services
constructs for platforms, cloud infrastructure, and software development; for example, the Air Force’s
CloudOne and Platform One as well as multiple in-house software factories such as Kessel Run andSpace CAMP (https://software.af.mil/software-factories and https://software.af.mil/dsop/services/);the Navy’s Black Pearl (https://blackpearl.us/); and the Army’s Coding Repository and Transformation
Environment (CReATE); and the new Software Factory at Army Futures Command. Further, theOffice of the Secretary of Defense has built a data management platform, ADVANA, with the goal
to establish it as the single authoritative source for audit and business data analytics. See Written
Statement for the Record of David L. Norquist, Deputy Secretary of Defense before the U.S. Senate
Armed Services Committee Subcommittee on Readiness at 6 (Nov. 20, 2019), https://www.armedservices.senate.gov/imo/media/doc/Norquist_11-20-19.pdf.

The digital ecosystem’s open architecture should be developed with consideration of existing
warfighting architectures; for example, the Joint Warfighting Network Architecture recommended in
Chapter 3 of this report.

23

The open architecture should: 1) Define a common set of well-documented common interfaces forthe ecosystem’s key components and building blocks; 2) Support and integrate the work of existing
pathfinders up and down the ecosystem technology stack; and 3) Incorporate the process elementsof the DoD DevSecOps Reference Design Version 1.0 Aug. 12, 2019, data authorizations, andcontinuous software ATO reciprocity.

24

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Blueprint for Action: Chapter 2 - Endnotes
The CIO should include guidance along with the open architecture describing what categories ofsystems are to be adherent and which may be exempt.
25

26
Later in this blueprint, NSCAI recommends that USD (R&E) be appointed co-chair and chief
science advisor to the Joint Requirements Oversight Council (JROC) for joint and cross-domain
capabilities. This recommendation is also emphasized in Chapter 3 of this report.

The Executive Summary that accompanies the DoD Data Strategy states that each Component
will develop “measurable Data Strategy Implementation Plans, overseen by the CDO and DoD Data
Council.” See Executive Summary: DoD Data Strategy, U.S. Department of Defense (Sept. 30, 2020),
https://media.defense.gov/2020/Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

27

For example, ensuring contract Data Item Descriptions include the use of application programming
interfaces as the data transfer medium. For additional details on AI documentation, see Chapter 7of this report and the Appendix containing the abridged version of NSCAI’s Key Considerations for
Responsible Development & Fielding of AI. See also the Commission’s recommendation to produce
documentation of the AI life cycle in the section on “Engineering Practices” in Key Considerations for
Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on filewith the Commission).

28

Memorandum from Deputy Secretary of Defense, Actions to Enhance and Accelerate Enterprise
Data Management, U.S. Department of Defense at 1 (Dec. 10, 2020).

29

30
As an example, the Steering Committee on Emerging Technology could consider designating the
Defense Logistics Agency (DLA) as an enterprise service provider for logistics applications andassociated services. These applications would be made available within the ecosystem for reuse andintegration. Similarly, upon publication of the reference design, the Committee could explore working
with the Intelligence Community to designate and integrate Intelligence Community application
service providers (e.g., the National Geospatial Agency for GEOINT application services).

The repository would support implementation of the DoD Data Strategy; the Strategy’s guiding
principles include “data is a strategic asset” and “enterprise-wide data access and availability.”
See DoD Data Strategy, U.S. Department of Defense at 3-4 (Sept. 30, 2020), https://media.defense.
gov/2020/Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

31

32
See “Advana” Defense Analytics Platform – Department of Defense, ACT-IAC (June 16, 2020),
https://www.youtube.com/watch? v=BIQ31B9Hv44.

The digital ecosystem rides on the capacity of DoD’s underlying network and communication
backbone to provide rapid, on-demand access to the essential AI building blocks. The DoD Digital
Modernization Strategy calls out the need to modernize the Department’s primary networks,
the warfighter’s communication connectivity, and coalition networks—highlighting the need to
upgrade the optical network transport, routers, switches, and satellite gateways. See DoD Digital
Modernization Strategy, U.S. Department of Defense at 20-21, 35, 37 (July 12, 2019), https://media.
defense.gov/2019/Jul/12/2002156622/-1/-1/1/DOD-DIGITAL-MODERNIZATION-STRATEGY-2019.PDF.

33

34
Components of which are already underway as a result of the JAIC’s Joint Common Foundation
initiative.

A common software delivery platform used by industry and academia based on the features of Git(https://git-scm.com), GitHub (https://github.com), and GitLab (https://about.gitlab.com).

35

36
See Tab 1 - Recommendation 1: “Create an AI software repository to support AI R&D” in Second
Quarter Recommendations, NSCAI at 3 (July 2020), https://www.nscai.gov/previous-reports/.

See Chapter 7 of this report. See also Tab 1 - Recommendation 6: “Expedite the development oftools to create tailored AI test beds supported by both virtual and blended environments” in Second
Quarter Recommendations, NSCAI at 14 (July 2020), https://www.nscai.gov/previous-reports/.

37

A federated repository system uses a federated directory that ties distributed repositories together
as a virtual whole. See Tab 1 - Recommendation 3: “Create a DoD-wide AI data catalog to enable
data discoverability for AI R&D” in Second Quarter Recommendations, NSCAI at 7 (July 2020), https://www.nscai.gov/previous-reports/.

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F O U N D AT I O N S O F F U T U R E D E F E N S EThis would be supported by the prototype centralized data repository recommended above,and hinges on implementation of the new DoD Data Strategy, which details the goals to make
DoD data visible, accessible, understandable, linked, trustworthy, interoperable, and secure. DoD
Data Strategy, U.S. Department of Defense at 6 (Sept. 30, 2020), https://media.defense.gov/2020/
Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

39

Trained AI models are a special class of data, and the same federated repository system solution
used for DataEx can also be used to support ModelEx.

40

Another type of anticipated trained AI model is digital twins, as modeling and simulation platforms,
such as the Army’s One World Terrain advance to support training digital twins through reinforcement
learning. For more on One Word Terrain, see One World Terrain: A Pillar of the Army’s Synthetic
Training Environment, USCICT (Aug. 2, 2019), https://www.youtube.com/watch?v=K50eL1wU4ic.

41

DoD users could choose services off a multi-cloud provider schedule paying as they go forcomputing resources and uploading/storing the government’s data. To facilitate seamless migrationof data and software from one platform to another, the DoD should negotiate contracts with providers
that appropriately limit expenses related to data egress and migration.

42

43
Internally developed software solutions and data sets could be contributed for use across the DoD
with built-in incentives for contributors through awarded cloud credits when products are contributed
and used. Limited public-facing elements could be brokered on the National AI Research Resource
recommended in Chapter 11 of this report.
44

See Chapter 7 of this report.

These platform environments should have ATO reciprocity for the building blocks they provision,
including incorporating DevSecOps development stacks.

45

46
Digital ecosystem contributors and users include embedded development teams working at
the tactical edge (see below Recommendation: Embed AI development capabilities in support ofoperations); private sector partners contributing trained models; academic researchers working
on open challenge problems; researchers working within a DoD lab; or international partners codeveloping interoperable AI capabilities.

Becky Frankiewicz & Tomas Chamorro-Premuzic, Digital Transformation Is About Talent, Not
Technology, Harvard Business Review (May 6, 2020), https://hbr.org/2020/05/digital-transformationis-about-talent-not-technology.

47

As recommended in Chapter 7 of this report, national security departments and agencies should
provide ongoing training to help the workforce better interact, collaborate with, and be supported by
AI systems—including understanding AI tools.

48

49
Steve Blank & Pete Newell, What Your Innovation Process Should Look Like, Harvard Business
Review (Sept. 11, 2017), https://hbr.org/2017/09/what-your-innovation-process-should-look-like.
50
Andrew Moore, et al., The AI Stack: A Blueprint for Developing and Deploying Artificial Intelligence,
Proc. SPIE 10635 (May 4, 2018), https://doi.org/10.1117/12.2309483. For a graphical depiction of the
AI stack, see About, Carnegie Mellon University Artificial Intelligence (last accessed Jan. 1, 2021),
https://ai.cs.cmu.edu/about.
51
Pub. L. 109-364, John Warner National Defense Authorization Act for Fiscal Year 2007, 109th
Congress (2006).

DoD Instruction 1300.19: DOD Joint Officer Management Program, U.S. Department of Defense
at 14 (April 3, 2018), https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/130019p.
pdf?ver=2018-04-03-114842-923.

52

53
See Chapter 2 of this report and this associated Blueprint for Action section below about leveraging
public-private talent exchanges to infuse technical expertise into the acquisition corps for NSCAI’s
recommendation to create a technology fellows program to support development of a Technology
Annex to the National Defense Strategy; there are numerous extant fellowships across the DoD
involving emerging technologies.

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This action aligns with the recommendation to establish a strategic data node within thedigital ecosystem discussed earlier in this Blueprint and with the DoD Data Strategy, which lists
Senior Leader Decision Support and Business Analytics as initial areas of focus. See DoD Data
Strategy, U.S. Department of Defense at 11 (Sept. 30, 2020), https://media.defense.gov/2020/
Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF.

54

Notably, this recommendation is aligned with Section 836 of the FY 2021 National Defense
Authorization Act, which directs the Secretary of Defense to develop and integrate advanced digital
data management and analytics capabilities that integrate all aspects of the defense acquisition
system; facilitate the management and analysis of all relevant data; enable the use of such data to
inform further development, acquisition, management, and oversight of such systems, including
portfolio management; and include software capabilities to collect, transport, organize, manage,
make available, and analyze relevant data throughout the life cycle of defense acquisition programs.
The section further requires capability demonstrations and revised policies to promote the use ofdigital management and analytics capabilities by March 15, 2022. Pub. L. 116-283, William M. (Mac)Thornberry National Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

55

For example, critical human resource variables such as performance and retention are likely related
to budget variables (pay), health care variables (accessibility and quality of care) and even real estatevariables (housing). These relationships will become transparent and quantifiable when data tagging
supports cross domain analyses.

56

Gamechanger is an AI-enabled tool designed to analyze documentation of U.S. government
legislation, policies, and regulations for semantic content, to trace authorities and responsibilities
across documents, and to map authorities and responsibilities to agencies and officials designatedtherein.

57

See discussion below for details on the responsibilities envisioned for JAIC as the Department’s AI
Accelerator.

58

59
For example, DIU is currently pursuing a number of AI projects to optimize business processes
in the DoD – –ranging from using AI-driven Robotic Process Automation to reduce labor costs for
the Army Comptroller, to improving Air Force readiness with AI-driven predictive maintenance, toleveraging AI-constructed knowledge graphs to rapidly identify supply chain risks. See JAIC Partners
with DIU on AI/ML Models to Resolve Complex Financial Errors, JAIC (Oct. 1, 2020), https://www.
ai.mil/blog_10_01_ 20-jaic_partners_with_diu_on_aiml_models_to_resolve_complex_finanical_errors.
html; U.S. Defense Department Awards C3.ai $95M Contract Vehicle to Improve Aircraft Readiness
Using AI, Business Wire (Jan. 15, 2020), https://www.businesswire.com/news/home/20200115005413/
en/US-Defense-Department-Awards-C3.ai-95M-Contract-Vehicle-to-Improve-Aircraft-ReadinessUsing-AI; Accrete.AI Accelerates Growth and Product Adoption with Defense Innovation Unit Contract,
Accrete.ai (April 23, 2020), https://blog.accrete.ai/newsroom/accrete.ai-wins-million-dollar-contractwith-the-defense-innovation-unit.

This should include an evaluation of existing policies and regulations on contract data rights,
data format, data definitions, and data environments to accelerate application of commercial AI for
acquisition, management, and oversight and maximize insights derived.

60

61
For a glimpse into the DoD’s innovation ecosystem, see Tap the Innovation Ecosystem, MITRE:
Acquisition in the Digital Age, (last accessed Jan. 25, 2020), https://aida.mitre.org/demystifying-dod/innovation-ecosystem/; Understanding the DoD Innovation Ecosystem, MITRE: Bridging Innovation(last accessed Jan. 25, 2020), https://bridge.mitre.org/visualization/.
62

See Interim Report, NSCAI at 31 (November 2019), https://www.nscai.gov/previous-reports/.

63
The term “digital innovation initiatives” is used here to describe the various entities across the
Office of the Secretary of Defense and the military services, such as the Defense Innovation Unit
(DIU), AFWERX, NavalX, and Army Applications Laboratory (AAL), that are focused on bridging the
gap with the commercial technology section–especially startups and non-traditional vendors–andaccelerating the delivery of best-of-breed technology solutions.

As the Department’s Chief Technology Officer, USD (R&E) has both the authority and mandate tocoordinate discrete efforts across OSD and the military services to accelerate the adoption of digitaltechnology and expand the national security innovation base (NSIB). However, USD (R&E) mustensure close coordination with USD (A&S) and, in the case of IT and information systems, DoD CIO,
to improve the transition of solutions emerging from these organizations into operational systems.

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65For example, through current SBIR “bridging” funds described in Chapter 11 of this report or
technology-specific supplemental funding recommended later in this Blueprint for Action under,“Make supplemental funding available to drive operational prototyping, scale, and transition of AI
technologies.”
66
As the Chief Technology Officer of the DoD, USD (R&E) has a “mission to advance technology
and innovation.” Additionally. USD (R&E) is responsible for advis[ing] the Secretary of Defense on
all matters related to research; engineering; manufacturing; developmental test & evaluation; andtechnology development, innovation, and protection activities and programs in the DoD and occurringinternationally [as well as] establishing priorities across those matters to ensure conformance with
Secretary of Defense policy and guidance. For a full list of USD (R&E)’s responsibilities and functions,see DoDD Directive 5137.02: Under Secretary Of Defense For Research And Engineering (USD
(R&E)), U.S. Department of Defense (Jan. 4, 2021), https://www.esd.whs.mil/Portals/54/Documents/
DD/issuances/dodd/513702p.pdf? ver=2020-07-15-124712-047. [hereinafter DoDD 5137.02]
67
For example, DIU leverages Other Transaction Authority (OTA) and the Commercial Solutions
Opening process to “test, field, and scale commercial technology in less than 24 months.” The
Air Force’s AFWERX, in partnership with Air Force Research Lab (AFRL) and DIU’s National
Security Innovation Network (NSIN), has pioneered new approaches to Small Business Innovation
Research (SBIR) and Small Business Technology Transfer (STTR) funding to “increase theefficiency, effectiveness, and transition rate” of the program. See Annual Report 2019, Defense
Innovation Unit at 4 (2019), https://assets.ctfassets.net/3nanhbfkr0pc/ZF9fhsMe6jtX15APMLalI/
cd088a59b91857c5146676e879a615bd/DIU_ 2019_ Annual_Report.pdf; SBIR Open Topics, U.S. Air
Force AFWERX (last accessed Jan. 1, 2021), https://www.afwerx.af.mil/sbir.html.
68

Also informed by the JAIC, and made accessible through the digital ecosystem.

69
As described in Chapter 3 of this report, there should be a push-pull relationship between
innovative technologies and concepts such that the Technology Annex informs, but does not limit, thescope of activity. Digital Innovation Initiatives will likely continue to have responsibilities outside of thisgo-to-market strategy; for example, the acceleration of commercial AI applications for core businessprocesses.
70
The impact and potential use cases of investments may not be apparent for several years. This
review aims to provide insight into current activities so as to influence, but not dictate, modifications to
the next “go-to-market strategy.” This process should be automated to the maximum extent possible
to minimize overhead.

Many of the processes and technical roadblocks faced by traditional and non-traditional
vendors that slow critical efforts to build and integrate AI systems will be greatly diminished by the
implementation of a digital ecosystem, as described above. However, until then, top-down support at
the highest levels of leadership will be essential to empower digital innovation initiatives. Per DoDD
5137.02, part of USD (R&E)’s functions include working in conjunction with the USD (A&S) to identify,
evaluate, and promote opportunities to reduce barriers to entry for commercial technologies and nontraditional defense partners; and leading initiatives to engage non-traditional suppliers of technology.
See DoDD 5137.02.

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72

Where appropriate, efforts should leverage expertise from FFRDCs and UARCs.

Prototyping contracts provide non-recurring engineering dollars to companies for early-stage
technologies and projects “to evaluate and inform [their] feasibility or usefulness.” Often, thesedollars come from dedicated funds, such as the SBIR and STTR programs and DIU’s internal
prototyping budget; and are distributed by organizations like DIU outside of the acquisition life cycle
domains, including DoD programs of record (PoR). Companies executing promising projects throughthese mechanisms often exhaust prototype funding and are unable to secure sustainable followon contracts (i.e., with a clear path toward integration into a PoR) because they cannot identify acustomer, or the customer’s funding is already committed. AFWERX improved transition in its SBIR
program by achieving buy-in from potential customers through matching program funds. See Tab
1 - Recommendation 7: “Strengthen Return on SBIR Investments” in Interim Report and Third Quarter
Recommendations, NSCAI at 52 (October 2020), https://www.nscai.gov/previous-reports/; Prototyping
Guidebook, U.S. Department of Defense at 36 (November 2019), https://www.dau.edu/tools/Lists/
DAUTools/Attachments/329/DoD%20Prototyping%20Guidebook,%20v2.0.pdf.

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For example, at least one F-22 and F-35 aircraft designated as AI test beds could incentivize
existing contractors and non-traditional firms to work together and better align their incentives to field
new mission capabilities. Such an initiative would build on initial efforts to integrate agile softwaredevelopment into F-22 modernization programs. See Craig Ulsh, Software Acquisition and Practices(SWAP) Study: Vignettes, DoD Defense Innovation Board at 6 (Jan. 10, 2019), https://media.defense.
gov/2019/Mar/07/2002097482/-1/-1/0/SWAP_STUDY_VIGNETTES.PDF.

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75
The 2019 National Defense Authorization Act identified metrics for DIU to report, such as: thenumber and types of transitions by the Unit to the military departments or fielded to the warfighter;and the impact of the Unit’s initiatives, outreach, and investments on Department of Defense accessto technology leaders and technology not otherwise accessible to the Department, including thenumber of non-traditional defense contractors with Department of Defense contracts or othertransactions resulting directly from the Unit’s initiatives, investments, or outreach; the number of
traditional defense contractors with contracts or other transactions resulting directly from the Unit’s
initiatives; and the number of innovations delivered into the hands of the warfighter. See Pub. L.
115-232, sec. 244, John S. McCain National Defense Authorization Act for Fiscal Year 2019, 115th
Congress (2018); Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021).

This metric should be appropriately scoped such that consideration is given to products orsolutions that lend themselves to enterprise licensing agreements and prioritize measures thatindicate the level of cross-service, cross-unit proliferation of a solution.
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77
A 2019 study conducted by the Defense Innovation Board Defense reached similar conclusions withregard to software acquisitions generally, stating that the current approach to software development
is broken and is a leading source of risk to DoD; it takes too long, is too expensive, and exposes
warfighters to unacceptable risk by delaying their access to tools they need to ensure mission
success. Software is Never Done: Refactoring the Acquisition Code for Competitive Advantage, DoD
Defense Innovation Board at i (May 2019), https://media.defense.gov/2019/May/01/2002126689/-1/1/0/SWAP%20COMPLETE%20REPORT.PDF.
78
The Adaptive Acquisition Framework promotes use of tailored acquisition approaches based on theneeded capability. It includes six guiding pathways for acquiring capabilities that Milestone Decision
Authorities (MDAs), other Decision Authorities (DAs), and Program Managers (PMs) can “tailor,
combine, and transition between”: Urgent Capability Acquisition, Middle Tier of Acquisition, Major
Capability Acquisition, Software Acquisition, Defense Business Systems, and Acquisition of Services.
See Adaptive Acquisition Framework Pathways, Defense Acquisition University, (last accessed Dec.
26, 2020), https://aaf.dau.edu/aaf/aaf-pathways/. The Software Acquisition Pathway was developed
based on a recommendation from the Defense Innovation Board in the 2019 Software Study. See
Software is Never Done: Refactoring the Acquisition Code for Competitive Advantage, DoD Defense
Innovation Board at 37, S2 (May 2019), https://media.defense.gov/2019/May/01/2002126689/-1/-1/0/
SWAP%20COMPLETE%20REPORT.PDF.
79
The Contracting Cone outlines all Federal Acquisition Regulation (FAR) and Non-FAR contract
strategies. Contracting Cone, Defense Acquisition University (last accessed Dec. 20, 2020), https://
aaf.dau.edu/aaf/contracting-cone/.

Specialized pathways include approaches captured within the Department’s Adaptive Acquisition
Framework such as the Middle Tier of Acquisition and Software Acquisition that are exempted fromcertain requirements within the Defense Acquisition System.

80

In January 2020, former Under Secretary of Defense for Policy Michele Flournoy cited concern
over inadequate training and incentives for acquisition professionals to make full use of authorities
provided by Congress. She noted “pockets of [acquisition] excellence” in Special Operations
Command and the Air Force, but argued the larger acquisition corps “is not using the authorities
effectively, consistently, and at scale.” See Testimony of The Honorable Michele A. Flournoy,
former Under Secretary of Defense for Policy before the U.S. House of Representatives Armed
Services Committee, Hearing on DoD’s Role in Competing with China at 6 (Jan. 15, 2020), https://
armedservices.house.gov/_cache/files/4/4/44fbef3d-138c-4a0a-b3a9-2f05c898578f/0E4943A5BFAE
DA465D485A166FABCF5F.20200115-hasc-michele-flournoy-statement-vfinal.pdf.

81

Including Federal Acquisition Regulation (FAR)-based approaches and non-FAR-based approaches
as outlined in the Defense Acquisition University’s Contracting Cone. See Contracting Cone, Defense
Acquisition University (last accessed Dec. 20, 2020), https://aaf.dau.edu/aaf/contracting-cone/.

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83Such as the middle tier of acquisition and the software acquisition pathway.

84
For example, efforts associated with section 230 of the Fiscal Year 2020 NDAA on talentmanagement of digital expertise and software professionals; section 256 on an education strategy for
Artificial Intelligence; and section 862 of the FY2020 NDAA on software development and software
acquisition training and management programs. In support of the implementation of Section 862,
USD (A&S) is developing a pilot software acquisition training program that aims to better enable the“creation and execution of acquisition strategies and contracts that support the speed of technology
and change” by providing students with the foundations of digital technologies through evolutionary
content in context of the Defense Acquisition System. Digital DNA: Software Acquisition Training Pilot,
U.S. Department of Defense at 1 (on file with the Commission); see also Report to Congress on FY20
NDAA Section 862(b)(1)(B) Software Development and Software Acquisition Training and Management
Programs, U.S. Department of Defense at Appendix H (January 2021), https://www.hci.mil/docs/
Policy/FY20_NDAASec862ReportToCongress_DoDSoftwDevSoftwAcqTngMgt_ Jan2021.pdf.

This should be coordinated appropriately with the relevant legal and ethics officials to avoid any
potential conflicts of interest.

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86
Section 1102 of the FY2021 National Defense Authorization Act directs the Secretary of Defense
to provide briefings to the defense authorization committees on implementation of public-private
exchange programs and recommendations for statutory changes to improve their use andeffectiveness. Section 1102 also directs the Secretary to take steps to ensure the exchange programis applied to the defense modernization priorities–including AI. While USD (R&E)’s modernization
directors are responsible for “unifying and advancing the Department’s investments and capabilities[in their areas], and ensur[ing] the transition of technologies into operational use,” the Department’s
acquisition professionals will be the personnel ultimately responsible for operationalizing themodernization priorities. See Pub. L. 116-283, William M. (Mac) Thornberry National Defense
Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021); see also Modernization Priorities,
U.S. Department of Defense, USD (R&E), (last accessed Dec. 28, 2020), https://www.cto.mil/
modernization-priorities/.

The DoD has already begun to make progress in these areas. For example, the Advanced
Distributed Learning (ADL) Initiative under the Office of the Under Secretary of Defense for Personnel
and Readiness (USD (P&R)), which originated in the 1990s, is a DoD-wide program for modernizing
DoD training/education, including the use of learning technologies and platforms, and support forcontent sharing, collaboration, and interoperability. ADL is currently pursuing an Enterprise Course
Catalog to federate disparate or decentralized catalogs across the organization, aggregating
the content into a single, Defense-wide portal. See Enterprise Course Catalog (ECC), Advanced
Distributed Learning Initiative (last accessed Feb. 12, 2021), https://adlnet.gov/projects/ecc/.

87

88Including DoD-specific training as well as relevant commercial and open-source training.

Examples could include draft acquisition strategy documents for programs planning to use themiddle tier or software acquisition pathways; model contracting language for AI technologies, etc.

89

Including on new or innovative acquisition approaches and best practices as well as new or
emerging digital technologies and technical approaches (e.g., digital engineering, MLOps, etc.).

90

This recommendation echoes a recommendation made by the Defense Innovation Board (DIB) in a2019 study on software acquisition and practices within the Department of Defense. The DIB called
for a new acquisition pathway for software that would prioritize continuous integration and delivery
of working software in a secure manner, with continuous oversight from automated analytics. The
DIB provided draft legislative language in the body of the report for consideration by the DoD and
Armed Services Committees in implementing this recommendation. The draft legislative text indicatedthe need for a rapid contracting mechanism to be established as part of the software pathway.
Although the creation of a software acquisition pathway was directed by section 800 of the FY2020
NDAA and the Department has since issued a formal policy on the pathway, the rapid contracting
mechanism remains unimplemented. See Software is Never Done: Refactoring the Acquisition Codefor Competitive Advantage, DoD Defense Innovation Board at S58 (May 2019), https://media.defense.
gov/2019/May/01/2002126689/-1/-1/0/SWAP%20COMPLETE%20REPORT.PDF; Pub. L. 116-92,
National Defense Authorization Act for Fiscal Year 2020; DoD Instruction 5000.87: Operation of the
Software Acquisition Pathway, U.S. Department of Defense (Oct. 2, 2020), https://www.esd.whs.mil/
Portals/54/Documents/DD/issuances/dodi/500087p.PDF? ver=virAfQj4v_LgN1JxpB_dpA%3D%3D.

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Using system-level program elements incentivizes programs to build “full stack” with eachsubcomponent and enabling technology being built and procured individually as part of the broader
program makeup. This reduces risk. In recent years, enabling services such as Platform One havere-emerged, but it is difficult to justify base operating budgets for these organizations because
they are not tied to discrete outputs. See Eric Lofgren, The DoD Budget Process: the Next Frontierof Acquisition Reform, George Mason University Center for Government Contracting (July 9, 2020),
https://business.gmu.edu/images/GovCon/ White_Papers/ The_DoD_Budget_Process.pdf.

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93
Joint Capabilities Integration and Development System (JCIDS) and the Planning, Programming,
Budget and Execution (PPBE) process are tightly linked. Military needs drive the development of
new programs to deliver capability. Traditionally derived from concepts of operations, these needs
are the basis against which the Department evaluates, costs, and ultimately pursues a solution. If
the Department determines that a material solution is necessary, the need will be decomposed into
requirements that prescribe the design, specification, and function of the system intended to deliver
the capability. Once validated, these requirements drive the DoD’s budget. Id. at 5.

Commonly known as “colors of money,” DoD funds are appropriated into the following categories,
each with its own allowable uses per law: Research, Development, Test & Evaluation (RDT&E) dollars,
Procurement dollars, Operations & Maintenance (O&M), and Sustainment dollars.

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95
The distinction between research and development funds and operating funds disincentivizes
the cycle of continuous development and integration necessary to derive value from AI andsoftware-based applications. Within the RDT&E appropriation alone, separate funding for research,development, prototyping, and fielding assumes a slow linear progression from lab to field predefined system requirements that allow for little to no user feedback. Once fielded, appropriations law
governing the use of O&M funds challenges upgrades to digital systems.

Congressional testimony from former Under Secretary of Defense for Policy Michele Flournoy
highlights the centrality of experimentation to developing new concepts and capabilities at the
speed required to outpace our competitors. See Testimony of The Honorable Michele A. Flournoy,
former Under Secretary of Defense for Policy before the U.S. House of Representatives Armed
Services Committee, Hearing on DoD’s Role in Competing with China at 8 (Jan. 15, 2020), https://
armedservices.house.gov/_cache/files/4/4/44fbef3d-138c-4a0a-b3a9-2f05c898578f/0E4943A5BFAE
DA465D485A166FABCF5F.20200115-hasc-michele-flournoy-statement-vfinal.pdf.

96

Requirements are developed that drive technological development, and prototyping andexperimentation occur as a means to refine requirements and manage risk. This incentivizes
integration of incremental technologies into programs of record rather than disruptive or rapidlychanging user-centered technologies, such as AI; and limits the ability of program managers to
respond to any fast-paced change in technology later in the life of the program. See Pete Modigliani
et al., Modernizing DoD Requirements: Enabling Speed, Agility, and Innovation, The MITRE Center forTechnology and National Security (March 2020), https://www.mitre.org/sites/default/files/publications/pr-19-03715-2-modernizing-dod-requirements-enabling-speed-agility-and-innovation.pdf.

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98
The budget activity 8 (BA 8) pilot seeks to overcome the barrier that DoD spending categories
pose to the development and sustainment of digital technologies. The Office of the Under Secretaryof Defense for Acquisition and Sustainment and the Office of the Under Secretary of Defense forComptroller selected nine programs to begin to pilot the BA 8 for FY2021. Defense appropriators
approved eight of the nine programs, and BA 8 is being established for each Service and Defensewide under the Research, Development, Test & Evaluation appropriation and enable two-year funding.
See H.R. 133, Consolidated Appropriations Act, 2021, 116th Congress (2020), https://docs.house.
gov/billsthisweek/20201221/BILLS-116RCP68-JES-DIVISION-C.pdf?source=email (joint explanatory
statement at 118).
99
Appointing USD (R&E) Co-Chair and Chief Science Advisor to the JROC would help pushforward efforts to reform requirements generation and validation. Serving as the system architectfor joint and cross-domain solutions, USD (R&E) would advocate for more flexible system designand specifications such as modular open systems architecture and standards, well-documented
application programming interfaces (APIs). See Chapter 3 of this report. See also Tab 2 Recommendation 2: “USD (R&E) should be appointed the Co-Chair and Chief Science Advisor to
the Joint Requirements Oversight Council (JROC) for Joint and cross-domain capabilities” in Interim
Report and Third Quarter Recommendations, NSCAI at 70 (October 2020), https://www.nscai.gov/
previous-reports/.

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100
Section 809 of the FY2021 NDAA directs the Secretary of Defense and the Director for ExtramuralInnovation and Research Activities to “conduct an assessment of the processes for developing
and approving capability requirements for the acquisition programs of the Department of Defense
and each military department” and submit reports to the defense authorization committees. Pub.
L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021,
134 Stat. 3388 (2021). Section 809 further stipulates that, as part of the assessment, both officials
must evaluate the “extent to which portfolio management techniques are used in the process fordevelopment capability requirements to coordinate decisions and avoid duplication of capabilities
across acquisition programs.” Id. The Joint Explanatory Statement accompanying the provisionindicates that the Department shall consider the recommendations made in the MITRE Corporation’sModernizing the Requirements Process: Enabling Speed, Agility, and Innovation as part of thedirected assessment. Recommendations include the establishment of enterprise-level requirements
or “Warfighter Essential Requirements’’ for capabilities to ensure acquisition programs are closelyaligned to warfighter needs, drive systems of systems approaches and reduce redundancies
between and among services and domains; and enable budget and requirements trade-offs througha portfolio management approach. The authors also recommend different management approaches
for requirements based on the attributes of the system being developed. See Pete Modigliani, et al.,Modernizing the Requirements Process: Enabling Speed, Agility, and Innovation, MITRE (March 2020),
https://www.mitre.org/sites/default/files/publications/pr-19-03715-2-modernizing-dod-requirementsenabling-speed-agility-and-innovation.pdf.
101
A formal legislative proposal may not be required. DoD retains discretion in the structure
and objectives of annual budget proposals. However, approval from Congress and the Office ofManagement and Budget is required.
102

Such as dashboards and digital engineering artifacts.

103
USD (R&E) should work closely with the JAIC, the Joint Staff, and the military services to identify
specific programs and mission areas ripe for potential application of AI technologies, with particularattention to near-term warfighter needs from the Combatant Commands, and use the fund toaccelerate efforts in those areas. Establishment of this fund would need to be accompanied withtransfer authority such that USD (R&E) could transfer resources to the relevant entities to conduct
these activities.
104
This is being led by the DoD Office of the Under Secretary of Defense for Comptroller andOffice of the Under Secretary of Defense for Acquisition and Sustainment, based on the findings
and recommendations of the Defense Innovation Board’s Software Acquisition and Practices
Study. Software is Never Done: Refactoring the Acquisition Code for Competitive Advantage, DoD
Defense Innovation Board (May 2019), https://media.defense.gov/2019/Apr/30/2002124828/-1/-1/0/
SOFTWAREISNEVERDONE_REFACTORINGTHEACQUISITIONCODEFORCOMPETITIVEADVANTAGE_
FINAL.SWAP.REPORT.PDF. Jeff Boleng, Special Assistant for Software Acquisition to the Under
Secretary of Defense for Acquisition and Sustainment, publicly stated the goal of the pilot as“simplifying the budget process, increasing the visibility, accountability of the funding.” Billy
Mitchell, DOD has OMB Support for Special Software-only Appropriations Pilots, FedScoop(Sept. 10, 2019), https://www.fedscoop.com/dod-omb-support-special-software-appropriationspilots/. In public remarks made March 3, 2020, Under Secretary of Defense for Acquisition andSustainment Ellen Lord underscored the significance of the pilot, asserting, “we will begin tosee results almost instantaneously, because the administrative burden of making sure you arecharging the right development number, the right production number, the right sustainment
number, slows things down.” Jared Serbu, Pentagon Teeing Up Nine Programs to Test New‘Color of Money’ for Software Development, Federal News Network (March 4, 2020), https://federalnewsnetwork.com/acquisition/2020/03/pentagon-teeing-up-nine-programs-to-testnew-color-of-money-for-software-development/; West 2020: 3 March 2020 Morning Keynotewith The Honorable Ellen Lord, WEST Conference (March 3, 2020), https://www.youtube.com/
watch? v=VGlqjyMhtok&list=PLFZb4znlHwx0TcsirmyYD6k5BAYxDRwU0&index=6&t=0s.

For example: budget activities within the appropriation could be aligned to a DoD Component;
program elements or budget lines under the budget activities would align to joint capabilities(e.g., Joint Command and Control) and then further decomposed into projects (i.e., key systems,
investments, and supporting activities).

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106

Often, technology that has been in use in the commercial sector for years.

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Blueprint for Action: Chapter 2 - Endnotes
Section 232 of the National Defense Authorization Act for FY2021 designates the JAIC as a direct
report to the Deputy Secretary of Defense, adds to the JAIC’s responsibilities the “acquisition anddevelopment of mature artificial intelligence technologies in support of defense missions,” and directs
the Secretary of Defense to clarify the roles and responsibilities of various DoD Components relative
to the “research, development, prototyping, testing, procurement of, requirements for, and operationaluse of artificial intelligence technologies.” See Pub. L. 116-283, William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).
107

Best practices could include user-centered approaches such as problem discovery, which could
be captured and shared via a modern, queryable knowledge management system; or algorithms ormodels added to the JAIC’s repository within the digital ecosystem.

108

For example, identity-based user authentication and access controls; definition of common
standard interfaces and documentation requirements; and accreditation and ATO reciprocity. See fulllist above.

109

110
For example, working with the Office of the Under Secretary of Defense, the Defense Contract
Management Agency, Service Acquisition Executives, and other relevant parties responsible foracquisition and procurement activities to develop model contract language that incorporates thestandards and practices outlined in NSCAI’s Key Considerations for Responsible Development& Fielding of AI. These would apply both to systems developed by DoD, as well as those thatare acquired (including Commercial off-the-shelf systems or those developed by contractors).
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for governance, see the sections on “Aligning Systems and Uses with American
Values and the Rule of Law” and “Accountability and Governance” in Key Considerations for
Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on filewith the Commission).
111

More details for a TEVV framework can be found in Chapter 7 of this report.

112
AI applications are extremely diverse and thereby necessitate a wide range of testing methods.
Establishing common approaches to tailoring appropriate processes and tools to the type of AI
application at hand will support the ability of DoD components to embrace and scale AI solutions
by shortening the testing cycle and making test results interpretable and comparable across the
Department. Given the diversity of use cases, the framework would not embody a one-size-fits-all
approach, but rather provide core capabilities and guidance adaptable across application areas. Fora full discussion on this framework, and required resourcing, see Chapter 7 of this report.

Depending on the current state of the implementation of the digital ecosystem, this shared accesscould be accomplished through the federated system of distributed software repositories—whether
the JAIC’s software repository or one managed by a DoD component that originally developed or
licensed the software tool.

113

114
Including tools for TEVV. This effort should also determine what AI development tools are
already available across the Department (e.g., where commercial software licenses already exist)
and, leveraging the acquisition authority granted in the FY2021 NDAA, procuring leading-edge AI
development tools with licensing terms to support enterprise-wide usage. Reasonable consideration
should be given for the maturity of the product/tool and likelihood of enterprise use. Section 808 of
the FY 2021 National Defense Authorization Act grants the Director of the Joint Artificial Intelligence
Center acquisition authority up to $75 million out of the funds made available in FiY 2021-2015 toenter into new contracts. Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization
Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

Such as data scientists, AI and software engineers, infrastructure engineers, product managers,
and other key positions.

115

116
Including full stack development, engineering, testing, integration, etc., for AI applications andsystems.
117
To reduce barriers to entry, the Department could also consider pairing the Blanket Ordering
Agreement or Blanket Purchasing Agreement with a Broad Agency Announcement or Commercial
Solutions Open solicitation procedure.

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F O U N D AT I O N S O F F U T U R E D E F E N S EThis should include a diverse cross-section of expertise that at a minimum includes engineering(i.e., data science and AI solutions), AI digital ecosystem architecture, AI software experts, productmanagers; and acquisition, legal, policy experts as well as domain experts.

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119
This could also involve JAIC representatives embedded at Combatant Command headquarters
where appropriate and feasible.

Of note, the NSCAI Interim Report Appendix 3: Workforce Model’s recommendations are designed
to support this model, with AI experts and developers serving at hubs, developers serving in spokes,
and deployment specialist training helping domain experts maintain data sets and software and better
partner with experts and developers. Interim Report, NSCAI at 61 (November 2019), https://www.
nscai.gov/previous-reports/.

120

121
CCMDs have specific operational needs that routinely outpace centralized development
approaches. Modern battlefield dynamics require that each commander have the ability to tailor thecharacter of his or her war to out-adapt the adversary.
122
To stand-up these teams quickly, the CCMDs could leverage the enterprise contracting vehicles
through the JAIC to access a pre-vetted pool of talent with AI engineering, data science, and productmanagement competencies. If local contracting vehicles are used, contract provisions should requirethat all development efforts are interoperable with and leverage the digital ecosystem.

In this way, the AI delivery teams will contribute to a growing resource of shared data and software
within the digital ecosystem by consuming ecosystem services, developing and fielding tailored AI
capabilities, and integrating them into sustainable projects available for use across the department.

123

As an example, both Army Futures Command (AFC) and Army Special Operations Command(USASOC) use a model known as “tactical data teams.” This model brings AI/ML expertise forward
to the field in the form of three- to six-person teams to build AI solutions for real-time operational
problems. Executed by a small business, Striveworks, under contract with AFC and USASOC, they are
currently supporting efforts in Central Command and Indo-Pacific Command Areas of Responsibility.

124

125
These are similar interactions with the digital ecosystem as those taken by the delivery teams at
Combatant Command HQ, only the forward-deployed development team will be consuming digital
ecosystem services locally on their provisioned mobile platform. Collocation of the developers with
operators will drive real-time experimentation and shorten application feedback loops.
126
DoD lacks reliable budget data to track its investments in AI and other critical technologies;
a weakness that should be addressed at the source with AI applications that assist humans ingenerating program descriptions and other budget artifacts.
127

For a full discussion of how AI will change warfare, see Chapter 3 of this report.

128

For a list of priority AI R&D research areas, see Chapter 3 of this report.

129
The Defense Science Board has recommended the level of 3.4% to mirror best practices in theprivate sector multiple times. Department of Defense Research, Development, Test, and Evaluation(RDT&E): Appropriations Structure, Congressional Research Service at 12 (Oct. 7, 2020), https://fas.
org/sgp/crs/natsec/R44711.pdf.

While defense budgets are projected to flatten or decline in the coming years, the threat
environment will only increase in complexity. To meet these new realities, we must create more roomin the budget while simultaneously increasing the lethality of our forces. By retiring legacy systems
and investing more in emerging technologies and, over the longer term, portfolios of attritablesystems, DoD can pursue these needs in tandem, boosting the composability and adaptability of ourmilitary forces.

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Blueprint for Action: Chapter 2 - Endnotes
Former Secretary of Defense Mark Esper pioneered his “night-court” budgeting process as
Army Secretary (2017-2019) and later applied it Department-wide. He “took a hard look at legacy
department programs and cut a number of them, refocusing funds on efforts to challenge China
and Russia.” As Army Secretary, he “helped guide those restructurings through Congress, and theprocess, which found around $25 billion in savings, has garnered largely positive reviews.” Aaron
Mehta & Joe Gould, Night Court Comes to the Pentagon, Defense News (Aug. 28, 2019), https://www.defensenews.com/pentagon/2019/08/28/night-court-comes-to-the-pentagon/. According to
the Pentagon’s press release detailing the highlights of the FY2021 budget proposal, the processapplied defense-wide generated $5.7 billion in FY2021 savings, $0.2 billion in Working Capital Fund
efficiencies, and another $2.1 billion in activities and functions realigned to the Services. Press
Release, The Office of the Under Secretary of Defense for Comptroller, DoD Releases FY 2021
Budget Proposal, https://comptroller.defense.gov/Portals/45/Documents/defbudget/fy2021/fy2021_
Press_Release.pdf.

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This echoes a recommendation made by the Future of Defense Task Force. The Task Forcerecommended that Congress commission the RAND Corporation (or similar entity) and theGovernment Accountability Office to study legacy platforms within the Department of Defense anddetermine their relevance and resiliency to emerging threats over the next 50 years. The Task Forcefurther recommended that upon completion of the studies, “a panel should be convened, comprising
Congress, DoD, and representatives from the industrial base to make recommendations on which
platforms should be retired, replaced, or recapitalized.” Future of Defense Task Force Report 2020,
House Armed Services Committee at 8 (Sept. 23, 2020), https://armedservices.house.gov/_cache/files/2/6/26129500-d208-47ba-a9f7-25a8f82828b0/424EB2008281A3C79BA8C7EA71890AE9.futureof-defense-task-force-report.pdf.

As noted in the discussion above on building a technical backbone, new programs should alsoadhere to the digital ecosystem reference design.

133

134
See Interim Report and Third Quarter Recommendations, NSCAI at 66 (October 2020), https://www.nscai.gov/previous-reports/.
135

The purpose and proposed contents of such a Technology Annex are discussed below.

136
See recommendations for the IC to increase S&T expertise and intelligence collection in Chapter 5of this report.

In its response to the 2017 NDAA provision creating USD (R&E), the DoD specified that the neworganization would organize around three major themes. The first was an SIAC that would focus
on understanding the enemy’s capabilities and vulnerabilities, conducting analysis on our own
U.S. capabilities, tracking technology trends across the globe and assessing potential/emerging
threats and/or future opportunities that warrant action, that merit investment. However, since theestablishment of USD (R&E), the SIAC has been downgraded from a direct report to the Under
Secretary and largely focused on examining threat technologies for OSD customers. See Reportto Congress, Restructuring the Department of Defense Acquisition, Technology and Logistics
Organization and Chief Management Officer Organization, U.S. Department of Defense at 8 (August2017), https://dod.defense.gov/Portals/1/Documents/pubs/Section-901-FY-2017-NDAA-Report.pdf[hereinafter 2017 AT&L Reorganization Plan]

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138

USD (R&E) has the mandate and authority to perform this function. See DoDD 5137.02 at 5-6.

This is consistent with a recommendation made in Chapter 3 of this report that the DoD should
integrate AI-enabled applications into all major Joint and Service exercises and, as appropriate, into
other existing exercises, wargames, experiments, and table-top exercises. See also Second Quarter
Recommendations, NSCAI at 27 (July 2020), https://www.nscai.gov/previous-reports/.

139

This would also directly support objectives of Section 1102 of the FY2021 NDAA with respect toutilization of public-private talent exchanges. Pub. L. 116-283, William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

140

141

For example, via the go-to-market strategy outlined above.

An unclassified version of the strategy must be communicated externally, to where the bulk of the
AI talent resides. Shifting to a more integrated and transparent communication of priorities would
enable Defense primes and non-traditionals to plan and invest more to help meet DoD R&D needs.
See Tab 1 - Issue 3: “Expanding Industry’s Role in DoD’s AI R&D to Develop Next-Generation
Capabilities” in Interim Report and Third Quarter Recommendations, NSCAI at 48 (October 2020),
https://www.nscai.gov/previous-reports/.

142

143
This could be done via the reference design for the digital ecosystem outlined above. As statedabove, adherence to the reference design should be driven top-down via a memorandum from theSecretary of Defense and enforced through the Joint Requirements Oversight Council (JROC).

For example, under microelectronics, this might include advancing AI multi-chip packages,
development of quantifiable assurance, 3D chip stacking, photonics, carbon nanotubes, gallium
nitride transistors, domain-specific hardware architecture, electronic design automation, andcryogenic computing. As recommended by NSCAI in our First Quarter Recommendations. See First
Quarter Recommendations, NSCAI at 51 (March 2020), https://www.nscai.gov/previous-reports/.

144

145
This should be coordinated appropriately with the relevant legal and ethics officials to avoid any
potential conflicts of interest.

OUSD (R&E) could leverage existing Intergovernmental Personnel Act authorities as well as thepilot Public-Private Talent Exchange Program. See Department Of Defense Public-Private Talent
Exchange (PPTE) Program: Questions/Answers, DoD Defense Civilian Personnel Advisory Service(Aug. 23, 2018), https://www.dcpas.osd.mil/Content/Documents/PPTEQuestions_ Answers23Aug2018.
pdf; Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for Fiscal
Year 2021, 134 Stat. 3388 (2021) (directing that the Department of Defense establish public-private
exchange programs to support the defense modernization priorities).

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BLUEPRINT FOR ACTION: CHAPTER 3

Chapter 3: AI and Warfare
Blueprint for Action

If U.S. forces are not organized, trained, and equipped for a new warfighting paradigm thatis emerging because of artificial intelligence (AI) and other emerging technologies, they
will be outmatched and paralyzed by the complexity of the future battlefield.
This Blueprint for Action includes five top-line recommendations to achieve military AI
readiness and prepare our forces for the future: 1) Drive organizational reforms throughtop-down leadership; 2) Develop innovative warfighting concepts; 3) Establish AIreadiness performance goals; 4) Develop and fund advanced technologies and R&D; and
5) Promote AI interoperability and the adoption of critical emerging technologies among
U.S. allies and partners.
Recommendation

Recommendation: Drive organizational reforms through top-down leadership.
Continuously out-innovating the competition requires strong commitment from the top
civilian and military leaders directing the rapid development and adoption of innovative anddisruptive approaches to warfare through top-down governance and oversight processes.
Action for the Department of Defense and the Office of the Director of National Intelligence:• Establish a Steering Committee on Emerging Technology, tri-chaired by the
Deputy Secretary of Defense, the Vice Chairman of the Joint Chiefs of Staff, and
the Principal Deputy Director of National Intelligence.1
o The Secretary of Defense and Director of National Intelligence should issue a
directive immediately establishing the senior oversight committee described above.
o The Steering Committee on Emerging Technology provides a forum to drivechange, focus, and action on emerging technology that otherwise would not beprioritized. It will enhance intelligence analysis related to emerging technology;
connect strategic vision to organizational change; focus concept and capability
development on emerging threats; guide defense investments that ensure
America’s strategic advantage against near-peer competitors; and provide theauthority to drive technology adoption and application by the Department.
• Assign the tri-chair Steering Committee on Emerging Technology responsibility foroverseeing the development of a Technology Annex to the next National Defense
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A I A N D WA R FA R E

Actions for the Department of Defense:• Ensure all future JAIC Directors are a three-star general or flag officer withsignificant operational experience who reports directly to the Deputy Secretary of
Defense.3o Three-star leadership allows the JAIC to engage with the services at a senior rank
and within their command structure. Operational experience enables the Director
to understand how AI can serve operational requirements and better communicate
with the services as to how AI meets capability needs.
• Appoint Under Secretary of Defense for Research and Engineering (USD (R&E))
as the co-chair and chief science advisor to the Joint Requirements OversightCouncil.4
o To accelerate AI and other emerging technologies for competitive advantage,
USD (R&E) must play a central role in connecting technological advancements inresearch and development to joint operational requirements.
Action for Congress:• In the Defense Authorization Act (NDAA) for Fiscal Year 2022, establish a Steering
Committee on Emerging Technology and National Security Threats and designatethat it be tri-chaired by the Deputy Secretary of Defense, the Vice Chairman of the
Joint Chiefs of Staff, and the Principal Deputy Director of National Intelligence.5

Recommendation

Recommendation: Develop Innovative AI-enabled Warfighting Concepts, Informed byExperimentation, Wargames and Real-world Exercises.
Battlefield advantage will shift to those who harness superior data, connectivity, compute
power, algorithms, and overall system security to new warfighting concepts. Developing
new operational concepts requires Services to incentivize experimentation, and foster aculture of “thinking Red”––in other words, considering the strategies of potential adversaries
when developing operational concepts.
Actions for the Department of Defense:• Develop innovative operational concepts that integrate new warfighting
capabilities with emerging technologies.
o The Deputy Secretary of Defense and the Vice Chairman of the Joint Chiefs should
issue a memorandum directing Components and Services to develop a complete
deterrence concept for systems warfare that leverages human-machine teaming,AI, and associated technology to prevail against intelligent adversary systems ofsystems.
o Under the guidance from the tri-chair Steering Committee on Emerging
Technology, USD (R&E) should receive $5 million for a team (approximately 20
people) in FY2022 funding to research and develop new AI-enabled capabilities fordevelopment and testing of advanced operational concepts. This project must be
done in conjunction with DARPA and other capability offices to share the costs offilling technological gaps discovered during the analytic process.

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o These operational concepts should be institutionalized in classified DoD documentsthat drive comprehensive force development and investment prioritization.
Confidential demonstrations should be executed to realize the deterrence concept.
• Integrate AI-enabled applications into all major Joint and Service exercises and, as
appropriate, into other existing exercises, wargames, and table-top exercises.
o The Deputy Secretary of Defense and the Vice Chairman of the Joint Chiefs should
issue a memorandum calling for inclusion of AI and other emerging technologies
into existing exercises, wargames, and table-top exercises. This includes large-scale
exercises and smaller, more frequent events at all echelons.
o The purpose of this would be to realize connectivity between systems and sensors,
rapid data analysis, faster and more informed decision-making, and more distributed
operations.
o Concept writers should participate in all major technology demonstrations.
o Develop performance objectives and associated metrics to assess integration of AIenabled applications into exercises, wargames, experiments, and TTXs.
• Incentivize experimentation with AI-enabled applications through the Warfighting
Lab Incentive Fund (WLIF).
o DoD should incentivize experimentation with AI applications across the Department
at every level possible by establishing either a special category or prioritizedevaluation criteria within the WLIF for proposals that incorporate AI applications.
■

Experimentation with AI-enabled applications are particularly well-suited forthe space, cyber, and information domains because of the high volumes of
24/7 data generated in these domains.

o The Steering Committee on Emerging Technology should provide annual guidance
for selection of WLIF proposals for funding based on priorities developed in theTechnology Annex to the NDS.
o DoD should increase WLIF funding by $10 million annually specifically for AIenabled applications.6• Encourage a culture of “thinking Red.”o DoD working closely with the Intelligence Community should develop a granular
understanding of our main competitors’ approach to systems confrontation. This will
help the Department to better understand our competitors’ operational concepts
and to eventually avoid battlefield surprise.
o Joint Warfare Analysis Center (JWAC) should be the lead to develop competitors’
operational concepts. Estimate $2.5 million allocation for a 10-week, 10-gameseries devoted to mastering red thinking.
■

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Red-thinking games must: 1) Integrate deterrence-credibility stretch problems
from key classified DoD documents; 2) clear denial concepts for our moststressing scenarios; 3) Be conducted with realistic basing and naval posture;4) the highest standards of incorporating the best available intelligence; 5)
the highest standards of AI-enabled modeling and simulation that ingest
and mimic red operations; 6) rigorous two-player adjudication with physicslevel detail on red capabilities; and 7) rapid turnaround on force development
considerations for the Secretary of Defense.

A I A N D WA R FA R E

o The Office of the Secretary of Defense and the Joint Staff should issue amemorandum directing all military educational institutions to foster in theircurriculum the culture of “thinking Red.”
Actions for Congress:• Congress should appropriate an additional $17.5 million to DoD’s budget to support
innovative concept development.
Recommendation: Establish AI and digital readiness performance goals.

Recommendation

To drive outcomes and accountability and provide a means for oversight of Department
efforts regulated to AI, DoD should establish key performance objectives and accompanying
metrics for AI and digital readiness.7
Actions for the Department of Defense:• By the end of 2021, establish AI and digital readiness performance goals. To
achieve more substantial integration of AI across DoD, the Deputy Secretary of
Defense should:
o Direct DoD components to assess military AI and digital readiness through existingreadiness management forums and processes. The Steering Committee on
Emerging Technology should work closely with the Under Secretary of Defense
for Personnel and Readiness,8 the Joint Staff, and the JAIC to ensure the identified
AI and digital readiness performance objectives are incorporated into the military
services’ readiness reporting recovery frameworks, and resourcing strategies.
o Direct the military services to accelerate review of specific skill gaps in AI, to inform
recruitment and talent management strategies and provide a report within 12
months.
■■■■Assess the number of civilian personnel needed in software developer,
software engineer, knowledge management, data scientist, and AI career fields
for both management and specialist tracks.
Assess the number of military personnel needed in software development,
data science, and AI career fields, in both management and specialist tracks,
and for commissioned and enlisted personnel.
Assess the specialties and personnel required for a DoD and military service
digital corps.
Establish annual retraining and recruiting goals to create and maintain the
personnel described above.

• Direct the military services, in coordination with the Under Secretary of Defense(Acquisition and Sustainment), the Joint Staff, and the Defense Logistics Agency,
and enabled by enterprise services and expertise at the JAIC, to prioritize
integration of AI into logistics and sustainment systems wherever possible.
o The Deputy Secretary of Defense should issue a memorandum directing themilitary services to accelerate use of AI and apply commercial best practices inp340

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predictive analytics for maintenance and supply chain to optimize all classes ofsupply, equipment, and parts.9 The Deputy Secretary of Defense should establish a$100 million fund, administered by the Under Secretary of Defense (Acquisition andSustainment) to provide matching contributions to service and agency efforts basedon estimated financial or operational return on investment.
o By the end of 2021, the Under Secretary of Defense (Acquisition and Sustainment),
supported by Senior Acquisition Executives and in coordination with the DoD CDO
and the JAIC, will establish performance objectives and identify best approaches
to achieve data-ready systems in logistics and sustainment systems to support
application of AI. Disparate conditions of data-readiness in existing and futuresystems will require differential approaches to achieve AI-readiness. Broadly, thesecategories of data-readiness are:■■■

Systems with proprietary vendor data (ex. F-35 Joint Strike Fighter, M1 Abrams
Tank)
Systems with government-owned data (ex. Maintenance and Availability Data
Warehouse)
Systems that are data-ready (government-owned data that has been
documented/tagged for storage/discovery and has published schema fordata access (ex. Next Generation Air Dominance, T-7 Redtail, Ground Based
Strategic Deterrent).

Actions for Congress:• Require the Secretary of Defense to establish performance objectives andaccompanying metrics for AI and digital readiness, and provide an update toCongress no later than 120 days after approving these goals.
Recommendation

Recommendation: Develop and Fund Advanced Technologies and R&D.
Development and fielding of advanced AI-enabled technologies will remain a criticalcomponent of DoD’s ability to achieve decision advantage on the battlefield.
Actions for the Department of Defense:• Define a joint warfighting network architecture by the end of 2021. OSD CIO andthe Joint Staff, in coordination with the Services, should issue a memorandum directing
the architecture for a secure, warfighting command and control network. A Serviceagnostic warfighting network will enable better integration of AI-enabled technologies
with current and future weapon systems. The OSD CIO should provide $5 million to theright entity to accomplish this design.
• Invest in priority AI R&D with the support areas that could support future militarycapabilities. To accelerate adoption of AI in warfighting missions, the Under Secretaryof Defense (R&E) should increase investments10 in the following priority R&D areas tosupport future AI-enabled warfighting capability. If advanced, this could build nearand long-term AI-driven capabilities for competitive advantage in a future method of
conflict defined by AI. These should be viewed as investments in deterrence in theinterim—pursuing critical incremental advancements—and in the long term—building
new capabilities yet to be determined that will sustain overmatch. Investments should
include:p
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A I A N D WA R FA R E

o USD (R&E),,with the support from DARPA, should prioritize AI R&D for the following
topics:■

The future of teaming—to advance human-AI and AI-AI teaming■

Advanced scene understanding■Intelligent edge devices, computing, and networking■

Robust and resilient AI■

Testing and Evaluation, Verification and Validation (TEVV)■

Integrated AI, modeling, and simulation for decision support■

Autonomous AI systems■

Toward more general Artificial Intelligence

Recommendation: Promote AI interoperability and the adoption of critical emerging
technologies among allies and partners.

Recommendation

America’s enduring relationships with allies and partners represent asymmetric advantages
over competitors and adversaries. Differential adoption of AI across military alliances
and intelligence partnerships creates interoperability risk that threatens allies’ politicaland military cohesion, the resiliency of alliance structures, and the efficacy of coalition
operations. The recommendations that follow reflect a holistic approach to furthering
cooperation around AI and emerging technologies in the context of defense, intelligence,and security arrangements. They focus on interoperability and improving capacity andcapability development to foster competitive military and intelligence advantages.
Component 1: Enhance Five Eyes efforts to achieve interoperable AI systems.
Actions for the Department of Defense and the Office of the Director of National Intelligence:• Coordinate with Five Eyes officials to conduct assessments of the comparative
strengths and gaps in AI-related technologies and applications among the Five
Eyes allies.
o Assessments would evaluate Doctrine, Organization, Training, Materiel, Leadershipand Education, Personnel, Facilities and Policy (DOTMLEPF-P) across the alliancefor adopting AI, and future plans for AI-enabled warfighting architectures.
• Coordinate with Five Eyes officials to develop a five-year plan for improving AI
interoperability across the Five Eyes alliance.
o Proposed plans should include, among other things, combined research priorities,
development objectives, experimentation, methods to facilitate data sharing, usecases, and common standards for TEVV of AI-enabled systems and interoperability
standards. It should also include stress tests for supply chains in critical industries
and corresponding risk-mitigation measures.

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o In developing plans, Five Eyes leaders should enhance ongoing efforts of theTechnical Cooperation Program,11 through the AI Strategic Challenge (AISC), tofurther align interoperable AI systems.
o Five Eyes leaders should continue to advance the joint development of intelligenceproducts by expanding efforts to “increase collection access and reliability, improve
the quality and quantity of partner data and analysis, align strategic capabilities andemerging technologies, and promote compatibility across digital architectures andanalytic tradecraft.” 12
Actions for the Department of Defense:• Direct a series of AI demonstration pilot projects and host an AI wargame andexperimentation series.
o Based on the recommended assessments and planning above, the Secretary of
Defense should: 1) Direct a series of AI demonstration pilot projects in areas suchas predictive maintenance, autonomous logistics, and sensor fusion with Five Eyes
partners across the Future Years Defense Program; and 1) host an AI wargame andexperimentation series, beginning with Five Eyes allies.
Component 2: Accelerate NATO AI adoption.
NATO and its member states recognize that AI-related technology has transformative
potential for collective security. Coordinated, accelerated, responsible adoption of AI must
be an urgent priority across the Alliance in order to address the challenge presented byalgorithmic warfare.13 NATO allies need to dedicate personnel and resources to support
the development and operational applications of AI-related, and other Emerging and
Disruptive Technologies (EDTs).
Actions for the Departments of Defense and State:• Provide clear policy guidance, technical expertise, and resource support to assist
and accelerate NATO’s AI-related initiatives to:o Ensure AI technologies are incorporated into the NATO Defense Planning Process,NATO Warfighting Capstone Concept, and plans for Deterrence and Defense of theEuro-Atlantic Area.
o Evaluate DOTMLPF-P for AI adoption and future plans for AI-enabled warfighting
architecture and interoperability in allied or coalition environments.
o Support and coordinate development and adoption of foundational definitions,operational and data-sharing practices, technical standards, and architectures
focused on interoperability, privacy, and responsible, legal deployment of AI.
o Ensure the NATO Science and Technology Strategy anticipates technologicaldevelopments to guide NATO and national research and development priorities.
o Develop NATO international staff and allied nation technical expertise.
o Conduct simulations, wargaming, experimentation, and pilot projects with use cases
for data fusion, data exploitation, and interoperability.

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o Assist in the collaboration with partners beyond the NATO Alliance, including
industry and academia• Develop, with NATO allies, a proposal for an Alliance-wide AI Implementation
Strategy deliverable for NATO Heads of State.
o The proposal should build upon key recommendations of the NATO Reflection
Group report submitted to the Secretary General,14 and should provide guidance on
the areas identified above.15
Component 3: Foster the JAIC AI Partnership for Defense (AI PfD) as a critical vehicle tofurther AI defense and security cooperation.
Launched in 2020, the AI PfD is a DoD-led effort to convene partner nations to “provide
values-based global leadership” on adoption of AI in the defense and security context.16
Current members include Australia, Canada, Denmark, Estonia, Finland, France, Israel,
Japan, Norway, South Korea, Sweden, and the United Kingdom.
Action for the Department of Defense:• Prioritize and foster the AI PfD as a critical space for democratic allies and partners
to work through defense issues on AI.
o The AI PfD can enhance U.S. efforts to accelerate AI adoption across NATO by
supporting key foundational efforts related to data governance and management,
infrastructure and technical, legal, and ethics expertise. DoD and Congress should
provide continued support to enable the AI PfD to further AI cooperation on defense
and security with key allies and partners.
Component 4: Incorporate AI into Indo-Pacific defense cooperation efforts.
Increased opportunities exist for collaboration with Quadrilateral Security Dialogue (Quad)partners India, Japan, and Australia, and other nations committed to advancing a free andopen Indo-Pacific region.
Actions for the Departments of Defense and State:• Build on the Quad framework and negotiate formal AI-related defense andintelligence cooperation agreements in the Indo-Pacific region with Australia,
India, and Japan, as well as with New Zealand, South Korea, and Vietnam.
o This could be done in connection with broader conventional defense andintelligence relationships, and existing security cooperation agreements, or in astand-alone manner, bilaterally or multilaterally. The U.S. Government should alsoprioritize AI interoperability at ministerial and working level meetings.17
Component 5: Create an Atlantic-Pacific Security Technology Partnership to improve
defense and intelligence interoperability across Europe and the Indo-Pacific.

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An Atlantic-Pacific technology partnership would seek to improve capability andinteroperability by bringing together technology innovation with allied and partner militaries
and intelligence communities, whether in a NATO, coalition, or other multinational context.
Action for the Departments of Defense and State:• Advance a deliberate NATO partnership with Indo-Pacific allies and partners for
AI-enabled defense cooperation.
o A NATO-Indo-Pacific partnership focused on AI is needed to facilitate earlycollaboration and lay the groundwork for interoperability among different allied andpartner warfighting architectures.
o Plans for such a partnership should include guidance from the tri-chair Steering
Committee on Emerging Technology for data sharing, common standards, wargame
and experimentation, and improving interoperability of AI systems and warfighting
architectures.
Component 6: Modify authorities and processes in order to improve DoD’s ability to
conduct international capability development.
DoD requires more flexibility in its ability to develop, test, and field AI-enabled systems
with existing and new foreign partners, both public and private.
Action for Congress:• Expand the flexibility and the agility of the Secretary of Defense’s authority toengage in cooperative R&D agreements.
o Legislation should permit DoD to pursue cooperative projects with privatecompanies, academic research centers, and defense and non-defense
governmental entities within NATO, major non-NATO allies, and friendly foreigncountries, without a direct showing to the improvement of conventional defense
capabilities.

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o Legislation should also account for partners’ non-monetary contributions, including
the value of R&D capabilities and the strategic partnerships, when assessing
potential projects.
Actions for the Department of Defense:• Review and revise policies related to International Armaments Cooperation toprovide flexibility for AI and software driven partnerships.
o The review should include policies related to technology transfer, national
disclosure, information and equipment use, equitability requirements, funding
requirements, and contracting.
o DoD should update policies to provide greater delegation of authorities to Military
Departments and Defense Agencies to conclude international agreements.
• Revise DoD Instruction (DoDI) 5530.03, “International Agreements,”18 to provide
appropriate guidance on AI and software-driven partnerships.
o DoDI 5530.03 should be revised to: 1) enable continuous collaboration on
evolutionary hardware and software products that need continuous update acrossresearch, development, testing, evaluation, and operational deployment withinternational partners; 2) provide sufficiently flexible entry and exit criteria for all
types of international partners (governmental, industry, and academic) to facilitate
capabilities, products, knowledge, and services at the point of need; and 3) provide
guidance for acceptable thresholds and limits to balance the protection andpromotion aspects of AI-related capability development with international partners.

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Blueprint for Action: Chapter 3 - Endnotes
The Commission acknowledges section 236 of the FY 2021 National Defense Authorization Act,
which permits the Secretary of Defense to establish a steering committee on emerging technology
and national security threats composed of the Deputy Secretary of Defense; the Vice Chairmanof the Joint Chiefs of Staff; the Under Secretary of Defense for Intelligence and Security; theUnder Secretary of Defense for Research and Engineering; the Under Secretary of Defense for
Personnel and Readiness; the Under Secretary of Defense for Acquisition and Sustainment; theChief Information Officer; and such other officials of the Department of Defense as the Secretary
determines appropriate. However, the structure described in section 236 does not include leadershipfrom the Intelligence Community and will thus not drive the intended action. See Pub. L. 116-283,
William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388
(2021), https://docs.house.gov/billsthisweek/20201207/CRPT-116hrpt617.pdf.

1This action is described in greater detail in the Chapter 2 Blueprint for Action, which designates
a member of the Steering Committee on Emerging Technology the Executive Agent responsible fordeveloping the Technology Annex and outlines the recommended contents and use for the Appendix.

2

Notably, section 236 of the FY2021 NDAA designates the Director of the Joint Artificial Intelligence
Center as a direct report to the Deputy Secretary of Defense. See Pub. L. 116-283, William M. (Mac)Thornberry National Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

3This echoes an action in the Chapter 2 Blueprint for Action, which emphasizes that to reduce
redundancies, increase interoperability, and drive a system-of-systems approach to requirements
development and management, USD (R&E) must have a stronger role in the Joint Requirements
Oversight Council.

4

As indicated above, DoD and ODNI have the authority to establish such a forum without legislative
action. However, codifying it into law will ensure that it is sustained through leadership transitions.
The defense committees could consider using the FY2022 NDAA to amend section 236 of the
FY2021 NDAA. As written, section 236 only “permits” the establishment of such a committee;
additionally, the provision does not clearly denote chairs of the committee and does not include
any Intelligence Community representation. This recommendation is also discussed in Chapter 5 ofthis report. Additionally, Chapters 2 and 5 of this report recommend establishing funds to mature,operationally prototype, and transition exceptionally promising AI-enabled technologies. For DoD,
USD (R&E) would control those funds and, for the IC, the ODNI CTO would control those funds. Thoseinvestments should be informed by the Steering Committee on Emerging Technology.

5

FY2021 O&M funding was $42.4 million. J7 received 110 proposals for FY2021 WLIF funding andselected 20 experimentation efforts. NSCAI staff discussions with JS/J7.

6

General Charles Q. Brown, Jr. & General David H. Berger, To Compete with China and Russia, theU.S. Military Must Redefine ‘Readiness,’ Washington Post (Feb. 1, 2021), https://www.washingtonpost.
com/opinions/2021/02/01/brown-berger-military-readiness/.

7“P&R must enable, guide, and assess a strategically ready Department of Defense for employment
by the Joint warfighter when and where it is needed, adaptive to the strategic geopolitical andthreat environments, and evolving military-technological advances.” Preserving Our Competitive
Advantage, Personnel And Readiness Strategy For 2030, U.S. Department of Defense at 13(October 2020), https://prhome.defense.gov/Portals/52/Documents/Strategy/PR_Strategy_FINAL_.
pdf? ver=KY6Vacn3kT1Gd9fNxnR34w%3D%3D.

8

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In the FY2021 NDAA, Title II, section 234, Congress directed “the Secretary of Defense to identify aset of no fewer than five use cases of the application of existing artificial intelligence enabled systems
to support improved management of enterprise acquisition, personnel, audit, or financial management
functions, or other appropriate management functions, that are consistent with reform efforts that
support the National Defense Strategy.” Pub. L. 116-283, William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

9

10With additional funding for DoD investments in AI R&D recommended in the Chapter 2 Blueprint for
Action.
11
DoD Instruction 3100.08: The Technical Cooperation Program (TTCP), U.S. Department of Defense(Oct. 15, 2018), https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/310008p.
pdf?ver=2017-11-30-114948-343.

The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines, Office of the Director of
National Intelligence at 10 (Jan. 16, 2019), https://www.dni.gov/files/ODNI/documents/AIM-Strategy.
pdf.

12

Memorandum from Robert O. Work, Deputy Secretary of Defense, Establishment of an Algorithmic
Warfare Cross-Functional Team (Project Maven), U.S. Department of Defense (April 26, 2017), https://www.govexec.com/media/gbc/docs/pdfs_edit/establishment_of_the_awcft_project_maven.pdf.

13

14NATO 2030: United for a New Era. Analysis and Recommendations of the Reflection Group
Appointed by the NATO Secretary General, NATO at 29-31 (Nov. 25, 2020), https://www.nato.int/cps/
en/natohq/news_179730.htm.

For further detail, see Interim Report and Third Quarter Recommendations, NSCAI at 187-195(October 2020), https://www.nscai.gov/previous-reports/.

15

The AI PfD seeks to align “like-minded nations to promote the responsible use of AI, advanceshared interests and best practices on AI ethics implementation, establish frameworks to facilitate
cooperation, and coordinate strategic messaging on AI policy.” Joint Statement, AI Partnership for
Defense (Sept. 15-16, 2020), https://www.ai.mil/docs/AI_PfD_ Joint_Statement_09_16_ 20.pdf. TheAI PfD held its second formal dialogue in January 2021. DoD Joint AI Center Facilitates Second
International AI Dialogue for Defense, JAIC (Jan. 27, 2021), https://www.ai.mil/news_01_ 27_ 21-dod_joint_ai_center_facilitates_second_international_ai_dialogue_for_defense.html.

16

17
See Tab 5 - Recommendation 2: “The Departments of State and Defense should negotiate formal AI
cooperation agreements in the Indo-Pacific region with Australia, India, Japan, New Zealand, South
Korea, and Vietnam” in Interim Report and Third Quarter Recommendations, NSCAI at 196 (October2020), https://www.nscai.gov/previous-reports/.

DoD Instruction 5530.03: International Agreements, U.S. Department of Defense (Dec. 4, 2019),
https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/553003p.PDF.

18

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Chapter 5: AI and the Futureof National Intelligence
Blueprint for Action

Intelligence will benefit from rapid adoption of artificial intelligence (AI)-enabledtechnologies more than any other national security mission. However, critical barriers keep
the Intelligence Community (IC) from turning this potential into real capabilities that are
scaled across agencies.
An Ambitious Agenda: AI-Ready by 2025.
To build on the progress that individual agencies have made, the IC should set theambitious goal of adopting and integrating AI-enabled capabilities across every possible
aspect of the intelligence enterprise as part of a larger vision for the future of intelligence.
Recommendation

Recommendation: Empower the IC’s science and technology leadership.
Actions for Office of the Director of National Intelligence (ODNI):• The DNI should designate the Director of Science and Technology (S&T) as the IC
Chief Technology Officer (CTO)1 and direct the IC CTO to:o Develop and monitor IC-wide metrics for AI investments, AI implementation, AI
outcomes, and AI readiness.
o Ensure maximum sharing and reuse of AI models, code, and tools across the IC toprevent unnecessary duplication where possible.
o Establish policies on, and supervise, IC research and engineering, technology
development, technology transition, appropriate prototyping activities,
experimentation, and developmental testing activities.
o After congressional approval and appropriation, manage a fund that would allow the
ODNI to identify and invest in AI applications with outsized potential that may nothave an identified source of agency or program funding as they near the end of their
S&T life cycle.
• The IC CTO, in coordination with the IC Chief Information Officer (CIO), Chief
Data Officer (CDO), and Chief Information Security Officer, should oversee theestablishment of common technical standards and policies for the IC. Thesestandards and policies should be coordinated with the DoD to promote maximum
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o An Application Programming Interface (API)-driven open architecture andassociated policies that support the infrastructure to enable AI.3
o Multi-level security standards for technical solutions allowing the movement of data
across security clearance levels and the policies to enable it.
o Data tagging and labeling.
o Data sharing and access, including incentives for data stewards that reward theirability to share their data; shift the culture such that data stewards make it a default
practice of externalizing their data via APIs, with appropriate levels of access
restriction and control.
o Common standards for machine readable processing, exploitation, anddissemination (PED) products.
o Automated and reciprocal Authority to Operate (ATO) processes that include rapid
code certification and accreditation processes.
o Documentation strategies for data, models, and systems, and of the AI life cycle
infrastructure to support traceability, training and testing procedures, and humanAI design guidelines.4
o Technical standards for algorithms in support of interpretability and explanation,and policies to strengthen accountability.
o Technologies and operational policies that align with privacy preservation, fairness,
inclusion, human rights, and documentation of value considerations and trade-offs.5
o Alternative hiring authorities for term-limited appointments appropriate fortechnical positions, such as Special Government Employees (SGE), highly qualified
experts (HQE), and Intergovernmental Personnel Act (IPA) detailees.
o Expanding the use of prize challenges as alternatives to traditional procurement.
o Program and contracting guidance for well-documented and hardened APIs,
data access and sharing across the IC, and provisions for the sharing and reuse ofsoftware products across the IC.
• The IC CTO, in coordination with DoD, should develop a Technology Annex to the
National Intelligence Strategy (NIS).6o The appendix should establish technology roadmaps to adopt AI-enabledapplications to solve operational intelligence requirements. The appendix should
address current issues within the IC, to include:■■

Aligning technical standards and policies with DoD to ensure seamless
interoperability as well as make the Executive branch a better customer andmore attractive market for industry.
Identify and promote acquisition reforms and methods that ensure the IC can
rapidly procure and field systems to its intelligence professionals.

o The Technology Annex to the NIS should, at a minimum, include:■Intelligence support requirements, including how the IC analyzes the global
environment and monitors technological advancements, adversarial capability
development, scientific and technical cooperation among U.S. competitors,
and emerging threats.
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■

■

■

■

■

■

■

Functional requirements and technical capabilities necessary to enable
concepts that address each challenge.
A prioritized, time-phased plan for developing or acquiring such technicalcapabilities, that takes into account research and development timelines,
a strategy for public private partnerships, and a strategy for connecting
researchers to end users for early prototyping, experimentation, and iteration.
Additional or revised acquisition policies and workforce training requirements
to enable IC personnel to identify, procure, integrate, and operate thetechnologies necessary to address the intelligence requirements.
Infrastructure requirements for developing and deploying technicalcapabilities, including data, compute, storage, and network needs; a resourced
and prioritized plan for establishing such infrastructure; and an analysis of thetesting, evaluation, verification, and validation (TEVV) requirements to support
prototyping and experimentation and a resourced plan to implement them,including standards, test beds, and red-teams for testing AI systems against
digital “denial & deception” attacks.
Consideration of human-factor elements associated with priority technicalcapabilities, including innovative human-centric approaches to user interface,
human-machine teaming, and workflow integration.
Consideration of interoperability with allies and partners, including areas forsharing of data, tools, and intelligence products.
Flexibility to adapt and iterate appendix implementation at the speed oftechnological advancement.

• ODNI should advance and continue to build out a purpose-built IC Information
Technology Environment (ITE) that can fuse intelligence from different domains
and sources.
o The IC ITE should be built in concert with the DoD digital ecosystem outlined
in Chapter 2 of this report; they should focus on a federated system that isinteroperable, integrated, and designed with building block services using the sameinterfaces as the DoD ecosystem.
o The IC should accelerate ad hoc work and continuous experimentation to learn
better how to integrate their systems.
■■

Intelligence fusion promised by AI can only occur when all relevant data is
made available across all systems. Building on the promise of IC ITE, the IC
CIO and CTO should work with their counterparts across the IC and mission
partners to ensure that IC integration and interoperability are given priority
when evaluating technology investments.
The IC CTO should establish a multi-agency accredited learning environment
and test bed where ad hoc work and continuous experimentation can occur
using all relevant intelligence data.

Actions for Congress:• Designate the Director of S&T within ODNI as the IC CTO, and grant that position
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and engineering, technology development, technology transition, appropriate
prototyping activities, experimentation, and developmental testing activities.
• Establish a fund that would allow the DNI to identify and invest in AI applications
with outsized potential that may not have an identified source of agency orprogram funding as they near the end of their S&T life cycle.
• Grant the Director of National Intelligence sufficient budgetary authorities
to enforce technical standards across the IC, including the ability to fence orotherwise withhold funding for programs that are not compliant with establishedcommon standards and policies.
• Establish a 10-year, $1 billion, Program of Record to provide long-term, predictable
funding for technologies identified in the Technology Annex to the National
Intelligence Strategy.
o This funding should target programs or departments with a proven track record oftransitioning new or critical technologies to meet mission needs.
Recommendation: Change risk management practices to accelerate new technology
adoption.

Recommendation

Actions for ODNI:• Establish an IT modernization Senior Risk Management Council (IT SRMC).
o The IT SRMC should be tri-chaired by the IC CTO, CIO, and CDO to promote theeffective governance of significant risk across the IC.
■■

The IT SRMC should report to the Principal Deputy Director of National
Security (PDDNI).
The IT SRMC should become a regular briefing entity in the Deputies
Executive Committee (DEXCOM).

o The IT SRMC should include a senior member from the following IC entities:■ODNI Office of General Counsel■

ODNI Office of Civil Liberties, Privacy, and Transparency■

ODNI Mission Integration Directorate■

Each intelligence agency and service brancho The IT SRMC responsibilities should include:■■

Reviewing existing policies or creating new policies to ensure the IC uses
informed risk acceptance and management practices when considering the
adoption and use of AI technologies.
Advising the DNI on enterprise risk associated with not adopting AI
technologies.

• Address shortcomings in the current implementation of the National Institute ofStandards & Technology (NIST) Federal Information Security Modernization Act(FISMA) Risk Management Framework (RMF).7
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o Recommendations from the IT SMRC should inform the operational risk of not
adopting a new technology as a balance to the technical risks considered in the
RMF, allowing agencies to make better informed decisions on what systems theychoose to bring on line or delay.
o The IC should automate the implementation and simultaneous assessment of RMF
considerations to the greatest extent possible.
o Agencies within the IC often implement the RMF with different, but associated,
policies that can prevent reciprocal accreditation and make it difficult to share tools
among agencies.
o The IC should make accreditation reciprocity within the RMF the standard and apply
a high level of scrutiny to any agency that seeks to not recognize the accreditationof others.
Actions for Congress:• Assess the IC’s approach to risk and work with the IC to ensure the proper balance
between risk acceptance, risk management, and risk avoidance.
Recommendation

Recommendation: Improve coordination between the IC and DoD.
Actions for ODNI:• In coordination with the Secretary of Defense, the DNI should immediately issue a
directive designating the PDDNI as a standing member and/or co-chair to the trichair Steering Committee on Emerging Technology.8
o Absent of Congressional action, the Director of National Intelligence should
work with the Secretary of Defense and members of the Steering Committee on
Emerging Technology, including the Deputy Secretary of Defense and Under
Secretary of Defense for Intelligence and Security, to identify the method andmeans to drive sustained coordination on emerging technology intelligence, policy,
and resourcing.
• Assist DoD, as requested, in developing the Technology Annex to the National
Defense Strategy.9• Work with DoD to establish an AI integration team focused on maximizing
knowledge, data, and model sharing across and between the IC and DoD.
Actions for Congress:• Revise the National Defense Authorization Act for Fiscal Year 2021 (FY2021
NDAA) provision authorizing a Steering Committee on Emerging Technology
by designating it to be tri-chaired by the Deputy Secretary of Defense, the Vice
Chairman of the Joint Chiefs of Staff, and the Principal Deputy Director of National
Intelligence.10

Recommendation

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Recommendation: Capitalize on AI-enabled analysis of open source and publicly available
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A I A N D T H E F U T U R E O F N AT I O N A L I N T E L L I G E N C E

Actions for ODNI:• Develop a coordinated and federated approach to integrate open source
intelligence into all current intelligence processes and products. ODNI should
promote coordination by taking the following actions:o Develop common standards and policies that enable the individual agencies to be
more effective, such as contracting publicly available data sources for common use
across the IC and clarifying or updating policy guidance on the appropriate use of
publicly available and open source information, including with respect to privacy
and civil liberties for U.S. persons or entities.
o Support the IC by identifying reliable industry partners across the spectrum of
information sources and creating contract vehicles to rapidly integrate them into
intelligence work across the IC. This should include establishing a pilot project totest “data-for-tools” exchanges in public-private partnerships.
o Aid the IC in communicating emerging risks and threats to industry and academia
by coordinating the right expertise from across the IC; –for example, byconnecting non-government entities to the Federal Bureau of Investigation for
counterintelligence guidance, or to the U.S. Cyber Command/National Security
Agency for cybersecurity.
o Develop a robust capability for bringing in individuals without security clearances or
awaiting security clearance adjudication and allowing them to work on unclassified
projects that directly support the IC.
• Each individual agency should develop open source capabilities focused on thespecialized applications of open source and publicly available information within
their unique intelligence domains.
Recommendation: Aggressively pursue security clearance reform for clearances at the
Top Secret level and above, and enforce security clearance reciprocity among membersof the IC.

Recommendation

Actions for ODNI:• Develop a Blueprint for Action for security clearance reform for clearances at
the Top-Secret-and-above level including detailed timelines and metrics. The
Blueprint for Action should include:o A collaborative effort with the private sector and academia to develop datainformed behavioral approaches to understanding risk factors and security
clearance adjudication.11
o Reforming identity management to ensure there is seamless security clearance
reciprocity across the IC.
o A mechanism to enforce security clearance reciprocity among members of the IC
and DoD.

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Actions for Congress:• Congress should require the DNI to develop a Blueprint for Action for security
clearance reform for clearances at the Top-Secret-and-above level including
detailed timelines and metrics.
• Where necessary, Congress should reinforce the DNI’s authority as head of the IC
to enforce uniform security clearance policies and practices across the IC.
• Congress should require the DNI and the directors of the major intelligence
services to regularly report on progress to the oversight committees.

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Blueprint for Action: Chapter 5 - Endnotes
We envision the IC CTO as having roles, responsibilities, and authorities similar to the Under
Secretary of Defense for Research and Engineering (USD (R&E)) within the DoD and to help the IC
implement guidance and priorities established by the Steering Committee on Emerging Technology
and the Technology Competitiveness Council.

1

In Chapter 3 of this report, the Commission recommends the creation of a Steering Committee on
Emerging Technology that is tri-chaired by the Deputy Secretary of Defense, the Vice Chairman of
the Joint Chiefs of Staff, and the Principal Deputy Director for National Intelligence. This Committee
should act as a forum through which to drive coordination between the IC and DoD, including the
Chief Technology Officers.

2

Consistent with the DoD digital ecosystem described in the Chapter 2 Blueprint for Action, the API
driven open architecture should: 1) Define a common set of well-documented common interfaces forthe ecosystem’s key components and building blocks; 2) Support and integrate the work of existing
pathfinders up and down the ecosystem technology stack; and 3) Incorporate the process elements
for data authorizations and continuous software ATO reciprocity.

3

Chapter 7 of this report provides more details on improving documentation practices for achievingbaseline robust and reliable AI.

4

Chapter 8 of this report provides details on developing and testing systems per goals of privacy
preservation and fairness.

5

A Technology Annex to the NIS should complement the Technology Annex to the National Defense
Strategy (NDS) recommended in Chapter 2 of this report. The recommended Executive Agent for
the Technology Annex to the NDS (see the Chapter 2 Blueprint for Action), the Under Secretary of
Defense for Research and Engineering (USD (R&E)) should act as the primary interlocutor with the IC
CTO for the creation of a Technology Annex to the NIS.

6

For more information, see FISMA Implementation Project, NIST (Dec. 3, 2020), https://csrc.nist.gov/
projects/risk-management/rmf-overview.
7

The Chapter 3 Blueprint for Action calls for the Secretary of Defense, with support from the Directorof National Intelligence, to issue a directive immediately establishing a tri-chair Steering Committee
on Emerging Technology to oversee development of concepts and capabilities that include emerging
and disruptive technologies to meet the current and future operational challenges facing the nation.

8

For a full discussion of the Technology Annex to the National Defense Strategy, see Chapter 2 of thisreport.

910This action mirrors the Chapter 3 Blueprint for Action. While DoD and ODNI have the authority toestablish such a forum without legislative action, codifying it into law will ensure that it is sustained
through leadership transitions. If, at the drafting of the FY2022 NDAA, the DoD and ODNI haveestablished the tri-chaired Steering Committee recommended herein, Congress should use the
FY2022 NDAA to codify the body into law. If DoD and ODNI have not established the Committee
as described in this report, Congress should include in the FY2022 NDAA a provision revising
the FY2021 NDAA, section 236, which permits the creation of a Steering Committee on Emerging
Technology, but is not structured effectively to improve coordination between the DoD and the IC.
For a full discussion of section 236, see the Chapter 3 Blueprint for Action. The Commission also
recommends that the legislative language be sufficiently broad so as to enable flexibility in theSteering Committee’s roles and responsibilities should they need to adapt as emerging technologies
and Department efforts evolve. See the Draft Legislative Language Appendix to this report.

For more information on the need for an academic and scientific review of behavioral approaches
to security clearance adjudication, see David Luckey, et al., Assessing Continuous Evaluation
Approaches for Insider Threats: How Can the Security Posture of the U.S. Departments and Agencies
Be Improved?, RAND Corporation at 28-34 (2019), https://www.rand.org/pubs/research_reports/
RR2684.html.
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Chapter 6: TechnicalTalent in Government
Blueprint for Action

The United States Government needs digital experts now or it will remain unprepared
to buy, build, and use AI and its associated technologies. Expanding digital expertise is
the most important step the government can take to modernize. While this challenge isrecognized, few parts of government have adequately invested in building their digital
workforce.
To expand its digital and AI digital workforce, the government needs to:• Organize technologists within government through a talent management systemdesigned to house highly skilled specialists.
• Recruit people that already have the skills the government needs, such as industry
experts, academics, and recent college graduates.
• Build its own workforce by training and educating current and future government
employees.
• Employ its digital workforce more effectively to ensure digital talent can perform
meaningful work once they are in government.
Organize
Recommendation

Recommendation: Create Digital Corps for Cabinet-Level Departments and SelectAgencies to Organize the Government’s Technical Workforce
How a digital workforce is organized is as important as the workforce’s level of expertise.
We propose creating Digital Corps for Cabinet-level departments and select agencies thatwould recruit, train, and educate personnel; place personnel in and remove personnel fromdigital workforce billets; manage digital careers; and set standards for digital workforce
qualifications. Agencies would create billets for members of the Digital Corps, and provide
guidance to members of the Digital Corps about the work they perform.
Existing Models: The Army’s Medical Corps. Full scaling of specialized talent will only happen
if hired personnel have freedom to solve technical challenges. Many existing strategies
for personnel management are inadequate due to a shortage of people in government
agencies who can properly manage such specialized talent. A notable counterexample tothis, which serves as an inspiration to our Digital Corps model, is the U.S. Army’s Medical

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Corps. The Medical Corps organizes experts with specialized health care skills that do not
fit into the Army’s traditional talent management framework.1 Nurses and doctors receive
education and training as civilians, but their skill sets are crucial to the Army’s health caresystem. So, the Medical Corps talent management framework was created to house these
medical professionals in a way that maximizes their ability to practice medicine within
the Army. Like the Medical Corps, the Digital Corps should have specialized personnel
policies, guidelines for promotion, training resources, and certifications for personnel todemonstrate proficiency in new digital areas.
Notably, a Digital Corps would not be comparable to either the Marine Corps or a Space
Service, as it would not have a service secretary or a distinct theater or domain, and its
members would work for existing services or agencies.
Roles Within the Digital Corps. Career fields are distinct from core competencies—
skills that every Digital Corps member should possess prior to hiring—such as modern
stack software development, deployment, and data-informed decision-making. Training
resources for each career field should be made available to Digital Corps members acrossevery agency. Departments and agencies must also be cognizant that digital talent israrely interchangeable across different skill sets; for example, database architecture,machine learning, and user experience design all fall into different career fields with nearzero overlap. Digital Corps members should be allowed to focus on any one of the following
additional career fields:• Software development• Product management• Data science• Security• Artificial intelligence• Data governance and use• DevOps and site reliability engineering• Emerging technologies2• Human-centered product design
Digital Corps technologists should be able to continue to promote without leaving theirfocus area and move upward into management. Many private tech companies distinguishbetween their engineering and engineering management tracks, so that skilled engineers
are not incentivized to become managers solely for the sake of career advancement. The
Army’s Medical Corps follows a similar model. Once promoted, officers highly competentin their medical specialty can either continue as clinicians or become administrators andmanagers within the Medical Service Corps.
Staffing and Digital Corps Billets. Cabinet-level departments and select agencies should
develop their own Digital Corps rather than relying on a single, government-wide Digital
Corps. For Corps members, this approach creates well-defined tracks for career progression
and stronger incentives to stay. This approach also makes it easier for departments andagencies to identify and invest in in-house talent for future technology projects.

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Each Cabinet-level department and select agency should create designated billets to be
filled by qualified members of its Digital Corps based on skills and experience. In addition,
each should maintain a central talent repository with Corps members’ portfolios of priordigital projects completed with the agency. Departments and agencies can then search
this repository to find the most suitable Corps member to fill each billet. Taking inspirationfrom software development companies, one method of reliably measuring skill proficiency
is to conduct digital interviews consisting of case questions and whiteboarding exercises.
We recommend that billets be filled based on candidates’ performance in these interviews,
chosen career field, and prior project experience (possibly while filling other billets within
the same agency at an earlier date).
Actions for Departments and Select Agencies:• Allocate resources toward the creation of Digital Corps modeled after the Army’s
Medical Corps.
• Develop Digital Corps training resources in the forms of licensed instructional
videos, tutorials, and coursework for each of the nine career fields listed.
• Create agency-specific talent repositories where Corps members can list project
portfolios, source code (where permitted), and career field training badges.
• Create billets and fill them through interviews, evaluation of Corps members’
career field training, and other relevant experiences.
• Develop parallel management-oriented and technical-oriented tracks for each
Corps member’s career progression, with set standards for promotion per agency.
Recruit
The government needs to improve its ability to attract scarce AI talent from the private
sector, academia, and recent college graduates. Doing so requires making paths toservice as easy as possible for as many technologists as possible.
Many AI and other digital practitioners are interested in working with the governmentand can and would do so as either full-time employees or part-time employees. Of thosedesiring full-time employment, some seek an entire career as a government civilian orin the military. Others, while willing to work with the U.S. Government full-time, are lesswilling to make long-term commitments or to dedicate as much of their time, and instead
desire to become short-term employees, fellows, talent exchange participants, or militaryreservists. A third group is willing to work with or for the government part-time, but are
unwilling to become full-time civilian employees and have no desire to serve as part of themilitary. To improve recruiting, the government needs to improve the hiring process andbuild mechanisms for part-time civilian service.

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Recommendation: Create a National Reserve Digital Corps

Recommendation

The government would benefit from access to a larger portion of the country’s total digital
workforce. Many government digital projects suffer from lack of access to digital expertise.
The U.S. Government should establish a civilian National Reserve Digital Corps (NRDC)
modeled after the military reserves’ service commitments and incentive structure. Membersof the NRDC would become civilian special government employees (SGEs),3 and workat least 38 days each year as short-term advisors, instructors, or developers across thegovernment.4 Longer-term positions would be established on an individual basis. While
short-term volunteers are not a substitute for full-time employees, they can help improve
AI education for both technologists and non-technical leaders, perform data triage andacquisition, help guide projects and frame technical solutions, build bridges between the
public and private sector, and other important tasks.5 Several AI practitioners within the
United States Government have said during interviews with the NSCAI that their projects
would benefit from the kind of reserve corps we propose here.
National Reser ve
Digital C orps.

General Structure. We recommend establishing and managing the NRDC as a set ofnodes that fall under the supervision of the Office of Management and Budget (OMB).
Each node would be aligned with a full-time government employee leader selected by
OMB rather than geography, digital applications, or government agency. In effect, OMB
would select node leaders, who would then be responsible for recruiting and organizing
their team. In addition to selecting node leaders, OMB would establish standards, ensure
nodes meet government client requirements, provide funding and administrative support,
maintain security clearances, establish access to an agile development environment andtools, and facilitate technical exchange meetings, when appropriate, to ensure stovepipes
are not created.

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Recruitment. Each node would be responsible for recruiting and screening its digital
experts. Notably, OMB would not be responsible for establishing qualification standards
for members of the NRDC. While volunteers would need to be able to pass a backgroundcheck and would not be employees of a foreign government (though they might be foreign
nationals), node leaders would be empowered to screen and select volunteers, and to
recruit experts from within NRDC for specific tasks. OMB would provide administrative
support, much like a human resources team in a private sector company.6
Project Selection. Projects would be selected in three ways:• Selection by a node after contact with a government client• OMB would direct a node to take on a project• Node leadership would approve individual projects driven by a perceived need that is
not tied to a request from a government client
Government clients would directly contact node leaders or OMB. Nodes would be
responsible for establishing relationships with government agencies and selecting projects,
but OMB would be responsible for ensuring that agencies’ requests are received and that
nodes contribute to NRDC’s mission and vision. Individual projects that are not driven by agovernment client’s request would be pursued at the node leadership’s discretion.
Relationship with Government Agencies. Members of the NRDC would work with agencies
on a project-to-project basis, such as consulting for a specific project or teaching a specificcourse. They would not have a commitment to work with the same agency consistently.
Government agencies would be responsible for paying for their projects, including the costfor reservist time.
Relationship with Civilian Employers. Members of the NRDC and their civilian employers
would be bound by the same rules as the military reserve under the Uniformed Services
Employment and Reemployment Rights Act (USERRA).7 Members would be responsible for
identifying conflicts of interest and removing themselves as appropriate. Employers would
not be able to discriminate against members of NRDC, fire them, or delay promotions as aconsequence of spending time serving in NRDC.8 Implementation could take the form ofa legislative recommendation to modify USERRA or a proposal modeled after USERRA.
Incentivizing Reservist Participation. Civilian reservists in this program would benefit inseveral ways. They would gain an opportunity to contribute to their country, do exciting,meaningful work, and attain awareness of work and advances in a community that differsfrom their own. They may also benefit from the following incentives:• The government should create an NRDC scholarship program modeled after
the Reserve Officer Training Corps. Universities would select students through acompetitive process to receive full tuition and study specific disciplines related todigital technology. In return for accepting the scholarship, graduates would spend partof their summers during school in government internships. Between their freshmanp361

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and sophomore years, students would spend six weeks becoming familiar with a rangeof U.S. Government departments and agencies. Between their sophomore and junioryears, students would spend six weeks as an intern at a specific government agency oroffice. Between their junior and senior years, students would spend another six weeks
interning at a specific agency or office. Upon graduation, scholarship recipients would
spend five years serving in the NRDC, beginning as a GS-7 and advancing to a GS-11
over the course of five years. Students would also begin the security clearance process
at least two years before graduating.9• The NRDC should include a training and continuing education fund for all members.
The NRDC would pay up to $50,000 to each reservist to attend training andeducational opportunities related to AI or to pay for student loans. Educational
opportunities would include conferences, seminars, degree and certificate granting
programs, and other opportunities. An incentive explicitly tied to continuing education
would increase the perceived and actual competency of AI reservists. It would
also attract those with an active interest in continuing education, especially newpractitioners seeking to establish themselves.
How NRDC Would Work: An Example. The following is a hypothetical example of how the
NRDC would function. In this example, OMB would begin creating a node by selecting
a leader that would be trusted to establish and manage a team of reservists. OMB
selects “Jennifer,” a full-time government employee working within the NRDC divisionof OMB, to lead a new NRDC node. Jennifer decides to organize her node functionallyrather than regionally. Using existing government tools and her professional contacts,
she recruits people from across the country, most of whom have backgrounds in healthcare data management or recent graduates with degrees related to the field. She also
recruits from within the NRDC by posting open positions on online job boards. During therecruitment process, OMB provides financial support for recruitment efforts, travel money,
and processes new reservist administrative paperwork, including security clearance
applications.
After the node is established and the team is in place, a government agency––in thisexample, the Centers for Disease Control and Prevention (CDC)––realizes it has two digitalneeds it cannot meet internally: improving a database and training their workforce in new
data management practices at the National Center for Chronic Disease Prevention and
Health Promotion. After reaching out to OMB, they determine that Jennifer’s node is thebest fit, and request assistance. After examining the request and her team’s workload,
Jennifer determines that she would support the CDC’s database improvement request
with a four-person team and support workforce training with a two-person team. The fourperson team spends 14 days examining the existing database and making updates tothe database. The two-person team spends 10 days on site at the National Center for
Chronic Disease Prevention and Health Promotion speaking with leaders and employees
about their data management needs and the current state of the workforce’s skill level,
developing curriculum, and teaching data management best practices.

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The teams Jennifer selects to support the CDC include Michael. Michael received afour-year scholarship from NRDC to study computer science as an undergraduate. After
graduating three years ago, he began working full-time as a data analyst at a health carecompany and working part-time on NRDC projects he coordinates with his node leader.
He also used his education stipend to pay for an online course from MIT last year. Thishypothetical shows that an NRDC can effectively increase the U.S. digital talent, connect
private-sector workers with a government agency, and create a pathway for that connection
to solve an actual problem.
Actions for Congress:• Pass legislation establishing the NRDC within OMBo Grant OMB direct-hire authorities to hire node leaders and reservists.
o The NRDC should offer full tuition scholarships to students studying specificdisciplines related to national security digital technology for up to four years in
exchange for five years of service as a member of the NRDC. This could be doneby including service in the NRDC as an option for people with degrees in digitalfields to pay off service obligations incurred as a result of education received in the
Defense Civilian Training Corps.10
o Legislation should authorize up to $50,000 in educational benefits for courses,
seminars, conferences, and other educational opportunities that are approved
by OMB. It should also ensure that members of the NRDC receive the same
employment protections as military reservists under USERRA. This can be doneby amending USERRA to cover “service in the uniformed services or the National
Reserve Digital Corps.”o Congress should make a two-year appropriation of $16 million to pay for initialadministrative, scholarship, and education benefits.
• Evaluate NRDC Successo Use three metrics to evaluate NRDC’s success: 1) The number of technologists
who participate annually; 2) Evaluations of results from government clients; and 3)
Evaluations of results from reservists. OMB should establish the central, organizing
function for the NRDC within six months of the passage of legislation, and establish
five nodes and a mechanism for distributing educational benefits within ninemonths of the passage of legislation.
Actions for OMB:
• Immediately upon receiving authority from Congress, establish a National Reserve
Digital Corps with systems and processes designed to:o Select and hire node leaderso Encourage potential government clients to contact NRDC nodes, or OMB, withpotential problems to resolveo Ensure government client needs are met by NRDC nodes
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o Provide administrative support (including for security clearances)o Establish node access to development environments and toolso Facilitate technical exchange meetingso Match recipients of NRDC scholarships with node leaders• At the outset, establish five NRDC nodes. Each node leader should be responsible
for:o Recruiting and hiring reservists
o Ensuring the quality of their worko Partnering with government agencies
Recommendation: Create Digital Talent Recruiting Offices Aligned with Digital Corps

Recommendation

Executive branch agencies should create agency-level digital talent offices of up to 20
personnel responsible for recruiting both early career and experienced professionals.
Recruiting offices would monitor their agencies’ need for specific types of digital talent. The
offices would be empowered to recruit technologists virtually, by attending conferences,
career fairs, recruiting on college campuses, and offering scholarships, recruiting bonuses,
referral bonuses, non-traditional recruiting techniques such as prize competitions, andother recruiting mechanisms. A recruiting office would assume responsibility for theiragency’s digital talent recruitment efforts, e.g., Science, Mathematics and Research
for Transformation (SMART) Scholarship-for-Service, and partner with agency humanresources offices to use direct-hire authorities and the Intergovernmental Personnel Act(IPA) to accelerate hiring. This would help scale digital talent recruitment by creating acentral, empowered organization that focuses on a specific mission; concentrates expertiseand funds; would help experts move in and out of government positions throughout theircareer; and can develop relationships with universities and private-sector companies.
Actions for Congress:• Amend Section 230 of the FY2020 NDAA. (Armed Services Committees)o The DoD should be required to appoint a civilian official responsible for digitalengineering talent recruitment policies and their implementation.
o The civilian official should be supported by a digital talent recruiting office with the
Office of the Under Secretary for Personnel and Readiness, as described above.
• Require the Office of the Director of National Intelligence (ODNI) to create a digitaltalent recruiting office. (Intelligence Committees)o The office should work with the IC to identify their agencies’ needs for specific typesof digital talent.
o Recruit technologists by attending conferences, career fairs, and actively recruitingon college campuses.

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o Integrate federal scholarship for service programs into agency recruiting; offer
recruitment and referral bonuses.
o Partner with their agencies’ human resource teams to use direct-hire authorities toaccelerate hiring.
• Require the Department of Homeland Security (DHS) to create a digital talent
recruiting office. (Senate Homeland Security and Governmental Affairs Committee
and the House Committee on Homeland Security)o The office should work with DHS to identify their agencies’ needs for specific typesof digital talent.
o Recruit technologists by attending conferences, career fairs, and actively recruitingon college campuses.
o Integrate federal scholarship for service programs into agency recruiting; offer
recruitment and referral bonuses.
o Partner with their agencies’ human resource teams to use direct-hire authorities toaccelerate hiring.
• Require the Department of Energy (DoE) to create a digital talent recruiting office.
(Senate Committee on Energy and Natural Resources and the House Committee on
Energy and Commerce)o The office should work with DoE to identify their agencies’ needs for specific typesof digital talent.
o Recruit technologists by attending conferences, career fairs, and actively recruitingon college campuses.
o Integrate federal scholarship for service programs into agency recruiting; offer
recruitment and referral bonuses.
o Partner with their agencies’ human resource teams to use direct-hire authorities toaccelerate hiring.
Actions for DoD, including U.S. military services, DOE, DHS, and the ODNI:• Create digital talent recruiting offices.
o Offices should work with their agencies to identify their need for specific types ofdigital talent.
o Recruit technologists by attending conferences, career fairs, and actively recruitingon college campuses.
o Integrate federal scholarship for service programs into agency recruiting; offer
recruitment and referral bonuses.
o Partner with their agencies’ human resource teams to use direct-hire authorities toaccelerate hiring.

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Recommendation: Grant exemption from OPM General Schedule Qualification Policies forSpecific Billets and Position Descriptions

Recommendation

AI practitioners applying for positions within the federal government and their hiring
agencies are constrained by OPM minimum qualification standards. While these standards
are important, and have increased fairness in hiring, they also prevent expert technologiststhat do not have master’s degrees—and in some cases, bachelor’s degrees or comparable
work experience—from joining the government at a reasonable level of compensation. For
example, a 19-year-old software developer or AI practitioner might have a proven trackrecord on cybersecurity or in AI competitions, but can only enter the government as aGS-7. To reduce this hiring challenge, the government should allow agencies to exempt
certain billets from OPM general schedule qualification policies, and instead allow local
hiring managers to make an independent decision about both hiring and pay grade basedon evaluations, prior work, alternative certification programs, or practical experience.
Actions for Congress:• Direct the Office of Personnel Management to amend 5 C.F.R. § 338.301, on service
appointments.
o Allow service secretaries and cabinet officials to create exceptions from the
Qualification Standards for General Schedule Positions by individual billet or
position description.
Actions for OPM and Military Services:• OPM should create and execute a process by which federal departments andagencies can apply for billets or position descriptions to be exempt from generalschedule qualification policies.
• Two-star-and-above commands and their civilian equivalents should declareindividual billets and position descriptions exempt from OPM qualification
standards without approval from OPM or any more senior authority.
Recommendation: Expand the CyberCorps: Scholarship for Service

Recommendation

The CyberCorps: Scholarship for Service (SFS) is a recruiting program designed to
attract students studying IT, cybersecurity, and related fields into the USG. Expanding it
could bring in more people with AI-related skills. It is managed by the National Science
Foundation in partnership with the Office of Personnel Management and the Departmentof Homeland Security. Students enrolled in the program receive a scholarship in exchange
for an obligation to work in an approved government agency for a period of time equal
to the time covered by the scholarship. Seventy undergraduate and graduate institutions
participate in SFS by selecting students for the program, and since 2001, 3,600 students
have received scholarships, 94% of whom went on to serve in government.11 Hiring typicallytakes place during annual online and in-person career fairs.12

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It should be noted that cyber and AI are different fields. Expanding CyberCorps: SFS to
CyberCorps and AI: SFS would avoid increasing administrative burdens. This should not
be taken as an indication that AI and cyber are synonymous, as the education and skills
for each field differ.
Actions for Congress:• Amend the CyberCorps: SFS, as defined by Section 230 of the National Defense
Authorization Act for Fiscal Year 2020.
o Include digital engineers.
o Pay for up to four years of scholarships.
o Include the opportunity to begin the security clearance process.
• Amend 15 U.S.C. § 7442 subsection (a).
o ... recruit and train the next generation of information technology professionals,
digital engineers, artificial intelligence practitioners, data engineers, data analysts,
data scientists, industrial control system security professionals, security managers,
and cybersecurity course instructors to meet the needs of the cybersecurity mission
for Federal, State, local, tribal, and territorial governments.
• Amend 15 U.S.C. § 7442 subsection (b).
o Provide an opportunity for scholarship recipients to initiate their security clearance
process at least one year before their planned graduation date.
• Amend 15 U.S.C. § 7442 subsection (c).
o Allow the scholarship to last for 4 years.
Actions for the National Science Foundation and Office of Personnel Management:• Broaden the CyberCorps: SFS.
o Pay for up to four years.
o Include fields falling under digital engineering, as those fields are defined by
the National Defense Authorization Act for Fiscal Year 2020 (Pub. L. 116-92,
section 230): the discipline and set of skills involved in the creation, processing,transmission, integration, and storage of digital data, including data science,machine learning, software engineering, software product management, andartificial intelligence product management.
Recommendation

Recommendation: Establish a STEM Corps
A bipartisan group of members of the House Armed Services Committee have proposed
H.R. 6526, STEM Corps Act of 2020. The proposal would authorize the appropriation of $5
million per fiscal year, with $500,000 for administrative costs and an advisory board. The
program provides a maximum scholarship of $40,000 per student per year. Scholarship
recipients would serve in different capacities within the DoD for a minimum of three years,p367

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with an option to either remain in the DoD or transfer to a private-sector company that hascontributed to STEM Corps funding. The proposal requires participants to be paid at a
rate not less than GS-6 for the first three years of their obligation and at not less than as a
GS-10 during their fourth year. This proposal has the potential to significantly increase thenumber of personnel with STEM backgrounds in the DoD civilian workforce for a relatively
low cost if a sufficient number of private-sector companies contribute. The potential forrecipients to transfer to the private sector after three years of government service may
create retention issues, but it may also serve as a mechanism to create bridges between
the DoD and private sector companies.
Actions for Congress:• Establish a STEM Corps in the FY2022 NDAA.
• Set aside $5 million for a STEM Corps for FY2022 and each fiscal year thereafter.
Actions for the DoD:
• With congressional authorization and appropriation, establish an office to manageand establish a STEM Corps as described above.
• Include a scholarship program, advisory board, private-sector partnershipprogram, and STEM Corps member management program.
Build
The government will not be able to come out of its workforce deficit through recruitingalone. AI and digital talent is simply too scarce in the United States. In 2020, there weremore than 430,000 open computer science jobs in the United States, while only 71,000
new computer scientists graduate from American universities each year.13 To overcome thechallenges presented by AI and digital talent scarcity, the government should deliberately
focus on building its AI and digital workforce.
Recommendation: Create a United States Digital Service Academy

Recommendation

The United States needs a new academy to train future public servants in digital skills.
Civil servants play a critical and often underappreciated role in government. They holdmuch of the government’s niche, long-term expertise. This is especially true for thedigital expertise that is badly needed for the government to modernize. Methods like thecompetitive service and scholarship for service programs have helped recruit talent, but
as the government’s needs changed, those approaches will no longer address the fullscope of the government’s needs. Bolder measures are necessary to produce the broad,diverse, and technically educated workforce the government needs.
Our proposed United States Digital Service Academy (USDSA) would be an accredited,
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within the federal government, and have the mission to help meet the government’s needsfor digital expertise. It would be advised by an interagency board that would be assisted
by a federal advisory committee composed of commercial and academic leaders inemerging technology.
Existing Models: The Military Service Academies. The USDSA should be modeled off ofthe five U.S. military service academies but should produce trained government civilians
not only to the military departments, but also to civilian departments and agencies beyond
DoD.15
The five military service academies each produce commissioned officers for the armed
forces.16 The academies select cadets and midshipmen through a congressional andpresidential nomination process, followed by a competitive admissions process. The cadetsand midshipmen, who are government employees, exchange a commitment to serve after
graduation for a tuition-free education. Many choose this path for the opportunity to serve;
the free tuition and education often are considered a bonus. Those who depart prior tomeeting the minimum requirements for graduation still incur either a service commitment
or financial requirement to pay back education received upon their departure from theschools.
The academies contribute between 15% and 20% of the new junior officers to their respective
services each year––the largest single commissioning source. Academy graduates also
play an outsized role in the military services’ senior leadership.17 As a result, the academies
help shape the identity and culture of their services, including their standards and ethicalnorms. USDSA would be comparable to the other service academies in many ways.
It would be a degree-granting institution focused on producing leaders for the UnitedStates Government. USDSA students, like military service academy students, would not
pay for tuition, or room and board, and would have a post-graduation service obligation.
Americans should expect USDSA graduates to seek to serve, to lead the nation’s digital
workforce, and to ensure the United States sets an example of intelligent, responsible, andethical high-tech leadership.
Key Differences Between USDSA and the Military Service Academies. The USDSA would
differ in significant ways. First and foremost, USDSA students would enter the institution to
become civil servants. They would know that their education would be repaid in the formof a five-year obligation to serve in government, which would begin upon graduation when
they become a civil servant at a GS-7 level. Exclusively producing civil servants would
eliminate the need for students to complete commissioning requirements, simplifying
the school’s curriculum and administrative burdens, and reduce the need for expansive
campus real estate for training and parade grounds. It would also make USDSA lessredundant, as the military service academies already produce hundreds of computerscientists, electrical engineers, and mathematicians every year.

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USDSA students would also have a more STEM-focused education. While the corecurriculum would ensure broad exposure to different fields, students would have ahighly technical education. A wide variety of technical majors could include AI, software
engineering, electrical science and engineering, computer science, molecular biology,
computational biology, biological engineering, cybersecurity, data science, mathematics,
physics, human-computer interaction, robotics, and design. Students could also blend
those majors with humanities and social science disciplines such as political science,
economics, ethics and philosophy, or history.
A third difference would be that USDSA graduates would serve across the Federal
government. To avoid both perceived and real parochial bias from the organizations
that administer service academies, USDSA would be administered as an independent
Federal entity. The minimum and maximum number of graduates who would serve in eachdepartment or agency would be determined annually by an interagency board.18
Mission Statement of the USDSA. We propose the following: “The United States Digital
Service Academy’s mission is to develop, educate, train, and inspire digital technology
leaders and innovators and imbue them with the highest ideals of duty, honor, and service
to the United States of America in order to prepare them to lead in service to our nation.”
The Student Experience. During their first year, students would begin the Academy’s corecurriculum, explore some electives to help determine their major, and take a summer
internship or fellowship. The core curriculum is envisioned to include, among other things,
American history, government, and law, as well as composition, mathematics, computerscience, and the physical and biological sciences. Once summer arrives, students would
participate in summer internships with private sector companies.
Students would select their major early in their second year, begin concentrating on theirtechnical field, and continue their core curriculum. They would also initiate their security
clearance application process. The goal would be for all students to graduate with at leasta secret clearance. After completing the classroom portion of their second year, students
would complete internships in two government agencies, which would help them focus
their goals for government service.
During their third year, USDSA students would increase the focus on their major, complete
the majority of their core curriculum, and begin committing to a government agency. Similar
to the military service academies, attendance of the first day of class at the start of their
third year serves as a commitment to five years of government service upon graduation.
After completing the classroom portion of the third year, students would participate inanother private sector internship.
Students would commit to a particular government agency and career field during
the first weeks of their fourth year and begin the job placement process. To select ap370

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department and career field, students would create a rank ordered list of career fields
within departments, agencies, and services. The USDSA would then match student
preferences to the government’s needs as identified by an annual interagency process.
After successfully completing all academic requirements, students would graduate as GS7s, with the potential to progress rapidly to GS-11. After completing their service obligation,
USDSA graduates would have the opportunity to transition to the NRDC.
Accreditation. In order to receive federal funding, the USDSA would take the required
steps to complete the accreditation process through a regional accreditation organization.
The accreditation organization would be determined based on the physical location of
the institution and recognized by the Department of Education and Council for Higher
Education Accreditation.19 Membership in such an organization ensures academic quality
throughout the institution’s life span, as accreditation requires ongoing assessment for
improvement. Future employers are able to affirm the credentials of USDSA graduates, theacademy is able to accept charitable donations, and post-graduate programs recognize
the validity of undergraduate degrees through accreditation. Based on the location of
USDSA, the institution would also work with the hosting state to determine compliancewith all core standards and processes.20
Proposed Blueprint for Action for the USDSA:
Phase One (Years 1-2 )• Identify and secure an appropriate site for initial USDSA buildout with room for future
expansion.
• Identify gaps in the government’s current and envisioned digital workforce by aninteragency task force under Office of Personnel Management leadership.
• Establish the USDSA administration as a new Executive branch agency with anindividual appropriation that will be responsible for the phased Blueprint for Action planand the management of the institution.
• Recruit tenure-track faculty.
• Recruit adjunct faculty, primarily from private-sector technology companies.21• Grant the USDSA the authority to accept outside funds and gifts from individuals andcorporations for startup, maintenance, and infrastructure costs.
• Appropriate $40 million to pay for administrative costs.
• Satisfy the necessary requirements set by the Department of Education as well as thestate USDSA is in for degree-granting approval.
• Apply for degree-program-specific accreditation through Computing Accreditation
Commission on Colleges of Accreditation Board for Engineering and Technology.22• Apply for accreditation with a Regional Accrediting Organization approved by the
Department of Education and Council for Higher Education Accreditation in order to be
granted “Candidate” status.
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T E C H N I C A L TA L E N T I N G O V E R N M E N T• Construct initial physical infrastructure.
• Appropriate additional costs for the selection and purchase of the physical location andconstruction of infrastructure.
Phase Two (Years 3-5)
• Begin classes with an initial class of 500 students at the beginning of year three.23• Demonstrate compliance with all requirements and standards of the regional
accrediting organization in order to be granted Membership status.
Phase Three (Years 6-7)
• Graduate the first class.
• Ongoing improvement through accreditation assessments.
• Assess, and as appropriate, expand class sizes.
Actions for Congress:• Authorize the establishment of the USDSA.
o An independent entity with a mandate to establish the institution described above.
o Appropriate $40 million over two years to pay for the USDSA’s initial administrative
costs.
Actions for the Office of Personnel Management:• Begin an interagency process to identify skill and personnel gaps in the federal
government’s digital workforce.
EmployDigitally talented people should be able to reasonably expect to spend a career
performing meaningful work focused on their field of expertise in government. Without
such an expectation, they are unlikely to join the government workforce, and without theirexperience matching expectations, they are unlikely to stay for long.
Recommendation: Establish Career Fields for Government Civilians in Software
Development, Software Engineering, Data Science, Knowledge Management, and Artificial
Intelligence

Recommendation

Government civilians play a critical role in the national security enterprise. A significant
portion of the government’s AI talent is likely to exist in the civilian workforce. Government
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allow them to focus on software development, data science, or AI for the majority of theircareer. This results in a highly limited ability to recruit talent from outside of government,
an inability for an individual to focus on a skill set for an extended time, a lack of continuing
education opportunities for these government civilians, and retention issues. It also
causes the government to struggle to identify and manage the software development,
data science, and AI talent within its workforce.24 Digitally focused occupational series will
better allow the government to track and manage its digital workforce, to attract new talentthat wants to focus on a technical skill set, and to create new positions.
The government should create software development, software engineering, data science,
knowledge management, and AI occupational series. This combination of occupational
series would significantly improve the government’s ability to recruit and manage expertsthat will supervise the collection and curation of data, build human-machine interfaces,
and help end users generate and act on data-informed insights. Many successful privatesector organizations use a version of this combination of skills.25 The government should
follow their example.
Actions for Congress:• Require OPM to draft software development, software engineering, data
science, knowledge management, and artificial intelligence occupational seriesclassification policies no later than 270 days after the passage of the legislation.
Actions for OPM:
• Create software development, software engineering, data science, knowledge
management, and artificial intelligence occupational series.
• Accelerate the creation of new digital occupational series.
o Rather than waiting for agencies to provide a formal request for a new occupational
series, ask agencies to provide supporting documents and subject matter experts tostudy and draft a classification policy for each occupational series.
Recommendation

Recommendation: Establish Digital Career Fields for Military Personnel
Digital subject matter experts’ inability to spend a career working on digital topics whileserving in the military is arguably the single most important issue impeding militarymodernization.26 Much like their civilian counterparts, U.S. military personnel do not havecareer paths that allow them to focus on software development, data science, or AI for the
majority of their career.27 The military has established career fields for doctors and lawyersthat allow them to focus on a technical field, develop their skill over time, and advance within
their service. The military is choosing not to do the same for many types of digital talent.
While some of the services train some operational research and systems analysis (ORSA)
personnel to perform machine learning and AI tasks, these personnel may be shifted towork on other ORSA tasks rather than AI. Phrased differently, AI practitioners have somebackground in ORSA, but not all ORSA personnel are trained to work in machine learning
or AI.28

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This results in a reduced ability to recruit talent outside of the government, an inability tofocus on a skill set for an extended time, a lack of continuing education opportunities, andretention issues. It also causes the government to struggle to identify and manage thesoftware development, data science, and AI talent within its workforce.29 These problems
are particularly acute for military personnel, who are required to regularly change positions
and move into manager roles or face eventual discharge from the military. The lack ofdigital career fields also causes the military services to struggle to identify and manage
the software development, data science, and AI talent within their workforces.30 As longas this state continues, the military should not expect to achieve better results for its digitalmodernization than its legal and medical fields would have without career fields for lawyers
and doctors.
The military services should have primary career fields that allow military personnel tofocus on software development, data science, or artificial intelligence for their entire career,either as managers or technical specialists.
Actions for Congress:• Require the military service chiefs to create career fields focused on softwaredevelopment, data science, and artificial intelligence.
o Congress should amend section 230 of the FY2020 NDAA to require the military
service chiefs to create career fields focused on software development, career fields
focused on data science, and career fields focused on artificial intelligence for bothcommissioned officers and enlisted personnel, and, as appropriate, warrant officers.
o Military personnel should be able to join these career fields either upon entry into
the military, or by transferring into the field after serving a period in another career
field. These career fields should have options that allow personnel to either follow
a path to senior leadership positions, or specialize and focus on technical skill sets.
Those that specialize and focus on technical skill sets should not have to leave theirfocus area and move into management positions to continue to promote. Legislation
should not restrict the military services to only two career fields, but rather require
each service to create at least two career fields, and more at their discretion. The
military services should be required to create the career fields within one year of
passage of legislation.
Actions for the Military Services:• Create career fields that allow military personnel to focus on softwaredevelopment, career fields that allow military personnel to focus on data science,and career fields that allow military personnel to focus on artificial intelligence.
o While remaining consistent with service personnel policies and procedures, thesecareer fields should be open to both enlisted personnel and commissioned officers,
and, as appropriate, warrant officers.
o Military personnel should be able to join these career fields either upon entry into
the military, or by transferring into the field after serving a period in another career
field.
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o These career fields should have options that allow personnel to either follow a path
to senior leadership positions, or specialize and focus on technical skill sets. Thosethat specialize and focus on technical skill sets should not have to leave their focus
area and move into management positions to continue to promote.
Recommendation

Recommendation: Provide Government Technologists with World-Class Tools, Data Sets,
and Infrastructure.
Highly skilled technologists working in government are regularly denied access tosoftware engineering tools. They have to jump bureaucratic hurdles to accomplish basic
job functions such as sharing source code or downloading data sets, leading to frustrationand periods of idling. To perform meaningful work in government, employees within thedigital workforce need access to enterprise-level software capabilities at par with thosefound in the private sector. Capabilities include software engineering tools, access tosoftware libraries, open-source support, and infrastructure for large-scale collaboration.
Employees within the AI career field in particular will need access to further specialized
resources such as curated data sets and compute power.
In order to be effective, developers need to be able to find and view source code written byother developers before them. Being unaware of existing code repositories often leads towriting redundant software that meets a different set of quality standards and robustness
than existing software. To prevent this, each member of the AI career field needs access toa shared, enterprise-level repository of AI software and tools, similar to that recommended
in Chapter 2 of this report for the Department of Defense. This repository should housesource code available to all AI developers within a government agency.
Each government agency should create enterprise-scale solutions for source code
management across multiple software projects. This does not mean that every developer
in an agency will be able to modify every single project in a repository—with protocols
for delegated access, a system administrator can set project-specific read and write
permissions for each AI developer. New software projects should be set up to allow
ubiquitous unit testing as code is written, and automatic integration into a code review
process to ensure robust and bug-free output. Following these guidelines will promote a
culture of software engineering excellence, emphasizing to technologists that it is possible
to work in government while remaining at the forefront of a digital field.
For new developers who join an agency, onboarding procedures must include separate
instructions for pushing their new code to this repository as well as instructions on how tonavigate the software catalog and search for existing source code.
All career fields also need unobstructed access to the latest open-source libraries and tools.
Over time, technologists develop individual preferences for their software development
environment, opting for custom software development kits (SDKs), debugging tools, cloud
tools, version control software, and data visualization platforms on local machines. To

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ensure productivity and developer satisfaction, agencies must give each developer theauthority to install vetted, authorized tools on their local machines.
AI developers use open-source software libraries for training machine learning models
and making them production-ready for real-world use. To harness the full power of theseessential libraries, AI developers should have access to vetted libraries, but also tocompute power while training their machine learning models. Models train very slowly on
a local machine because of the complexity of underlying mathematical calculations in the
training process. As a result, AI developers prefer to train them rapidly through automatic
deployment pipelines on commercially available platforms, or another external service.
Smoothing the transition from local software development to cloud services is critical forany organization using AI and ML.31
Actions for Departments and Agencies (including, but not limited to, the Department ofEnergy, Department of Homeland Security, Department of State, Department of Commerce,
and Department of Justice):32• Ensure software developers and engineers, data scientists, and AI practitioners:o Have access to systems with capabilities comparable to Repo One and PlatformOne.
o Are authorized to install custom software licenses, debugging tools, cloud
deployment tools, version control software, and data visualization platforms on their
computers.
o Have agency-specific resources for cloud-based compute power that AI developers
can harness to train machine learning models with greater speed.

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Blueprint for Action: Chapter 6 - Endnotes
Jim Perkins, et al., Don’t Just Copy and Paste: A Better Model for Managing Military Technologists,
War on the Rocks (Aug. 24, 2020), https://warontherocks.com/2020/08/dont-just-copy-and-paste-abetter-model-for-managing-military-technologists/.

1

These fields were selected from a combination of NSCAI’s Third Quarter recommendations andPartnership for Public Service’s Tech Talent for 21st Century Government. See Interim Report andThird Quarter Recommendations, NSCAI (October 2020), https://www.nscai.gov/previous-reports/;
Tech Talent for 21st Century Government, Partnership for Public Service: Tech Talent Project (April2020), https://ourpublicservice.org/wp-content/uploads/2020/04/ Tech-Talent-for-21st-CenturyGovernment.pdf.

2

A special government employee is “an officer or employee of the executive or legislative branch of
the United States Government . . . who is retained, designated, appointed, or employed to perform,
with or without compensation, for not to exceed one hundred and thirty days during any period of
three hundred and sixty-five consecutive days.” 18 U.S.C. § 202.

3

Members of the military reserves typically serve two to three days a month, and one 14-day
obligation a year, averaging around 38 days a year.

4

Organizations that employ full-time technical experts in temporary positions, such as the UnitedStates Digital Service or Defense Digital Service, already exist, and have proven successful. The
NRDC is an alternative for experts that cannot or do not want to pursue a full-time route.

5

Some administrative functions, such as background checks, security clearance processing,processing tax paperwork, and others, would place an unnecessary burden on local nodes andshould be addressed by a central body such as OMB.

6

Uniformed Services Employment and Reemployment Rights Act of 1994, U.S. Department of Justice(Aug. 6, 2015), https://www.justice.gov/crt-military/userra-statute.
7

Frank Whitney, Employment Rights of the National Guard & Reserve, U.S. Department of Justice(last accessed Jan. 1, 2021), https://www.justice.gov/sites/default/files/usao-ednc/legacy/2011/04/29/
EmploymentRights.pdf.

8

All reservists would apply for security clearances, but this should not imply that reservists would
work primarily on classified materials. A large part of the work needed to modernize the government is
unclassified.

9

10
The Defense Civil Training Corps was created by the National Defense Authorization Act for Fiscal
Year 2020. See Pub. Law 116-92, sec. 860, National Defense Authorization Act for Fiscal Year 2020,
116th Congress (2019).
11

Engagement with government officials on Aug. 22, 2019, Feb. 7, 2020, and March 9, 2020.

12
CyberCorps: Scholarship for Service, U.S. Office of Personnel Management (last accessed Jan. 1,2021), https://www.sfs.opm.gov/.
13
Code.org (last accessed Jan. 11, 2021), https://code.org/promote. See also Oren Etzioni, What
Trump’s Executive Order on AI Is Missing: America Needs a Special Visa Program Aimed at AttractingMore AI Experts and Specialists, Wired (Feb. 13, 2019), https://www.wired.com/story/what-trumpsexecutive-order-on-ai-is-missing/.
14
The USDSA should also have the authority to accept gifts, particularly to help fund its
establishment.

The Council on Foreign Relations report, Innovation and National Security: Keeping Our Edge,
recommends creating a digital military service academy. James Manyika & William McRaven,
Innovation and National Security: Keeping Our Edge, Council on Foreign Relations (September 2019),
https://www.cfr.org/report/keeping-our-edge/. Our recommendation is for a civilian digital service
academy that would not produce any uniformed military personnel.
15

The five academies include the United States Military Academy, the United States Naval Academy,
the United States Coast Guard Academy, the United States Merchant Marine Academy, and theUnited States Air Force Academy.

16

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17Joseph Moreno & Robert Scales, The Military Academies Strike Back, The Chronicle of Higher
Education (Nov. 12, 2012), https://www.chronicle.com/article/the-military-academies-strike-back/. As
an example, 5 Secretaries of the Navy, 29 Chiefs of Naval Operations, and nine Commandants of the
Marine Corps graduated from the United States Naval Academy.
18
Each military service academy has a maximum and minimum number of positions available forevery available career field, causing some graduates to receive career fields other than their first
choice. Similarly, USDSA graduating classes would have a minimum and maximum number of civilian
graduates that join each military department or government agency.
19
The military service academies are accredited by different regional accreditation organizations
recognized by the U.S. Secretary of Education and Council for Higher Education. Their engineering
programs are generally accredited by the Accreditation Board for Engineering and Technology, Inc.
20

State approval and accreditation are not the same, but both are required.

21
Recruitment will rely on private-sector champions to recruit high-profile adjunct faculty that canserve as beacons that will attract additional faculty and high-quality students.
22
The Computing Accreditation Commission on Colleges of Accreditation Board for Engineeringand Technology is a nonprofit, ISO 9001 certified organization that accredits college and university
programs in applied and natural science, computing, engineering, and engineering technology.

For comparison, since 2001, C:SFS has had 3,600 graduates, or about 189 graduates per year,
according to program officials NSCAI spoke with on March 9, 2020.

23

This analysis is based on the NSCAI staff conducting more than 100 interviews with government
officials between May 2019 and May 2020. This feedback has emerged as a common theme in nearly
all of NSCAI’s workforce discussions. See e.g., NSCAI interviews with government officials (June 7,2019); NSCAI interviews with government officials (May 17, 2019).

24

25
NSCAI staff interview with a private-sector company (Sept. 9, 2019); NSCAI staff interview with a
private-sector company (Sept. 19, 2019); NSCAI staff interview with a private-sector company (April
24, 2020).
26

NSCAI staff interviews with government and private-sector senior leaders (May 6, 2020).

Workforce Now: Responding to the Digital Readiness Crisis in Today’s Military, Defense Innovation
Board at 1-7 (2019), https://media.defense.gov/2019/Oct/31/2002204196/-1/-1/0/ WORKFORCE_NOW.
PDF.

27

NSCAI staff has interviewed several ORSA personnel performing AI-related tasks. All agreed
when asked that a separate career field for artificial intelligence or data science is needed. Existing
initiatives make some progress, but do not adequately address the lack of career fields for digitaltalent.

28

The NSCAI staff conducted more than 100 interviews with government officials between May 2019and May 2020. This feedback has emerged as a common theme in nearly all of NSCAI’s workforce
discussions. See e.g., NSCAI interviews with government officials (June 7, 2019); NSCAI interviews
with government officials (May 17, 2019).

29

NSCAI’s First Quarter Recommendations included an addition to the Armed Services Vocational
Aptitude Battery to test for computational thinking that would help identify aptitude and a test forcoding language proficiency that would help identify skill. First Quarter Recommendations, NSCAI at
33-35 (March 2020), https://www.nscai.gov/previous-reports/. Both tests will be helpful, but will notmeet their full utility without digital career fields. In conversations with NSCAI, numerous government
officials continuously identified a lack of digital career fields as a key impediment to talentmanagement. See e.g., NSCAI interviews with government officials (June 7, 2019); NSCAI interviews
with government officials (May 17, 2019).

30

2020 Interim Report and Third Quarter Recommendations, NSCAI at 37-38 (October 2020), https://www.nscai.gov/previous-reports/.

31

32

See Chapter 2 of this report for a detailed description of how DoD would implement this plan.

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Chapter 7:
Establishing Justified
Confidence in AI Systems
Blueprint for Action

A Holistic Framework for Ensuring Justified Confidence in AI Systems.
The U.S. Government should align on a common understanding of critical steps
needed to ensure justified confidence in AI systems, including confidence in their
responsible development and use. The Commission has outlined such a strategy in the
Key Considerations. The Key Considerations provide a framework for the responsible
development and fielding of AI that should be adopted by all agencies critical to national
security. The framework includes near-term recommendations and topics that agencies
should give priority consideration, practices that should be implemented immediately, andpolicies that should be defined or updated to reflect new AI considerations.
Based on robust feedback from agencies including Department of Defense (DoD),Intelligence Community (IC), Department of Homeland Security (DHS), Federal Bureau
of Investigation (FBI), Department of Energy (DoE), Department of State (DoS), andDepartment of Health and Human Services (HHS), as well as the GSA AI Community ofPractice, the Key Considerations also outlines areas needing future work and targeted
investment to overcome current challenges. Agencies that have already adopted AI
principles noted broad alignment between the Key Considerations framework and their AI
principles. For instance, the framework’s recommended practices help operationalize the
AI Principles of the DoD and IC1 and the Principles for Use of AI in Government.2
The implementation of the Key Considerations’ recommendations for future action will
be important not only for agencies, but also for cooperation across the world on the
responsible development and fielding of AI.3 Further, while the Commission’s mandate led
to a focus on recommendations specific to national security entities in our report, many
recommendations we elevate in the Key Considerations are relevant to the whole country,
including other sectors and industry.
Heads of departments and agencies critical to national security should implement the
Key Considerations as a framework for the responsible development and fielding of AI
systems. Agencies, at a minimum, include the DoD, IC, FBI, DHS, DoE, DoS, and HHS.

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Implementing the Key Considerations includes developing policies and processes toadopt the framework’s recommended practices, monitoring their implementation, andcontinually refining them as best practices evolve. While this framework covers dozens ofpractices that contribute toward an ideal state of responsible development and fielding,some practices will be more critical than others depending on the stakes and context, andcomplying with them will require different costs and resources. This Blueprint for Action
provides details on the key actions from this framework that all departments and agencies
critical to national security can and should take now as a priority, and investments andresources that the government should make available to further responsible AI acrossall agencies. These span recommendations for Robust and Reliable AI; Human-AI
Interaction and Teaming; Testing and Evaluation, Verification and Validation; Leadership;
and Accountability and Governance.
Recommendations for Robust and Reliable AI

Recommendation

Action for the Office of Science and Technology Policy (National AI Initiative Office):• Focus federal research and development (R&D) investments on advancing AI
security and robustness, to help agencies better identify and mitigate evolving
AI system vulnerabilities. Confidence in the robustness and reliability of AI systems
requires insight into the development process and the operational performance of thesystem. Insight into the development process is supported by capturing decisions anddevelopment artifacts for review; insight into operational performance is supported
by runtime instrumentation and monitoring to capture details of execution. In bothdevelopment and operation, there is a need to invest in R&D for better tools to facilitate
the capture of needed processes and data. R&D should also advance interpretability
capabilities to better understand if AI systems are operating as intended. And R&D
should support better characterization of performance envelopes to enable the gradualrollout and adoption of AI systems. “Robust AI” is included among the priority researchareas found in Chapter 11 of this report.
Action for all Departments and Agencies• Create an AI Assurance Framework. All government agencies will need to develop
and apply an adversarial machine learning threat framework to address how key AI
systems could be attacked and should be defended. An analytical framework can help
to categorize threats to government AI systems, and assist analysts with detecting,responding to, and remediating threats and vulnerabilities.4 This framework must
address supply chain threats to data and models as well as adversarial AI attacks.5 The
framework will support assurance of data authenticity and data and model integrity.
“Create an AI Assurance framework” is included among recommendations found in
Chapter 1 of this report.

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Action for DoD and the Office of the Director of National Intelligence (ODNI):• Create dedicated red teams for adversarial testing. Such red teams should assume
an offensive posture, dedicated to trying to break systems and make them violate rules
for appropriate behavior.6 Because of the scarcity of required expertise and experience
for AI red teams, the DoD and ODNI should consider establishing enterprise-widecommunities of AI red teaming and vulnerability testing capabilities that could
be applied to multiple AI developments. The Commission supports the aligned
recommendation by WestExec Advisors that the DoD and ODNI should consider“standing up a national AI and ML red team as a central hub to test against adversarial
attacks, pulling together DoD operators and analysts, AI researchers, T&E, CIA, DIA,
NSA, and other IC components, as appropriate. This would be an independent redteaming organization that would have both the technical and intelligence expertise to
mimic realistic adversary attacks in a simulated operational environment.”7
Actions for Agencies Critical to National Security:8• To Meet Baseline Criteria for Robust and Reliable AI –o Upgrade development, procurement, and acquisition strategies to ensure that thoseaccountable for the development, procurement, or acquisition of an AI system (e.g.,
program managers) adopt the following practices:
■

■

■

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381

Consult an interdisciplinary group of experts to conduct hazard analysis
and risk assessments. These should cover, as relevant to the context:
potential disparate impact related to unwanted bias; privacy and civil liberties;
international humanitarian law; human rights;9 system security against
targeted attacks;10 risks of technology being leaked, stolen, or weaponizedby adversaries against the U.S.;11 and steps taken to mitigate identified risks.
Agencies should specify in their respective strategies who will consult sucha group and who will ultimately make final decisions based on the group’s
advice.
Improve documentation practices. Produce documentation describing thedata used for training and testing; model(s); other relevant systems (including
connections and dependencies within systems); required maintenance(for datasets and models) technical refresh, and when the system is used
in a different operational environment. For data, documentation should
include how data were sampled, and their provenance. For synthetic data,
documentation should also include details on how the data were generated.12
Build overall system architectures to limit the consequences of system
failure. Agencies should build an overall system architecture that monitors
component performance and handles errors when anomalies are detected;
build AI components to be self-protecting (validating input data) and selfchecking (validating data passed to the rest of the system); and include
aggressive stress testing. As with all high-consequence software systems,
where technically feasible, it is important that high-consequence AI systems
have overall system architectures that support robust recovery and repair or
fail-fast and fail-over to a reliable degraded mode safe system. There should
be clear mechanisms for disengaging and deactivating the system when things
go wrong.13

E S TA B L I S H I N G J U S T I F I E D C O N F I D E N C E I N A I S Y S T E M S

Recommendations for Human-AI Interaction and Teaming

Recommendation

Action for Department of Defense:• Invest in a sustained, multi-disciplinary initiative to enhance human-AI teaming
through the Service Laboratories and DARPA.
o This initiative should focus on maximizing the benefits of human-AI interaction;better measuring human performance and capabilities when working with AI
systems; and helping AI systems better understand contextual nuances of asituation. Advances in human-machine teaming will enable human interactions
with AI-enabled systems to move from the current model of interaction where thehuman is the “operator” of the machine, to a future in which humans are able to havea “teammate” relationship with machines. Specific funding should be dedicatedto research on how to improve human-machine teaming and interaction when it
involves human life-safety or lethal deployment of a system. Additional researchis urgently needed which should address the following issues, among others:delegation of authority, observability, predictability, directability, communication,and trust.
o R&D investment should also focus on the following:■■■

Developing improved human performance assessment, an essential element
for AI to understand when and how an appropriate AI intervention should be
made.
Developing new approaches to humans and AI establishing and maintaining
common ground in support of collaboration, particularly cognitive
collaboration. This encompasses how a newly established human-AI teamscaffolds its mutual understanding and then how it extends it to creatively andcollaboratively tackle new challenges.
Developing new approaches to trust calibration in human-AI teams. This
includes helping people understand when AI is approaching or outside
the bounds of its competency envelope, and likewise helping machines
understand when people are approaching their limits. The two together
will help the human-AI team calibrate trust appropriately and shape theirinteraction for improved team performance.14 “Enhanced human-AI interaction
and teaming” is included among the priority research areas found in Chapter 11of this report. This recommendation also maps to the overall DoD R&D funding
recommendation in Chapter 3 of this report.

Actions for Agencies Critical to National Security:• Meet Baseline Criteria for Effective Human-AI Interaction and Teaming –o National security departments and agencies should clarify policies on human roles
and functions, develop designs that optimize human-machine interaction, andprovide ongoing and organization-wide AI training.
■

Develop design methodologies that improve our understanding of humanAI interaction and provide specific guidance and requirements that can beassessed.15 These methodologies should clearly delineate requirements ofpotential human-AI teaming alternatives and identify whether a proposedsolution is likely to meet those requirements or not.
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BLUEPRINT FOR ACTION: CHAPTER 7• Designs should mitigate automation bias (that places unjustified
confidence in the results of computation) and unjustified reliance onhumans as a failsafe mechanism. They should provide accurate cues to thehuman operator about the level of confidence the system has in its results/
behaviors.
■

Ensure policies provide ethical bounds regarding when and where AI isappropriate within a human-AI team in a given context.
• Policies should identify what functions humans should perform across theAI life cycle; bound assignments and functions, including autonomous
functionality; define when tasks should be handed off between a humanand machine based on bounds; and require feedback loops to informoversight and ensure systems operate as expected.

■

Provide ongoing training to help the workforce better interact, collaborate
with, and be supported by AI systems—including understanding AI tools.16
As relevant, employees across departments and agencies, and the DoD in
particular, should, at a minimum:• Gain familiarity with AI tools (e.g., through everyday interaction), including
use of AI systems in realistic situations and provide continual feedback tointegrate improvements.17• Receive education that includes fundamentals of AI and data science,
including coverage of key descriptors of performance and probabilities.18• Receive training on interpreting performance standards and metrics
correctly and making informed decisions based on them.19• Gain an understanding of both the fundamental concepts and the highlevel concepts in terms of how the system components interact with eachother.20• Have training to recognize human cognitive biases so that humanoperators interacting with machines can recognize where they might be
succumbing to such bias.21• Receive ongoing refresher trainings suited to system operators. Refresher
trainings are appropriate when systems are deployed in new settings
and unfamiliar scenarios, and when predictive models are revised withadditional training data as system performance may shift, introducing
behaviors that are unfamiliar to operators.22

Recommendation

Recommendations for Testing and Evaluation, Verification and Validation
Action for the Department of Defense:• DoD should tailor and develop TEVV policies and capabilities to meet the changes
needed for AI as AI-enabled systems grow in number, scope, and complexity in the
Department.23
This should address the following elements:p383

E S TA B L I S H I N G J U S T I F I E D C O N F I D E N C E I N A I S Y S T E M S• Establish a testing and evaluation, verification and validation (TEVV) frameworkand culture that integrates testing as a continuous part of requirements
specification, development, deployment, training, and maintenance and includes
run-time monitoring of operational behavior.24 An AI testing framework should:
o Establish a process for writing testable and verifiable AI requirement specifications
that characterize realistic operational performance.25
o Provide testing methodologies and metrics that enable evaluation of theserequirements—including principles of ethical and responsible AI, trustworthiness,robustness, and adversarial resilience.26
o Define requirements for performance reevaluation related to new usage scenariosand environments, and distribution over time.
o Encourage incorporation of operational usage workflow and requirements from thedefined use case into the testing.
o Issue data quality standards to appropriately select the composition of training andtesting sets.
o Support the use of common modular cognitive architectures within suitableapplication domains that expose standard interface points for test harnessing—supporting scalability through increased automation along with federated
development and testing.
o Support a cyclical DevSecOps-based approach, starting on the inside and working
outward, with AI components, system integration, human-machine interfaces, andoperations (including human-AI and multi-AI interactions).
o Remain flexible enough to support diverse missions with changing requirements
over time.
• Extend existing and develop new TEVV methods and tools for dealing withcomplex, stochastic, and non-stationary systems, including the design ofexperiments, real-time monitoring of states and behaviors, and the analysisof results. These methods/tools need to account for human-system interactions(HSI) and their impact on system behavior, system-system interactions and theireffect on emergent behavior across a group of systems, and adversarial attacks,
via both conventional cyberattacks, and nascent perceptual adversarial AI attacks.
Risk assurance concepts should be extended beyond simple “stop-light” charts ofconsequence and likelihood for a risk being realized and leverage tools that support
developing assurance cases that present verifiable claims about system behavior andprovide reviewable arguments and evidence to support the claims.27• Make TEVV tools and capabilities readily available across the DoD, including
downloadable and configurable AI TEVV software stacks.28 In addition, the DoD should
ensure tools that support TEVV and reliability and robustness goals are available
department-wide including tools for bias detection, explainability, and documentation
across the product life cycle (e.g., of data inputs and system outputs).
• Update existing and create new live, virtual, and constructive test ranges for
AI-enabled systems (blending modeling and simulation, augmented reality, andcyber physical system environments). Upgraded test ranges should include livevirtual-constructive environments, the ability to capture data from testing, and theability to evaluate data from operations. They should support: 1) The full explorationof potential system states and behaviors over a range of runtimes and fidelity levels;p384

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2) the co-development of AI-system functionality and concepts of operations(CONOPS) associated with human-system and system-system teaming; and 3) a fuller
understanding of the impact of adversarial activities undertaken to counter thesesystems. Build these capabilities upon extensive modeling and simulation (M&S)
facilities, human and constructive adversarial “red teams,” virtual and augmented reality
enablers, full instrumentation, and post-run big data analytics capability.
• Support the T&E community by restructuring the processes that underlie
requirements specification, system design, T&E itself, and CONOPS development.
This includes continuing DoD investments and policies supporting architecting
software-intensive systems using common frameworks and composable subsystems,29
the inclusion of runtime instrumentation (adding the capture of internal states of thesystem, analogous to a flight data recorder on aircraft) in system design and monitoring
during operation,30 the proper curation and protection of data used in training thesesystems, and a heavy investment in successively sophisticated M&S, starting at therequirements stage and proceeding through development, TEVV, and operator training.
Action for the National Institutes of Standards and Technology (NIST):• NIST should provide and regularly refresh a set of standards, performance metrics,
and tools for qualified confidence in AI models, data, and training environments,
and predicted outcomes.31 Over time, as the science of how to test systems acrossresponsible AI attributes evolves, NIST should provide guidance on:o Metrics to assess system performance per responsible AI attributes (e.g., fairness,interpretability, reliability, robustness) and according to application/context profiles.
This should include:■■

Definitions, taxonomy, and metrics needed to enable agencies to better assess
AI performance and vulnerabilities.
Metrics and benchmarks to assess reliability of model explanations.32

o For each of the metrics and technical measures created, NIST should also provide
measurable outcomes against which success can be determined.33• In the near term, NIST should also provide guidance on:o Standards for testing intentional and unintentional failure modeso Exemplar data sets for benchmarking and evaluation, including robustness testing
and red teamingo Defining characteristics of AI data quality and training environment fidelity (tosupport adequate performance and governance)
In conducting the above, NIST should publish quarterly updates to inform departments
and agencies about the trustworthy frameworks, standards, and metrics work it isplanning.34

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Action for the Office of Science and Technology Policy - National AI Initiative Office:• The federal government should increase R&D investment to improve ourunderstanding of how to conduct TEVV. This is needed to better understand how
to efficiently and effectively test AI systems to provide objective assurance to support
a justified level of confidence, build checks and balances in systems, and how tomonitor and mitigate unexpected behavior in a composed system-of-systems or whensystems interact. Such R&D should advance our understanding of how to test system
performance across responsible AI attributes (e.g., fairness, interpretability, reliability,
and robustness). This recommendation is echoed by the priority research areas foundin Chapter 11 of this report, including “TEVV of AI Systems” and “standard methods andmetrics for evaluating degrees of auditability, traceability, interpretability, explainability,
and reliability.” For more information, see also Chapter 3 of this report.
Actions for Agencies Critical to National Security:• To ensure optimal performance of AI systems, national security departments andagencies should:
o Plan for and execute aggressive stress testing of AI components to evaluate error
handling and robustness against unintentional and intentional threats underconditions of intended use.
o Include testing for blind spots and fairness throughout development anddeployment. Testing and validation should be done iteratively at strategicintervention points, especially for new deployments.
o Clearly document system performance requirements (including identified system
hazards), metrics used for TEVV, deliberations on the appropriate fairness metrics
to use, and the representativeness of the test data for the anticipated operational
environment.
o Conduct red teaming to rigorously challenge AI systems, exploring their risks,
limitations, and vulnerabilities including intentional and unintentional failuremodes.
Recommendations for Leadership

Recommendation

Actions for DoD, IC, FBI, DHS, DoE, DoS, and HHS:
• Every department and agency critical to national security and each branch of thearmed services, at a minimum, should have a dedicated, full-time Responsible AI
Lead who is part of the senior leadership team. Responsible AI Leads must havededicated staff, resources, and authority to succeed in their roles. Every leadshould have at least two full-time staff to effectively fulfill the following:o The Responsible AI Lead in each department should oversee the implementationof the Key Considerations recommended practices alongside the department/
agency’s respective AI principles.35 This includes driving policy development andtraining programs for the department and internally coordinating Responsible AI
leads in the department’s supporting branches or agencies (as applicable) to ensure
synergistic implementation of such policies and programs. The department leadshould determine the Responsible AI governance structure to ensure centralized
and consistent policies36 are applied across the department.
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o The department Responsible AI Lead and those supporting Responsible AI leads
should collectively:
■

■

■

■

■

■

provide Responsible AI training to relevant personnel;
serve as subject matter experts regarding existing and proposed Responsible
AI policy and best practices;
shape procurement policy and guidance for product managers to ensurealignment with recommended practices and adopted AI principles;build a central repository of Responsible AI work going on in the department,
and lessons learned from practical implementation across the department, tohelp streamline department efforts;
ensure interagency knowledge sharing for responsible AI, including iterative
sharing of best practices, resources and tools, evolving risks and vulnerabilities,
and other lessons learned from practical implementation;
annually produce a report for Congress on department resources received,
any additional resources needed, and an update on required policy work andimplementation of recommended practices.

o Where possible, centralized assessments and shared learnings should becommunicated across a department’s elements or branches, to avoid units spending
unnecessary and duplicative resources and to accelerate practices that reduce
friction in workflows. Responsible AI Leads in each department should consider
the Learning, Knowledge, and Information Exchange (LKIE) framework as a way toaccelerate organizational knowledge within their department given the need toleverage collective insights that are gleaned from on-the-ground experience where
the Key Considerations will be put into practice rather than letting the insights sit
in silos.37 Furthermore, having Responsible AI “champions”38 who “socialize” thisknowledge can help to transfer the knowledge within and across different U.S.
Government agencies and components.39
o Borrowing from the world of cybersecurity, the Lead also should consider
coordinating the adoption of an empirically driven prioritization matrix for risk
management.40
Action for the National AI Initiative Office:• In addition to the National AI Initiative responsibilities defined in the National
Defense Authorization Act for Fiscal Year 2021 (FY2021 NDAA),41 the Office should
create a standing body of multi-disciplinary experts who can be voluntarily called
upon by agencies as a resource to provide advice on Responsible AI issues. The
group should include people with expertise at the intersection of AI and other fields
such as ethics, law, policy, economics, cognitive science, and technology including
adversarial AI techniques. As the government upskills and diversifies its workforce with
AI expertise, this standing body of experts should help fill gaps in multi-disciplinary
expertise that can be called upon by agencies as needed for processes including multidisciplinary risk assessment, human-AI teaming assessments, and red-teaming.
• Leveraging this in-house expertise, and serving as the central resource for best practice
sharing across agencies, it should also:p387

E S TA B L I S H I N G J U S T I F I E D C O N F I D E N C E I N A I S Y S T E M So Maintain a Learning, Knowledge, and Information Exchange (LKIE) repository tobenefit all agencies:■

A repository compiling insights across agencies (e.g., per the LKIE framework
mentioned above) would accelerate organizational knowledge and support
the goal of interagency sharing of insights gleaned from on-the-groundpractice—rather than letting such insights sit in silos.42 These collective
insights would be generalized from bright spots of successful AI adoption andfrom lessons learned from AI adoptions that faced problems in development
or use.43 Centralized insights will also provide a resource to help agencies
address critical questions that will arise as AI capabilities evolve. Examplesof potential critical questions include how to support redress with updated
policies and procedures; how to efficiently monitor behavior in operation; andhow to effectively measure and address changes introduced by technicalrefresh. With technical refresh, it is necessary to analyze results carefully. Evenif overall performance may be steady or improve after a refresh, the aggregate
performance can mask certain parts of the performance envelope where
results are significantly skewed and problematic.

Action for Congress:• To enable departments and agencies critical to national security to execute
Responsible AI work department-wide, and to encourage necessary appointmentsof Responsible AI personnel, Congress should appropriate an estimated $21.5
million each fiscal year to fund billets.
o Organizations that have high mission complexity and diverse components mayneed more support staff and/or Responsible AI Leads to be allocated across theorganization. The Commission recommends that, at a minimum, the following isneeded:■■■■For the DoD, a department-wide Responsible AI (RAI) Lead and supporting RAI
Leads for each branch of the armed services, with each lead supported by two
staff members;
For the Intelligence Community, an ODNI RAI Lead and supporting RAI Leads
for each IC agency, with each lead supported by two staff members;
For the DOE, a RAI Lead and a supporting RAI Lead for the National
Laboratories, with each lead supported by two staff members; andFor the FBI, DHS, and HHS, a RAI Lead in each respective organization who is
supported by two staff members.44

Recommendations for Accountability and Governance

Recommendation

Actions for Agencies Critical to National Security:• Adapt and extend existing policies to ensure accountability is established anddocumented across the AI life cycle for any given AI system and its components.45• Establish clear requirements about information that should be captured about
the development process46 (via traceability) and about system performance andp
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behavior in operation (via runtime monitoring) to support reliability and robustness aswell as auditing for oversight. Instrumentation to support monitoring can contribute toinsights about system performance, but must be provided thoughtfully to prevent new
openings for external espionage or tampering with AI systems.47
o Guidance should include technical audit trail requirements per mission needs forhigh-stakes systems.
• Institute comprehensive oversight and enforcement practices.
o Agencies should identify or establish new policies, due to the novelty andadvancement of AI technologies, that:■■allow individuals to raise concerns about irresponsible AI development (e.g.,through an ombudsman); andprovide layers of human oversight or redundancy so that high-stakes decisions
do not rely entirely on determinations made by the AI system.48

o Adapt and extend oversight practices to include reporting requirements49 forAI systems; a mechanism to allow for thorough review of the most sensitive andhigh-risk AI systems (to ensure auditability and compliance with deployment
requirements); an appealable process for those found at fault of developing or using
AI irresponsibly; and grievance processes for those affected by the actions of AI
systems.50
o Establish selection criteria that indicate if and when specific recommended
practices (as found in the Key Considerations) need to be used according to system
and mission risks.
o Define triggers that would require escalated review of an AI system.

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Blueprint for Action: Chapter 7 - Endnotes
See Key Considerations for Responsible Development & Fielding of Artificial Intelligence Supporting
Visuals, NSCAI (July 2020), https://www.nscai.gov/wp-content/uploads/2021/01/Key-ConsiderationsSupporting-Visuals.pdf.

1

See Donald J. Trump, Executive Order on Promoting the Use of Trustworthy Artificial Intelligencein the Federal Government, The White House (Dec. 3, 2020), https://trumpwhitehouse.archives.
gov/presidential-actions/executive-order-promoting-use-trustworthy-artificial-intelligence-federalgovernment/. The Principles for Use of AI in Government do not apply to national security agencies;
however, they do apply to agencies the Commission considers critical for national security (e.g.,
Department of State and Department of Health and Human Services).

2

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations for
Responsible Development & Fielding of AI. For additional details on international cooperation, see
the Commission’s recommendation for future action in the sections on “Aligning Systems and Uses
with American Values and the Rule of Law” and “System Performance” in Key Considerations for
Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on filewith the Commission).

3

There are various public and private efforts ongoing. See for instance the MITRE-Microsoft
adversarial ML framework, Ram Shankar Siva Kumar & Ann Johnson, Cyberattacks Against Machine
Learning Systems Are More Common than You Think, Microsoft Security (Oct. 22, 2020), https://www.
microsoft.com/security/blog/2020/10/22/cyberattacks-against-machine-learning-systems-are-morecommon-than-you-think/; Adversarial AI Threat Matrix: Case Studies, MITRE (last accessed Jan. 10,2021), https://github.com/mitre/advmlthreatmatrix/blob/master/pages/case-studies-page.md.

4

NISTIR 8269 (Draft): A Taxonomy and Terminology of Adversarial Machine Learning, National
Institute of Standards of Technology (October 2019), https://csrc.nist.gov/publications/detail/nistir/8269/draft.

5

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for red teaming, see the section on “Engineering Practices” in Key Considerations
for Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (onfile with the Commission).

6

See Michele Flournoy, et al., Building Trust Through Testing (October 2020), https://cset.georgetown.
edu/wp-content/uploads/Building-Trust-Through-Testing.pdf.

7

As noted above, the Commission considers these, at a minimum, to include the DoD, IC, DHS, FBI,
DoE, Department of State, and HHS.

8

For more on the importance of human rights impact assessments of AI systems, see Report of
the Special Rapporteur to the General Assembly on AI and its impact on freedom of opinion andexpression, UN Human Rights Office of the High Commissioner (2018), https://www.ohchr.org/EN/
Issues/FreedomOpinion/Pages/ReportGA73.aspx. For an example of a human rights risk assessment
for AI in categories such as nondiscrimination and equality, political participation, privacy, andfreedom of expression, see Mark Latonero, Governing Artificial Intelligence: Upholding Human
Rights & Dignity, Data Society (October 2018), https://datasociety.net/wp-content/uploads/2018/10/
DataSociety_Governing_ Artificial_Intelligence_Upholding_Human_Rights.pdf.

910These can include reidentification attacks. Departments and agencies should use privacy
protections such as robust anonymization that can withstand sophisticated reidentification attacks,
and when possible, privacy-preserving technology such as differential privacy, federated learning,and ML with encryption of data and models.

For exemplary risk assessment questions that IARPA has used, see Richard Danzig, Technology
Roulette: Managing Loss of Control as Many Militaries Pursue Technological Superiority, Center for a
New American Security at 22 (June 28, 2018), https://s3.amazonaws.com/files.cnas.org/documents/
CNASReport-Technology-Roulette-DoSproof2v2.pdf?mtime=20180628072101.
11

12
Such documentation should support assurances of the authenticity, integrity, and provenance of
data.

p
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See Making Responsible AI the Norm Rather than the Exception, Montreal AI Ethics Institute at
9 (Jan. 13, 2021), https://arxiv.org/pdf/2101.11832.pdf [hereinafter MAIEI Report] (This includes“building fail safes and backup modes that don’t have to rely on continuous access to the ‘intelligent’
elements and have graceful failures that minimize harm.”).

13

14
See Brian Wilder, et al., Learning to Complement Humans, Proceedings of the TwentyNinth International Joint Conference on Artificial Intelligence (2020), https://www.ijcai.org/
Proceedings/2020/0212.pdf.

For an example of applicable guidelines, see Saleema Amershi, et al., Guidelines for Human-AI
Interaction, CHI ’19: Proceedings of the CHI Conference on Human Factors in Computing Systems(May 2019), https://dl.acm.org/doi/10.1145/3290605.3300233.

15

For more on training, see the Appendix of this report containing the abridged version of NSCAI’s
Key Considerations for Responsible Development & Fielding of AI. For additional details on theCommission’s recommendation for training, see the section on “Human-AI Interaction and Teaming”
in Key Considerations for Responsible Development & Fielding of Artificial Intelligence: Extended
Version, NSCAI (2021) (on file with the Commission).

16

17

Such everyday interaction and continual feedback loops will further enhance TEVV.

18
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for training, see the section on “Human-AI Interaction and Teaming” in Key
Considerations for Responsible Development & Fielding of Artificial Intelligence: Extended Version,
NSCAI (2021) (on file with the Commission). See also MAIEI Report at 7.
19

MAIEI Report at 7.

20

Id.

21

Id.

22
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for training, see the section on “Human-AI Interaction and Teaming” in Key
Considerations for Responsible Development & Fielding of Artificial Intelligence: Extended Version,
NSCAI (2021) (on file with the Commission).

To the greatest extent possible, DoD should develop TEVV policies and capabilities in coordination
with the Office of the Director of National Security.

23

To achieve this, heavy investment is needed that supports requirements generation/traceability,
the integration of heterogeneous test data at all stages of testing, and the use of extensive M&S, test
automation, and data analytics wherever feasible.

24

25This should be framed broadly, providing left/right limits that provide guidance but do not limit
innovation.
26
These testing methodologies and metrics should support robust red teaming, meeting the DoD’s
particular needs for solutions hardened to adversarial actions.

Miles Brundage, et al., Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims, arXiv (April 20, 2020), https://arxiv.org/abs/2004.07213.

27

TEVV tools and software stacks should be shared across the Department using the AI Digital
Ecosystem described in Chapter 2 of this report.

28

Such frameworks for composing testable AI systems should be established and accessed throughthe AI Digital Ecosystem described in Chapter 2 of this report.

29

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Blueprint for Action: Chapter 7 - Endnotes
See e.g., Software Acquisition Pathway Interim Policy and Procedures, Memorandum from theUnder Secretary of Defense, to Joint Chiefs of Staff and Department of Defense Staff (Jan. 3, 2020),
https://www.acq.osd.mil/ae/assets/docs/USA002825-19%20Signed%20Memo%20(Software).
pdf (stating that program managers are required to “achieve ... continuous runtime monitoring ofoperational software”).
30

This recommendation is in line with Congress’ expansion of NIST’s mission regarding AI standards
in the FY2021 NDAA, section 5301 to include: “advance collaborative frameworks, standards,
guidelines” for AI, “support the development of a risk-mitigation framework” for AI systems, and“support the development of technical standards and guidelines” to promote trustworthy AI systems.”
Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021,
134 Stat. 3388 (2021).

31“Documentation of the assumptions and limitations of the benchmarks so created will also beessential in helping those utilizing them to make sure they will get the intended intelligence from it
rather than becoming falsely confident about the system.” MAIEI Report at 9.
32

33

MAIEI Report at 9.

34
Doing so will enable departments and agencies to plan and prioritize any internal standards work
accordingly (e.g., avoiding redundant or obsolete efforts).

For each of the metrics and technical measures mentioned in the Key Considerations, it will be
important to have measurable outcomes against which success can be determined. See MAIEI Report
at 9.

35

36
This includes, for example, “Accountability and Governance” policy work identified below in thisBlueprint for Action.
37

MAIEI Report at 11-16.

“AI champions” are a cross-functional group of ambassadors, who can, for example, consider
ways to operationalize AI ethical principles and serve as internal advocates and evangelists forresponsible AI. See Department of Defense Joint Artificial Intelligence Center Responsible AI
Champions Pilot, DoD (last accessed Feb. 3, 2021), https://www.ai.mil/docs/08_ 21_ 20_responsible_
ai_champions_pilot.pdf; Tim O’Brien, et al., How Global Tech Companies can Champion Ethical AI,
World Economic Forum (Jan. 14, 2020), https://www.weforum.org/agenda/2020/01/tech-companiesethics-responsible-ai-microsoft/.

38

39

MAIEI Report at 12.

40

MAIEI Report at 20-23.

Pub. L. 116-283, Div. E., Title LI, sec. 5102, William M. (Mac) Thornberry National Defense
Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

41

42

MAIEI Report at 11-16.

43
For instance, this could include communication of failure modes (e.g., when a system produces aformally correct, but unsafe outcome), and instances to establish a shared understanding of how andwhere the systems go wrong. Leveraging this, agencies should tap into USG network-wide expertiseto address those failures. See Ram Shankar Siva Kumar, et al., Failure Modes in Machine Learning,
Microsoft (Nov. 11, 2019), https://docs.microsoft.com/en-us/security/engineering/failure-modes-inmachine-learning; MAIEI Report at 7.

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Collectively, considering both Responsible AI Leads and supporting staff, this recommendation
proposes 21 full-time employees (FTEs) for the DoD; 54 for the IC; 3 for the FBI; 3 for DHS; 6 for DoE;
3 for HHS; and 3 for DoS.

44

45
As noted in the Key Considerations, agencies should determine and document who is accountable
for a specific AI system or any given part of an AI system and the processes involved with it. Thisshould identify who is responsible for the development or procurement; operation (including thesystem’s inferences, recommendations, and actions during usage) and maintenance of an AI system;
as well as the authorization of a system and enforcement of policies for use. See the Appendix of thisreport containing the abridged version of NSCAI’s Key Considerations for Responsible Development& Fielding of AI. For additional details on the Commission’s recommendation for accountability, see
the section on “Accountability and Governance” in Key Considerations for Responsible Development& Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).
46
For a list of recommended information that documentation should note about system development,
see the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendations for traceability, see the Key Considerations for Responsible Development & Fieldingof Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).

For example, “APIs are ‘doors’ to access digital infrastructures thus, the security and resilience ofdigital environments will also depend on the robustness of the API infrastructure.” V. Lorenzino, etal., Application Programming Interfaces in Governments: Why, What and How, European Union Joint
Research Centre (2020). https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-researchreports/application-programming-interfaces-governments-why-what-and-how.

47

See Frances Duffy, Ethical Considerations for Use of Commercial AI, John Hopkins Applied Physics
Laboratory at 31 (Dec. 2020). For example, DoD Directive 3000.09 requires human oversight inthe targeting and execution process for lethal autonomous weapons. See DoD Directive 3000.09:
Autonomy in Weapons Systems, U.S. Department of Defense (May 8, 2017), https://www.esd.whs.mil/
portals/54/documents/dd/issuances/dodd/300009p.pdf.

48

For example, reporting risk and impact assessment, steps taken to mitigate such risks, and system
performance during testing and fielding.

49

50
As with all consequential software systems, developers and adopters of consequential AI systems
must adapt and extend existing support for oversight, audit, reporting, and appealable accountability
for developing or using systems irresponsibly, and a redress process where appropriate for thoseaffected by system actions. Existing frameworks must be tailored to reflect issues of concern
with AI-based systems (particularly based on machine learning). These issues of concern are
discussed in more detail in the Appendix of this report containing the abridged version of NSCAI’s
Key Considerations for Responsible Development & Fielding of AI. For additional details on theCommission’s recommendations for accountability and governance, see the Key Considerations for
Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on filewith the Commission)p394

BLUEPRINT FOR ACTION: CHAPTER 8

Chapter 8:Upholding Democratic Values:
Privacy, Civil Liberties, andCivil Rights in Uses of AI for
National Security
Blueprint for Action

The U.S. needs an approach for adopting AI domestically for national security that upholds
and bolsters respect for democratic values, including privacy, civil liberties, and civilrights. Such an approach must strengthen, provide, and show leadership with regard to:1) transparency; 2) approaches for AI system development and testing; 3) the ability tocontest AI decisions; 4) oversight over AI development and use; and 5) legislative andregulatory controls on how AI is used. Our recommendations include immediate actions
that the President, the Congress, and agencies should take; a comprehensive assessment
by a Task Force that leads to reforms for AI governance and oversight; and areas forcontinued work. The recommendations are aimed at assuring that AI systems used bynational security agencies uphold democratic values. Secondarily, the adoption of theserecommendations can earn and inspire public confidence, both domestically and abroad,in uses of AI by national security agencies.
Recommendation

Recommendation Set 1: Increase Public Transparency about AI Use through Improved
Reporting
Actions for Congress:• For AI systems that involve U.S. persons, require AI Risk Assessment Reports
and AI Impact Assessments to assess the privacy, civil liberties and civil rights
implications for each new qualifying AI system or significant system refresh.
o The Commission proposes Congress require elements of the Intelligence
Community (coordinated by the Office of the Director of National Intelligence(ODNI)) as well as the Department of Homeland Security (DHS) and the Federal
Bureau of Investigation (FBI), to prepare and publish an AI Risk Assessment
Report and conduct AI Impact Assessments to assess the privacy, civil liberties,
and civil rights implications of each new qualifying AI system or significant systemrefresh. The Commission recognizes the current requirements for privacy impactp395

U P H O L D I N G D E M O C R AT I C VA L U E S : P R I VA C Y, C I V I L L I B E R T I E S , A N D C I V I L R I G H T S I N U S E S O F A I F O R N AT I O N A L S E C U R I T Y

assessments and civil liberties impact assessments done at agencies as requiredby current statute. AI-related technologies may be reviewed by these, but are
not fully/adequately captured by the current assessments. The Commission’s
recommendation intends to augment these requirements.
o The AI Risk Assessment Report and AI Impact Assessment would be required for“new qualifying AI systems” and for “significant system refreshes.” The Commission
proposes that the Task Force described later in this Blueprint be charged withdetermining the decision procedures for identifying which AI systems andsignificant system refreshes would require AI Risk and Impact Assessment Reports.
o The intent of the AI Risk Assessment Report and AI Impact Assessment is to ensure
potential impacts are considered and mitigated while avoiding an unnecessary
increase in compliance burdens.
■Legislated frameworks for ensuring effective and pragmatic risk mitigation(with the ability to categorize systems per risk and determine the appropriate
mitigations if any) exist in other models that can be used as a template (e.g.
FISMA).

o The AI Risk Assessment Report should include a detailed analysis of system
implications for, and steps to mitigate and track risks (e.g., through metrics) to:
■

■

■

■

■

Freedom of expression (e.g., is the AI-enabled surveillance targeting people
because of their First Amendment protected activity or is the AI-enabledgovernment surveillance causing or may potentially cause a chilling effect?);
Equal protection (e.g., is the AI-enabled surveillance biased toward a protectedclass? What are the likely effects the new technology or program will have on
key demographics such as race, gender, or disability?);
Privacy (e.g., is a warrant required for the government action? Are minimizationand query processes sufficient/satisfactory?);
Redress and due process (e.g., what mechanisms exist, or limitations have been
accepted, for providing redress for adverse government actions taken based on
information generated by the AI system?); andThe assessment should account for the environment in which the AI system
will be deployed, including its interactions with other AI tools and programs
that collect personally identifiable information (PII).

o AI Impact Assessment should be made available periodically, but no less thanannually, to the agency’s Privacy and Civil Liberties ( PCL ) Office to determine thedegree to which a qualifying AI system remains compliant with the constraints
and metrics established in the Risk Assessment. AI Impact Assessments should
be based on outcomes, impacts, and metrics collected during system use, anddetermine if the existing validation processes should be improved.
o Resources and staffing. PCL Offices should assess the resources, including
staff, needed to adequately complete the above. Agency heads should support
additional resourcing for PCL Offices as part of the annual budget process.
o Disclosure notices. Congress should require ODNI, DHS, and the FBI to review
non-public and/or classified AI programs once the program is shut down for
declassification and/or disclosure.

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Action for the President:• Should Congress not require new privacy, civil liberties, and civil rights reporting(as identified above), conduct AI Risk Assessment and AI Impact Assessment
Reports as described above.
Actions for DHS and the FBI:• DHS and the FBI should impose new obligations for System of Record Notices(SORNs) and Privacy Impact Assessments (PIAs) specific to AI systems to ensurethat they provide richer information.
o SORNs and PIAs should provide a holistic picture about the collection, use,and storage of personal information by any AI system, including its connections
to existing systems and accounting for the layering of different surveillancetechnologies where applicable. Agency practices do not sufficiently support the
production of SORNs and PIAs that adequately depict how AI systems collect, use,and store personal information.1
o DHS and the FBI should require that all PIAs include description of the algorithm(s)used and purpose of the algorithm(s); the potential for inferring additional
information about individuals from the aggregation of multiple data sources; andimportantly, the measures that will be used to address these risks.
o DHS and the FBI should require that SORNs provide more specificity in describing
types of data collected, data sources and the connections between data sources,
and who will use such data and why.
• DHS and the FBI should take steps to increase public transparency about the AI
systems they employ.
o DHS has recently started an effort to improve transparency, and those efforts should
be prioritized and assessed as they are implemented.2
o The FBI should implement similar reforms to improve transparency.
• DHS and the FBI should make their websites easier for the public to navigate andensure the websites are regularly updated. Privacy, Civil Liberties, and Civil Rights
Risk and Impact Assessment Reports, related semiannual reports, PIAs, and SORNs
should be located in a central place; have clearly marked dates next to the title, andchronologically ordered, and published in a timely manner. DHS and the FBI shouldseek public comments annually about the navigability of their websites and potential
improvements.
Recommendation

Recommendation Set 2: Develop & Test Systems per Goals of Privacy Preservation and
Fairness
Actions for the President:• Through Executive Order, the President should require the Director of National
Intelligence, the Secretary of Homeland Security, and the Director of the FBI totake the following actions:p397

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o In implementing steps to achieve this objective, the Commission recommends that
ODNI, DHS and the FBI adopt practices from the Key Considerations. In particular:
■

■

■

■

■

Use privacy protections such as robust anonymization that can withstandsophisticated reidentification attacks, and when possible, privacy-preserving
technology such as differential privacy, federated learning, and machinelearning (ML) with encryption of data and models.3
Mitigate bias in development and testing. For development, conduct
stakeholder engagement to establish consensus on the definition of fairness
metrics to be used for the specific development and identify necessary
constraints on system behavior to protect civil rights and avoid inequitableoutcomes.4 In testing, confirm that identified constraints are enforced.5 Testing
to expose unintended bias should include testing for and documentation ofdifferent types of error rates (e.g., differences in false positive or false negative
rates) or disparate outcomes across demographics.6
Use AI-tools to support assessing fairness (e.g., industry tools cited in the Key
Considerations).7
Ensure the MLOps toolchains include routine calibration of agreed-uponfairness metrics throughout continuous development and integration.8
Assess model performance and system impact during fielding on an ongoingbasis, including emergent behavior, to ensure compliance with privacy, civilrights, and civil liberties objectives.9• Designate an office, committee, or team in each agency to conduct a predeployment review of AI technologies that will impact privacy, civil liberties, andcivil rights, including relevant documentation.
o This should include review in advance of their deployment and for complianceover the life span of the system.10 An office in each Intelligence Community
agency, DHS, and the FBI should be equipped to assess data, model, and system
documentation, and testing results of technologies per their intended use.
o In undertaking this review, the Commission recommends the designated office use
the Key Considerations.
Actions for Congress:• Establish third-party testing center(s) to allow independent, third-party testing of
national-security-related AI systems that could impact U.S. persons.
o Congress should fund NIST to create a Third-Party AI Testing Lab program under
the NIST National Voluntary Laboratory Accreditation Program.11

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o The third-party test mechanism’s mandate should be to:
■

■

■

■

Tailor metric assessment per agency mission and authorities;
Develop an approach for testing both software products that can be installed ina test facility and cloud-based services;
Establish binding data dissemination agreements with stakeholders of thesystem to be tested (e.g., the agency requesting testing and relevant vendors
and data owners);
Collaborate with the agency seeking testing to reach consensus on how tohandle the test data provided and the test results and analyses.12

o Third-party test center(s) should allow government vendors to share proprietarydata without fear of it being exposed to competitors; and offer the benefits of anaggregated view of performance across a sector or collection of corporations andaggregated best practices.
o Third-party test center(s) should be used by agencies prior to procuring or fielding
high-consequence systems that impact U.S. persons, and use should be considered
to overcome in-house testing limitations.
• Require the Department of Justice (DOJ), in consultation with the Privacy andCivil Liberties Oversight Board (PCLOB), to develop binding guidance for theuse of third-party testing (e.g., thresholds for high-consequence systems or
unprecedented factors) of AI systems.13
o This should include criteria for when an AI system may pose high enough risk for
privacy, civil liberties, and civil rights that it would trigger a testing requirement by athird party. In forming such guidance, PCLOB and the DOJ should consult with PCL
Officers in federal agencies.
Acknowledgment of continued work for the technical community and legal experts.
There are significant unresolved tensions between various technical approaches topreserving civil rights and civil liberties and current and anticipated legal frameworks.
For example, scholars have expressed concern “that technical and legal approaches tomitigating bias will diverge so much that laws prohibiting algorithmic bias will fail in practice
to weed out biased algorithms and technical methods designed to address algorithmic bias
will be deemed illegally discriminatory.”14 Continued work in the technical, legal, and policy
domains is required to find a consensus balance that addresses technical approaches topreserving privacy, civil liberties, and civil rights and evolving policy.
Recommendation

Recommendation Set 3: Strengthen the ability of those aggrieved by AI to seek redressand have due process.
Actions for FBI and DHS:
• The FBI and DHS should each conduct a review of its respective policies andpractices related to AI technology to ensure that parties aggrieved by governmentp399

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action involving the use of AI, including through system actions or misuse, canseek redress and clearly know how to do so. At least annually, the FBI and DHS
shall assess if updates or changes are required to their respective reviews.
o This review should determine whether notice of AI use in decision-making isadequately provided to aggrieved parties to enable redress, as well as the degree ofauditability and interpretability needed to contest.
o The FBI and/or DHS review team—which must include the Offices of Privacy andCivil Liberties—should submit recommendations to their respective agency heads
for any regulatory and/or policy changes necessary to amend existing redress
mechanisms to reflect issues raised by the use of an AI-enabled system.
o The Attorney General, working with the Director of the FBI, and the Secretary ofHomeland Security, respectively, should direct appropriate actions to ensure thateach agency:■■

provides adequate redress, based on the recommendations of the review; andprovides the public with clear, updated guidance on how to seek redress insituations covered by the review, including by posting relevant information on
their websites.

Actions for the Attorney General:• Issue federal guidance on AI and due process. This guidance should describe how
relevant agencies should safeguard the due process rights of U.S. persons when AI
use may lead to a deprivation of life or liberty. This should include what obligations
agencies have to disclose on its use of AI15 to a criminal defendant in a timely manner
prior to trial or hearing (i.e., notice obligations), including the role that AI played leading
to an arrest, charge, or criminal prosecution. Such guidance should be incorporated into
agency operational guidelines.
Acknowledgment of continued work by the judicial and/or legislative branches:
The above actions should ensure that agencies receive clear guidance on AI-related
redress and due process16 in the interim as Congress and/or the courts weigh in on federal
requirements. Continued work will be needed to provide baseline guidance with theevolution of AI capabilities and their application,17 and to address open questions on thefederal rules of evidence and criminal procedure as they relate to AI.18
Recommendation Set 4: Strengthen Oversight and Governance Mechanisms to Address
Current and Evolving Concerns

Recommendation

Actions for Congress:• Strengthen the Privacy and Civil Liberties Oversight Board’s (PCLOB) ability toprovide meaningful oversight and advice to the federal government’s use of AIenabled technologies for counterterrorism purposes. To achieve this, Congressp
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should provide for a targeted expansion of PCLOB’s authorities and appropriations asfollows:
o Awareness of AI programs. As part of PCLOB’s authority to access all relevantmaterial from agencies, agencies should be required to provide PCLOB notice prior
to the fielding or repurposing of an AI system, as well as any associated privacy, civil
liberties, and civil rights impact assessments.
o Visibility into technology. Agencies should be required to provide to PCLOB, upon
PCLOB’s request, specific information about technology used in any AI system,
including: the data used for AI systems (e.g., documentation regarding the datacollection processes for AI-enabled tools and programs, including disclosure andconsent processes); models used (and supporting model documentation regarding
training and testing); and model repurposing (beyond that context for which it wastrained/approved).
o Resources and other organizational requirements. PCLOB requires an increase
to its resources, both in terms of talent and funding, to achieve its mission andmanage its portfolio as AI adoption increases. PCLOB should provide Congresswith a self-assessment of its resources and organizational structure given the
expected increase of AI-related programs that fall under its current mandate andresponsibilities.
• Empower DHS Offices of Privacy and Civil Rights and Civil Liberties. Congressshould bolster the roles of DHS’ Office of Privacy and Office of Civil Rights and Civil
Liberties by requiring the Chief Civil Rights and Civil Liberties Officer, in coordination
with the Privacy Officer, to play an integral role in the legal and approval processes forthe procurement and use of AI-enabled systems, including associated data of machinelearning systems in DHS. As part of this legislation, the Privacy and Civil Rights andCivil Liberties offices should report back to Congress concerning additional staffing or
funding resources that are required to satisfy this mandate.
Action for the Secretary of Homeland Security:• Ensure the Privacy Officer and the CRCL Officer receive permanent seats inthe new DHS enterprise-wide AI Coordination and Advisory Council. Suchappointments are needed in order to meaningfully satisfy the DHS AI Strategy objective
titled, “Formalize AI Governance Processes at DHS.” 19
Actions for the President:• Through Executive Order, require stronger coordination and alignment among
oversight and audit organizations through creation of an interagency working
group focused on oversight and audit. Voluntary compliance by agencies with AI
documentation and testing requirements should be supported by rigorous, technicallyinformed oversight. To achieve this and overcome current auditing impediments, astanding body (e.g., an interagency working group) should align and coordinate toenhance AI oversight and audit with respect to privacy, civil liberties, and civil rights.
This includes system auditability such that the government can monitor and trace thesteps that produced a system’s output,20 and auditing to ensure systems are not being
misused.

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o Composition: Organizations should include the Department of Justice Intelligence
Oversight Section; Office of the Inspector General of the Intelligence Community;
the Government Accountability Office; the Privacy & Civil Liberties Oversight
Board; Civil Liberties and Privacy Offices of national security agencies; the National
Security Council, and the Office of Science & Technology Policy.
o Function: The interagency working group should provide a forum for members
to substantively and regularly address and share information. The working groupshould:■■■Develop an inventory of the types of AI-relevant oversight and audit currently
performed by and anticipated by the participant organizations.
Develop an inventory of specific capabilities developed in each organization toaddress AI oversight and audit.
Assess available AI-enabled tools that can be adapted to support more
effective and efficient oversight and audit.
• Tools that support financial audit21 and model risk management22 areexamples of advances in applying AI to improve the efficiency andscalability of audits that should be reviewed for adoption.

■

■

■

■

■

Identify priority investment requirements for each organization to address
current needs.
Identify priority research topics for open S&T gaps in supporting AI oversightand audit, including research gaps in applications of AI in support of privacy
and civil liberties (e.g., ML techniques for classification, recommendation,
anomaly detection, and other applications)23 and extending tools such as thosethat support financial audits and model risk management;
Recommend policy or legislative changes for specific authorities granted tothe individual organizations.
Address mission and focus overlap among representative organizations.
Issue reports, at a minimum annually, on key oversight and audit activities aswell as S&T gaps.

Action for the President or Congress:• Establish a task force to assess the privacy and civil rights and civil liberties
implications of AI and emerging technologies.
The goal of the task force would be to identify gaps and make recommendations to ensurethat uses of AI and associated data in U.S. government operations comport with U.S. law
and values, and to study organizational reforms that would support this goal. Specifically,it should assess existing policy and legal gaps for current AI applications and emerging
technologies, and make recommendations for:• legislative and regulatory reforms on the development and fielding of AI and emerging
technologies;24 andp402

BLUEPRINT FOR ACTION: CHAPTER 8• institutional changes to ensure sustained assessment and recurring guidance on privacy
and civil liberties implications of AI applications and emerging technologies.
As mentioned in Chapter 8 of this report, the advancement of AI requires a forwardlooking approach to oversight that anticipates the continued evolution and adoption of
new technologies, and better positions the government to manage their employment
responsibly well into the future. The Commission assesses that, to achieve this goal, a newtask force is needed.
The Commission recommends that the President or Congress create a task force with theproposed membership, structure, function, and priorities identified below.
For expediency, the President should:• Issue an Executive Order that creates a task force charged with recommendingreforms for AI governance and oversight.
o Membership and structure. The President should create a task force in the
Executive Office of the President to develop recommendations on ensuring
adequate AI governance and oversight. The President should designate a senior
official to lead the task force. Members should include the heads of OMB, NIST,PCLOB, and the GAO; and Chief Civil Liberties and Privacy Officers and Inspectors
General of all national security agencies. In addition, the task force should include
representatives from civil society (including organizational leaders with expertisein privacy, civil liberties, and civil rights), industry, and academia. The National AI
Advisory Committee Subcommittee on AI and Law Enforcement should also be
represented.25
o Function. The task force should be charged with the following responsibilities:■■

Conducting a macro assessment of the privacy and civil rights and civil
liberties implications of the capabilities of AI and emerging technologies;
Making recommendations for legislative and regulatory reforms on thedevelopment and fielding of AI and emerging technologies, including
associated data, in the following key areas:• Privacy, Civil Liberties, and Civil Rights (P/CLCR) reporting. Binding
guidance on when the IC, DHS, and FBI should prepare and publish
an AI Risk Assessment Report and AI Impact Assessments, specificallywhat constitutes a qualifying AI system or significant system refresh (asdiscussed in the first recommendation of Chapter 8 of this report).
• Biometric technologies. This should include baseline standards for federal
government use of biometric identification technologies, including but notlimited to, facial recognition.
o To address the urgent need for baseline standards and safeguards
regarding facial recognition, this should include assessing gaps infederal legislation, gathering input from agency stakeholders (andtheir legal counsel) currently using facial recognition for national
security missions; privacy, civil liberties, and civil rights experts insideand outside of government, including PCLOB; and from the public atlarge in order to make facial recognition legislation recommendations.

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o Beyond facial recognition, guidance will be needed regarding otherbiometric identification tools including voiceprints.
• Government procurement of commercial AI products. This should
include contractual best practices for ensuring industry AI products(including associated data) procured by the government uphold privacy,
civil liberties, and civil rights expectations (including privacy, information
security, fairness/non-discrimination, auditability, and accountability). Thisshould include third-party requirements that should be incorporated into
procurement terms that speak to responsible AI objectives, including fortesting validation.26 Consideration should be given to both governmentoff-the-shelf and commercial-off-the-shelf (COTS) procurement.27• Data privacy and retention. Updates to and reforms of government data
privacy and retention requirements to address AI implications.
■

Making recommendations for institutional changes to ensure sustained
assessment and recurring guidance on privacy and civil liberties implicationsof AI applications and emerging technologies.
• Evolving AI capabilities are poised to challenge existing expectations
for privacy, civil liberties, and civil rights.28 In light of this, the task force
should assess the utility of a new entity within the federal government toregulate and provide government-wide oversight of AI use by the federal
government.
• In evaluating options for a new entity, the task force should consider thefollowing:o Authorities and resources necessary for the new entity to provide
ongoing guidance and baseline standards for:■■The federal government’s development, acquisition, and fieldingof AI technologies to ensure they comport with privacy, civil
liberties, and civil rights law and values, and to include guardrails
for their use and disallowed outcomes29 to be incorporated inpolicy and embedded in system development; andTransparency to oversight entities and the public regarding theFederal Government’s use of AI systems and the performance of
those systems.

o Existing interagency and intra-agency efforts to address AI oversight;
ando The unique needs of national security, law enforcement, and othergovernment missions with respect to AI systems and potential
implications for privacy, civil liberties, and civil rights, and civil liberties.
• After considering the potential utility of a new organization, makerecommendations on organizational placement and structure,composition, authorities, and resources needed.
■

Assessing ongoing efforts to adapt regulation of the private sector’s AI
adoption,30 and as appropriate, consider and recommend institutional ororganizational changes to facilitate adequate regulation of commercial
development and fielding of AI and associated data.
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o Reporting. The task force should issue a report to the President with its
legislative and regulatory recommendations on a rolling basis, but no later than
within 180 days of its creation. It should issue a report to the President with its
recommendations for organizational changes within one year of its creation. The
Commission recommends that the report be provided to Congress to ensuretransparency and assist Congress in examining these critical issues.
• In the alternative, Congress should mandate the existence of this task force as
outlined above.
Acknowledgment of continued work to update and clarify legal frameworks on key issues
in data protection and data privacy:
A comprehensive approach to upholding privacy and civil liberties in the AI era requires
tackling several large, unresolved policy and legal questions regarding data protection
and data privacy. Detailed recommendations on these issues would extend beyond thescope of this Commission’s mandate, but we identify them here in order to urge further
study and congressional action.
• Legal concerns over federal use of third-party data. Congress and/or the Judiciary
should assess the adequacy of current legal constraints over the federal government’s
obtainment and use of third-party data, including data acquired from data brokers.
Either through evolving case law or legislation, agencies would benefit from clarity
surrounding the Fourth Amendment’s application on third-party data.31 In themeantime, agencies should provide transparency on their respective policies and legal
basis for accessing and using commercial data.32• National data protection standards. Data privacy policies and standards that apply togovernment agencies alone will be inadequate, and in some cases may harm national
security interests.33 This is particularly important considering how adversaries (bothstate and non-state actors) can access and use data collected about U.S. persons.
As Congress considers proposals for national data security and privacy protection, it
should ensure any future legislation addresses the issue of microtargeting. As noted in
Chapter 1 of this report, AI systems will create new capabilities for state actors to target
individuals with precision as well as numerous aspects of our society like cities, supply
chains, universities, corporations, infrastructure, and financial transactions. Strong data
privacy protections will be necessary for a multitude of reasons, including to shield theUnited States from this new phenomenon.
• National framework for use of biometric technologies. In the absence of federal
legislation regulating the use of facial recognition, the existing patchwork of state andlocal laws and regulations creates a number of difficulties for government officials,
industry, and the public. This has led to actions including: companies prohibiting thesale of facial recognition to law enforcement,34 and local government bans on the use of
facial recognition have emerged from coast to coast.35 The lack of a consistent federal
approach is also a liability for national security agencies when best practices are notused locally.36 In developing regulation, it will be critical that policy and legislationaccount not only for facial recognition, but other types of biometric identification that,when combined with other AI technology, can introduce additional concerns.37

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Unregulated and Legal Data Collection & Brokering for
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Blueprint for Action: Chapter 8 - Endnotes
For instance, a recent DHS IG report criticizes the DHS Privacy Office for not establishing controls
to ensure that privacy compliance documentation is complete and submitted as required, andspecifically noted DHS had not performed required periodic reviews for new and evolving privacy
risks. DHS IG, DHS Privacy Office Needs to Improve Oversight of Department-wide Activities,
Programs, and Initiatives, OIG-21-06, (Nov. 4, 2020). Civil society members have noted that PIAs
and SORNs are often too opaque to be helpful, and that agencies sometimes try to shoehorn new
data collections under older SORNs and PIAs. See Comments of the Electronic Frontier Foundation
Regarding System of Records Notices 09-90-2001,09-90-2002, Electronic Frontier Foundation (Aug.
17, 2020), https://www.eff.org/files/2020/08/17/2020-08-17_-_eff_comments_re_hhs_regs_re_covid_
data.pdf (criticizing two SORNs issued by the Department of Health and Human Services during the
pandemic, as “overly vague in describing the categories of data collected, the data sources, and the
proposed routine uses of the data”).

1

The Commission acknowledges DHS’ steps to improve public records as noted in the DHS AI
Strategy: “Future AI systems implemented by DHS will require a public release of system information
with appropriate exceptions for certain sensitive military and intelligence systems, and some
exceptions for law enforcement activities. DHS will produce a framework for releasing AI system
information and a process for public comment.” See U.S. Department of Homeland Security Artificial
Intelligence Strategy, U.S. Department of Homeland Security at 14 (Dec. 3, 2020), https://www.dhs.
gov/publication/us-department-homeland-security-artificial-intelligence-strategy.

2

To support agencies in this goal, federal R&D investment should continue to advance the state of
the art for preserving personal privacy. For information regarding the critical AI research areas theCommission recommends OSTP prioritize, see the Chapter 11 Blueprint for Action. Agencies should
also assign responsibility for assessing the state of the practice and encouraging federated learningand anonymization pilots for government databases used in machine learning developments (e.g., toChief Data Officers at each agency).

3

Development practices should also include documenting trade-offs made, including optimizations
that cause a trade-off in performance across fairness metrics.

4

For instance, constraints about proxies for national origin or protected classes used for rules-based
system predictions.

5

These include: 1) Documenting operating thresholds including those that yield different true positiveand false positive rates or different precision and recall across demographics; (2) Assessing therepresentativeness of data and model for the specific context at hand; (3) Using tools to probe forunwanted bias in data, inferences, and recommendations; (4) Testing for fairness and articulating theapproach, performance, and metrics used. For an extensive list of practices, see the Appendix of thisreport containing the abridged version of NSCAI’s Key Considerations for Responsible Development& Fielding of AI. For additional details on the Commission’s recommendations to mitigate bias indevelopment and testing, see the Key Considerations for Responsible Development & Fielding of
Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).

6

Examples of tools available to assist in assessing and mitigating bias in systems relying on machinelearning include Aequitas by the University of Chicago, Fairlearn by Microsoft, AI Fairness 360
by IBM, and PAIR and ML-fairness-gym by Google. Microsoft’s AI Fairness checklist provides anexample of an industry tool to support fairness assessments. See Michael A. Madaio et al., CoDesigning Checklists to Understand Organizational Challenges and Opportunities around Fairness in
AI, CHI 2020 (April 25-30, 2020), http://www.jennwv.com/papers/checklists.pdf.

7

A widely used Industry example of a fairness metric is Equality of Opportunity (EEO), defined in
Machine Learning Glossary: Fairness, Google Developers (Feb. 11, 2020), https://developers.google.
com/machine-learning/glossary/fairness. Note that EEO is suited for some contexts and a poor fit forothers—this is why careful deliberation of the operational metrics for fairness must be establishedearly in the development process.

8

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Select practices include: 1) Assessing statistical results for performance over time to detect
emergent bias; 2) recurrent testing and validation at strategic milestones, especially for new
deployments and classes of tasks; and 3) Continuously monitoring AI system performance,
including the use of high-fidelity traces to determine if a system is going outside of acceptableparameters (e.g., for fairness and privacy leakage) pre-deployment and in operation. For an
extensive list of practices, see the Appendix of this report containing the abridged version of
NSCAI’s Key Considerations for Responsible Development & Fielding of AI. For additional details on
the Commission’s recommendation for maintenance and deployment, see the section on “System
Performance” in Key Considerations for Responsible Development & Fielding of Artificial Intelligence:
Extended Version, NSCAI (2021) (on file with the Commission).

910
ML systems in particular require ongoing assessments of privacy and fairness assurances,including the specific definition of fairness being assumed.
11This requires the creation of an AI TEVV handbook, a culmination of applied research, to create
the testing protocols that should be carried out by third-party testing lab(s) and the accreditation
procedures by which labs can become certified.
12
In some cases, exposure of test results could reveal weaknesses in a national security system that
could be exploited by an adversary.

As noted in Ethical Considerations for Commercial Use of AI, “rigorous testing is particularly
important for high-risk applications, and standards should be established to determine the natureof those applications.” See Frances Duffy, Ethical Considerations for Use of Commercial AI, Johns
Hopkins Applied Physics Laboratory (Dec. 2020).

13

Alice Xiang, Reconciling Legal and Technical Approaches to Algorithmic Bias, Tennessee Law
Review at 7 (July 13, 2020), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3650635. See
also Zachary Lipton, et al., Does Mitigating ML’s Impact Disparity Require Treatment Disparity?,
arXiv (Jan. 11, 2019), https://arxiv.org/abs/1711.07076. (Some approaches to mitigate disparate
outcomes explicitly make use of membership in protected classes such as race or gender, and aredemonstrably more equitable than comparable algorithms that are “blind” to protected classes.)

14

Disclosure requirements should be specific to each application of AI. See Frances Duffy,
Ethical Considerations for Use of Commercial AI, Johns Hopkins Applied Physics Laboratory at 31(December 2020). (“Appropriate disclosure requirements should be created for the purposes oftraceability in a court case or for the government’s own internal use.”)15

16
As noted in the Key Considerations, existing policies for contestability should be assessed
and updated as needed to ensure accountability and to mitigate errors though feedback loops.
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation to adopt policies to strengthen accountability and governance, see the section on“Accountability and Governance” in Key Considerations for Responsible Development & Fielding of
Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).
17
Due process rights require that individuals have the ability to meaningfully challenge a decision
made against them. In federal criminal trials, this includes having the government’s explanation of
how an unfavorable decision was reached, so it can be contested. In cases where AI-assisted or
AI-enabled decisions are made, certain AI techniques will be less conducive to due process. See
Danielle Keats Citron, Technological Due Process, Washington University Law Review (2008), https://
openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1166&context=law_lawreview; see also Ryan
Calo & Danielle Keats Citron, The Automated Administrative State: A Crisis of Legitimacy, Emory Law
Journal (April 3, 2020), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3553590.
18
For instance, evidentiary standards for admitting AI evidence in court have yet to be developed andare not encompassed in current Daubert standards guidance.
19
DHS’s Artificial Intelligence Strategy, dated December 2020, includes the establishment of a DHS
enterprise-wide AI Coordination and Advisory Council composed of internal subject matter experts
to monitor and support the adoption of AI technology by DHS Components. See U.S. Department ofHomeland Security Artificial Intelligence Strategy, U.S. Department of Homeland Security at 10 (Dec.
3, 2020), https://www.dhs.gov/publication/us-department-homeland-security-artificial-intelligencestrategy.

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Blueprint for Action: Chapter 8 - Endnotes
For issues relevant to AI system audits, see Global Perspectives and Insights: The IIA’s Artificial
Intelligence Auditing Framework Part, Institute of Internal Auditors (2018), https://na.theiia.org/
periodicals/Public%20Documents/GPI-Artificial-Intelligence-Part-II.pdf.
20

See e.g., Audit Map (last accessed Jan. 3, 2021), https://auditmap.ai/; The Next Generation ofInternal Auditing–Are You Ready?, Protiviti (2018), https://www.protiviti.com/sites/default/files/united_states/insights/next-generation-internal-audit.pdf.

21

22
See e.g., Bernhard Babel, et al., Derisking Machine Learning and Artificial Intelligence, McKinsey &Company (Feb. 19, 2019), https://www.mckinsey.com/business-functions/risk/our-insights/deriskingmachine-learning-and-artificial-intelligence; Saqib Aziz & Michael Dowling, Machine Learning andAI for Risk Management, Disrupting Finance at 33-50 (Dec. 7, 2018), https://link.springer.com/
chapter/10.1007/978-3-030-02330-0_3.

Xuning (Mike) Tang & Yihua Astle, The Impact of Deep Learning on Anomaly Detection, Law.com(Aug. 10, 2020), https://www.law.com/legaltechnews/2020/08/10/the-impact-of-deep-learning-onanomaly-detection/.

23

Examples include baseline AI standards and policy guidance for biometric identification
technologies; for government procurement of commercial AI products; and for federal data privacy
standards.

24

25
In the FY2021 NDAA, Congress directed the Secretary of Commerce, in consultation with othersenior Executive branch officials, to establish the National AI Advisory Committee, including a
Subcommittee on AI and Law Enforcement. The Subcommittee is tasked to “provide advice to the
President on matters relating to the development of artificial intelligence relating to law enforcement.”
Pub. L. 116-283, sec. 5104 William M. (Mac) Thornberry National Defense Authorization Act for Fiscal
Year 2021, 134 Stat. 3388 (2021).

These should seek to encourage contracts with companies that have transparent policies andpractices in support of traceability and auditability and those that share information about how theirtechnology works and how it performs in independent testing.
26“Federal government acquisition regulations require that agencies procure software commercially
off-the-shelf whenever possible, due to their cost effectiveness. Only when no comparable systems
exist are agencies permitted to develop government off-the-shelf solutions.” See Frances Duffy,
Ethical Considerations for Use of Commercial AI, Johns Hopkins Applied Physics Laboratory at S-1(December 2020). As standards and requirements for system development and testing evolve, it may
be helpful for the government to “establish and maintain a list of COTS AI technologies that havebeen vetted and approved for micro-purchasing, based on their consistency with government security
and testing standards, as well as their transparency.” This could facilitate both rapid procurement
and proper assessment of a vendor’s consistency with Responsible AI practices. See Frances Duffy,
Supplement to Ethical Considerations for Commercial Use of AI: Implications of Acquisition Scale,Johns Hopkins Applied Physics Laboratory (forthcoming).

27

For example, policymakers and legislators will need to direct future attention to policies to preserve
PCL as technological capabilities for ubiquitous sensing grow, e.g., in smart cities. In the future,ubiquitous sensing may make it impossible to distinguish U.S. persons’ data versus non-U.S. persons’data for AI analytics. Another example for continued consideration includes the role of AI in filtering to
remove U.S. persons’ information from bulk data and conversely using AI to reveal such information,
as minimization and de-minimization guidance may evolve based on AI efficacy relative to the status
quo.

28

Disallowed outcomes and guidance will need to be updated over time as community norms andtechnical capabilities change.

29

30
See, for example, Remarks of Commissioner Rebecca Kelly Slaughter: Algorithms and Economic
Justice, FTC (Jan. 24, 2020) https://www.ftc.gov/system/files/documents/public_statements/1564883/
remarks_of_commissioner_rebecca_kelly_slaughter_on_algorithmic_and_economic_
justice_01-24-2020.pdf; Artificial Intelligence and Machine Learning in Software as a Medical Device,
U.S. Food and Drug Administration (January 2021), https://www.fda.gov/medical-devices/softwaremedical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device.

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U P H O L D I N G D E M O C R AT I C VA L U E S : P R I VA C Y, C I V I L L I B E R T I E S , A N D C I V I L R I G H T S I N U S E S O F A I F O R N AT I O N A L S E C U R I T Y

See Byron Tau, Homeland Security Watchdog to Probe Department’s Use of Phone Location Data,
Wall Street Journal (Dec. 2, 2020), https://www.wsj.com/articles/homeland-security-watchdog-toprobe-departments-use-of-phone-location-data-11606910402 (reporting that “DHS’s general counsel
began examining [the agency’s use of location tracking data] after concerns were raised by several
offices within the department that use of the technology wasn’t compatible with [Carpenter],” and thatthe DHS IG planned to investigate the matter).

31

32
In ODNI Director Avril D. Haines’ confirmation hearing, she was asked about the IC’s use ofcommercially available location data. She testified that she would “try to publicize, essentially, aframework that helps people understand the circumstances under which we do that and the legal
basis that we do that under. . . I think that’s part of what’s critical to promoting transparency generally
so that people have an understanding of the guidelines under which the intelligence community
operates.” Charlie Savage, Intelligence Analysts Use U.S. Smartphone Location Data Without
Warrants, Memo Says, New York Times (Jan. 22, 2021), https://www.nytimes.com/2021/01/22/us/
politics/dia-surveillance-data.html.

Investigative reporting and opinion pieces have underscored the national security threats involved
with smartphone location data. Charlie Warzel & Stuart A. Thompson, They Stormed the Capitol.
Their Apps Tracked Them, New York Times (Feb. 5, 2021), https://www.nytimes.com/2021/02/05/
opinion/capitol-attack-cellphone-data.html?referringSource=articleShare; Stuart A. Thompson& Charlie Warzel, How to Track President Trump, (Dec. 20, 2019), https://www.nytimes.com/
interactive/2019/12/20/opinion/location-data-national-security.html; Stuart A. Thompson & Charlie
Warzel, Twelve Million Phones, One Dataset, Zero Privacy, New York Times (Dec. 19, 2019), https://www.nytimes.com/interactive/2019/12/19/opinion/location-tracking-cell-phone.html.

33

34
See Larry Magid, IBM, Microsoft And Amazon Not Letting Police Use Their Facial Recognition
Technology (June 12, 2020), https://www.forbes.com/sites/larrymagid/2020/06/12/ibm-microsoft-andamazon-not-letting-police-use-their-facial-recognition-technology/?sh=34b473dc1887; Asa Fitch,
Microsoft Pledges Not to Sell Facial-Recognition Tools to Police Absent National Rules, Wall Street
Journal (June 11, 2020), https://www.wsj.com/articles/microsoft-pledges-not-tosell-facial-recognitiontechnology-to-police-absent-national-rules-11591895282.

See Ban Facial Recognition, Fight for the Future (last accessed Feb. 4, 2021), https://www.
banfacialrecognition.com/map/.

35

36
The Department of Defense, the Drug Enforcement Administration, Immigrations and Customs
Enforcement, the Internal Revenue Service, the Social Security Administration, the U.S. Air Force
Office of Special Investigations, and the U.S. Marshals Service have all had access to one or morestate or local face recognition systems. See Clare Garvie, et al., The Perpetual Line-up: Unregulated
Police Face Recognition in America, Georgetown Law Center on Privacy & Technology (Oct. 18,
2016), https://www.perpetuallineup.org/.

Such types of identification aided by AI include voice recognition and gait detection. An example
of additional risks includes when biometric identification is coupled with other advancing capabilities;
for instance, for identity recognition or for emotion recognition. See Emotional Entanglement: China’s
Emotion Recognition Market and its Implications for Human Rights, Article 19 (January 2021), https://www.article19.org/wp-content/uploads/2021/01/ER-Tech-China-Report.pdf. See also Drew Harwell &Eva Dou, Huawei Tested AI Software that Could Recognize Uighur Minorities and Alert Police, Report
Says, Washington Post (Dec. 8, 2020), https://www.washingtonpost.com/technology/2020/12/08/
huawei-tested-ai-software-that-could-recognize-uighur-minorities-alert-police-report-says/; Parmy
Olson, The Quiet Growth of Race Detection Software Sparks Concerns Over Bias, Wall Street Journal(Aug. 14, 2020), https://www.wsj.com/articles/the-quiet-growth-of-race-detection-software-sparksconcerns-over-bias-11597378154.

37

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Blueprints for Action

PART TWOp411

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E

Chapter 9: A Strategy for Competition and Cooperation
Chapter 9 Annex: A Strategy for Competition and Cooperation

413
415

Chapter 10: The Talent Competition

421

Chapter 11: Accelerating AI Innovation

435

Chapter 12: Intellectual Property

465

Chapter 13: Microelectronics

483

Chapter 13 Annex: Executive Order on Microelectronic Strategy
Chapter 14: Technology Protection

489
493

Chapter 14 Annex: Technology Protection

511

Chapter 15: A Favorable International Technology Order

517

Chapter 15 Annex: A Favorable International Technology Order
Chapter 16: Associated Technologies

559
581

The following Blueprints for Action cover Part II of NSCAI’s Final Report. Part II, “Winning the Technology
Competition” (Chapters 9-16), outlines AI’s role in a broader technology competition and recommends
actions the government must take to promote AI innovation to improve all facets of national competitivenessand protect critical U.S. advantages. These Blueprints for Action complement the Commission’s Final
Report and mirror its organizational structure.
Building upon the top-line recommendations in the Commission’s Final Report, the Blueprints for Action
serve as more detailed roadmaps for Executive and Legislative branch actions to retain America’s AI
leadership position. The Blueprints for Action identify who should take a particular action––Congress,
the White House, or an Executive Branch department or agency. The Commission provides estimatedincreases in funding or appropriations as part of its recommendations. All recommendations that include
funding figures should be considered estimates for consideration by Congress and/or the Executive
Branch.

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Chapter 9: A Strategy forCompetition and Cooperation
Blueprint for Action

The United States should advance a comprehensive policy on China that promotes andprotects a rules-based international order. By investing in U.S. competitiveness andresilience at home, safeguarding critical technologies, and deepening coordination withallies and partners, the United States can pursue cooperation with China—where it is in thenational interest and from a position of strength. Properly sequenced and resourced, sucha strategy would generate solutions to global challenges and leverage formal diplomatic
dialogue to address critical issues around emerging technology.
Recommendation

Recommendation: Establish a High-Level U.S.-China Comprehensive Science andTechnology Dialogue (CSTD)The United States should establish a regular, high-level technology dialogue with Chinathat benefits the American people, remains faithful to our allies, and presses China to abide
by international rules and norms. The dialogue should focus on challenges presented byemerging technologies—to include AI, biotechnology, and other technologies as agreed
by both sides. The CSTD should have two overarching objectives:• Identify targeted areas of cooperation on emerging technologies to solve global
challenges such as climate change, public health, and natural disasters; and• Provide a forum to air a discrete set of concerns or friction points around specific uses of
emerging technologies while building relationships and establishing process between
the two nations.
The United States should be clear-eyed that the dialogue will not solve all our differenceswith China. The CSTD should be results-oriented, and it should achieve concrete outcomes
for the American people.
Actions for the White House and the Department of State:• Establish the CSTD.
o Emerging technologies play an instrumental role in the economic, social, andsecurity dynamics between the United States and China. Therefore, the CSTD
should be established as part of a comprehensive strategy toward China thatmobilizes democratic allies and partners in support of a rules-based international
order.

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A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O No The Department of State—in close coordination with the Office of Science andTechnology Policy—should lead the CSTD.
o The Department of State should build a process that is results oriented and aims
to address challenges and opportunities in the current relationship between the
United States and China related to the emerging technologies. For example:
1. The CSTD should explore collaborative technological solutions to global
challenges (e.g., climate change, healthcare and biodata, food safety andsecurity, and natural disasters).
2. The CSTD should identify areas of current challenges related to emerging
technologies (e.g., data sharing and privacy, supply chain risk management,international standards and norms, and intellectual property) and develop a
clear roadmap with milestones to address these issues.
o The CSTD should initiate personnel exchanges and data-sharing frameworks tosupport and foster identified research projects with reciprocal access to informationthat can lead to concrete results.
o The United States should identify leads for each of these topics (e.g., theDepartment of Energy, the U.S. Special Presidential Envoy for Climate, and the
National Oceanic and Atmospheric Administration for climate change; the National
Institutes of Health for healthcare; the U.S. Food and Drug Administration forfood safety; and the Department of Defense and U.S. Agency for International
Development for natural disasters).
• Relation to strategic dialogue. On a separate track from this CSTD, the Commission
has recommended that the United States and Chinese governments convene a
Strategic Security Dialogue (SSD) focused on eliminating misunderstandings andmisperceptions on key strategic issues and threats and reducing the likelihood of
inadvertent escalation. China has resisted U.S. attempts to create such a dialogue fornearly a decade, but its creation has never been more critical. The Commission’s vision
regarding the role of the SSD is explored in greater detail in Chapter 4 of this report.
o This dialogue should be the primary forum for discussions regarding practices
surrounding AI-enabled and autonomous weapon systems and should include
discussions on testing, doctrine, and use, and potentially the exploration of practicalconcrete confidence-building measures to mitigate risks.
o It is important to separate the SSD from the CSTD to ensure discussions related
to conflict escalation and crisis stability are insulated from political forces which
influence the broader U.S.-China bilateral relationship.

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Chapter 9 Annex: A Strategy for Competition and Cooperation
Draft Executive Order Establishing the Technology Competitiveness Council
By the authority vested in me as President by the Constitution and laws of theUnited States of America, and in order to provide a coordinated process for developing
technology policy and a national technology strategy and for monitoring its implementation,
it is hereby ordered as follows:
Section 1. Policy. The national security, economic competitiveness, and domestic
prosperity of the United States require a comprehensive and coordinated approach by theFederal Government to ensure long-term U.S. leadership across the entire suite of criticaland emerging technologies. To achieve this objective, this order establishes a Technology
Competitiveness Council to develop a National Technology Strategy and to coordinate
policies regarding critical and emerging technologies across the Federal Government.
Section. 2. The Technology Competitiveness Council.
(a) Establishment. There is established a Technology Competitiveness Council(Council).
(b) Membership. The Council shall be composed of the following members:(i) the Vice President, who shall be Chair of the Council;(ii) the Secretary of State;(iii) the Secretary of the Treasury;(iv) the Secretary of Defense;(v) the Attorney General;(vi) the Secretary of Commerce;(vii) the Secretary of Energy;(viii) the Secretary of Homeland Security;(ix) the Director of the Office of Management and Budget;(x) the Assistant to the President for Technology Competitiveness;(xi) the Assistant to the President for National Security Affairs;p415

A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O N(xii) the Assistant to the President for Science and Technology;(xiii) the Assistant to the President for Economic Policy;(xiv) the Assistant to the President for Domestic Policy;(xv) the United States Trade Representative;(xvi) the Chairman of the Joint Chiefs of Staff; and(xvii) the heads of other executive departments and agencies and othersenior officials within the Executive Office of the President, as determined by the
Chair.
A member of the Council may designate, to perform the Council functions of themember, a senior-level official who is part of the member’s department, agency, or office
and who is a full-time officer or employee of the Federal Government.
(c) Responsibilities of the Chair.
(i) The Chair or, upon his or her direction, the Assistant to the President
for Technology Competitiveness, shall convene and preside over meetings of theCouncil and shall determine the agenda for the Council.
(ii) The Chair shall authorize the establishment of such committees of
the Council, including an executive committee, and of such working groups,
composed of senior designees of the Council members and of other officials
invited to participate in Council meetings, as he or she deems necessary or
appropriate for the efficient conduct of Council functions.
(iii) The Chair shall report to the President on the activities andrecommendations of the Council. The Chair shall advise the Council as appropriate
regarding the President’s directions with respect to the Council’s activities andnational technology policy generally.
(d) Administration.
(i) The Council shall have a staff, headed by the Assistant to the President
for Technology Competitiveness.
(ii) The Office of Administration in the Executive Office of the President
shall provide the Council with such personnel, funding, and administrative support,
to the extent permitted by law and subject to the availability of appropriations, asp
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directed by the Chair or, upon the Chair’s direction, the Assistant to the President
for Technology Competitiveness, to carry out the provisions of this order.
(iii) To the extent practicable and permitted by law, including the Economy
Act, and within existing appropriations, agencies serving on the Council shall makeresources, including but not limited to personnel and office support, available to
the Council as reasonably requested by the Chair or, upon the Chair’s direction,
the Assistant to the President for Technology Competitiveness.
(iv) The heads of agencies shall provide, as appropriate and to the extent
permitted by law, such assistance and information to the Council as the Chair may
request to implement this order.
(v) Members of the Council shall ensure that their departments andagencies cooperate with the Council and provide such assistance, information,
and advice to the Council as the Council may request, to the extent permitted bylaw.
(vi) The creation and operation of the Council shall not interfere withexisting lines of authority and responsibilities in the departments and agencies.
(vii) On technology policy and strategy matters relating primarily to national
security, the Council shall coordinate with the National Security Council (NSC)
through the Deputy National Security Advisor for Cyber and Emerging Technology
to create policies and procedures for the Council that respect the responsibilities
and authorities of the NSC under existing law.
Section. 3. Functions of the Council. The Council shall:(a) develop recommendations for the President on U.S. technology competitivenessand technology-related issues, advise and assist the President in development andimplementation of national technology policy and strategy, and perform such other duties
as the President may prescribe;(b) develop and oversee the implementation of a National Technology Strategy as
required by section 4 of this order;(c) serve as a forum for balancing national security, economic, and technology
considerations of U.S. departments and agencies as they pertain to technology research,development, commercial interests, and national security applications;(d) coordinate policies across U.S. departments and agencies related to U.S.

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A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O N

competitiveness in critical and emerging technologies and ensure that policies designed
to promote U.S. leadership and protect existing competitive advantages are integrated
and mutually reinforcing; and(e) synchronize budgets and strategies, in consultation with the Director of the
Office of Management and Budget, in accordance with the National Technology Strategy.
Section. 4. National Technology Strategy. It is the policy of the United States to retain
leadership in critical and emerging technologies essential to U.S. national security andeconomic prosperity. Within one year of the date of this order, and annually thereafter,
the Council shall submit to the President a National Technology Strategy containing thefollowing elements:(a) an assessment of the U.S. Government’s efforts to preserve U.S. leadershipin key emerging technologies and prevent U.S. strategic competitors from leveraging
advanced technologies to gain strategic military or economic advantages over the UnitedStates;goals;(b) a review of existing U.S. Government technology policy, including long-range(c) an analysis of technology trends and assessment of the relative competitivenessof U.S. technology sectors in relation to strategic competitors;(d) identification of sectors critical for the long-term resilience of U.S. innovation
leadership across design, manufacturing, supply chains, and markets;(e) recommendations for domestic policy incentives to sustain an innovation
economy and develop specific, high-cost sectors necessary for long-term national security
ends;(f) recommendations for policies to protect U.S. and allied leadership in criticalareas through targeted export controls, investment screening, and counterintelligence
activities;(g) identification of priority domestic R&D areas critical to national security andnecessary to sustain U.S. leadership, and directing funding to fill gaps in basic and appliedresearch where the private sector does not focus;(h) recommendations for talent programs to grow U.S. talent in key critical andemerging technologies and enhance the ability of the Federal Government to recruit andretain individuals with critical skills into Federal service; andp
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BLUEPRINT FOR ACTION: CHAPTER 9(i) methods to foster the development of international partnerships to reinforce
domestic policy actions, build new markets, engage in collaborative research, and create
an international environment that reflects U.S. values and protects U.S. interests.
Section. 5. Advisory Committee on Technology Competitiveness.
(a) There is established an Advisory Committee on Technology Competitiveness(Committee) to provide advice and recommendations to the Council and matters within thescope of the Council’s responsibilities.
(b) The Committee shall include the Assistant to the President for Technology
Competitiveness and not more than 16 additional members appointed by the President.
The additional members shall include distinguished individuals from sectors outside of
the Federal Government. They shall have diverse backgrounds and expertise in national
security, economic competitiveness, and critical and emerging technologies relevantto the National Technology Strategy. The Assistant to the President for Technology
Competitiveness, along with one non-Federal member of the Committee, shall serve as
Co-Chairs. Members of the Committee shall serve without any compensation for theirwork on the Committee, but they may receive travel expenses, including per diem in lieu
of subsistence, as authorized by law for persons serving intermittently in the government
service (5 U.S.C. 5701-5707).
(c) The Committee shall meet as directed by the Co-Chairs of the Council andshall provide advice or work product solely for use by the Council in the performance of its
duties under this order.
(d) The Office of Administration in the Executive Office of the President shallprovide such funding and administrative and technical support as the Committee may
require.
(e) The Committee shall terminate two years from the date of this order unlessextended by the President.
Section. 6. General Provisions.
(a) If any provision of this order or the application of such provision is held to beinvalid, the remainder of this order and other dissimilar applications of such provision shallnot be affected.
(b) This order is not intended to, and does not, create any right or benefit,
substantive or procedural, enforceable at law or in equity by any party against the UnitedStates, its departments, agencies, or entities, its officers, employees, or agents, or any
other person.

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A S T R AT E G Y F O R C O M P E T I T I O N A N D C O O P E R AT I O N(c) Nothing in this order shall be construed to impair or otherwise affect:(i) the authority granted by law to an executive department or agency, orthe head thereof; or(ii) the functions of the Director of the Office of Management and Budget
relating to budgetary, administrative, or legislative proposals.
(d) This order shall be implemented consistent with applicable law and subject tothe availability of appropriations.

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Chapter 10:
The Talent Competition
Blueprint for Action

The United States must dramatically invest in its artificial intelligence (AI) talent pipelines inorder to remain at the forefront of AI now and into the future. It is imperative that the UnitedStates strategically invest in science, technology, engineering, and mathematics (STEM)
education at all levels and improve the immigration system to allow for more AI talent toenter and remain in the United States. Therefore, this Blueprint for Action is organized into
two broad categories of recommendations for strengthening the U.S. talent pipeline: theU.S. education system and immigration.
Talent Pipeline: U.S. Education System
Investments in STEM education are a necessary part of increasing American national
power and improving national security. This requires the United States to reform its
education system to produce both a higher quality and quantity of graduates.
Recommendation

Recommendation: Pass a New National Defense Education Act
In response to the Soviet launch of Sputnik in 1957, the United States passed the National
Defense Education Act (NDEA) in 1958 to extend U.S. leadership in education andinnovation.1 The NDEA promoted the importance of science, mathematics, and foreign
languages for students, authorizing more than $1 billion toward decreasing student loans,funding for education at all levels, and funding for graduate fellowships. Many students
were able to attend college because of this bill; 3.6 million students attended college in
1960, and by 1970, it was 7.5 million.2 This act helped America win the Space Race andaccelerated our ability to innovate, and it is widely regarded as one of the most successfulpieces of education legislation in U.S. history.
Now is the time for a new NDEA. The NDEA greatly increased the number of Americans
with a college degree, expanded the number of math and science teachers to meet thedemand of the K-12 system after the postwar baby boom, and was focused on defensecentric fields, particularly a deficiency in mathematicians. The impacts of federal spendingon higher education today are echoes of the investments made in the late 1950s by the
Eisenhower administration. The United States needs a second NDEA (NDEA II) in order toaddress the current digital talent gap and prevent the United States from falling behind in
the race for AI and STEM talent.

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T H E TA L E N T C O M P E T I T I O N

Actions for Congress:• Increase Funding for STEM- and AI-Focused After-School Programso STEM and AI-focused after-school learning programs expose students to STEMand AI-related programs beyond normal school hours. The length of the school
day limits teachers’ ability to cover a myriad of topics. American elementary school
students are exposed to an average of 20 minutes of science and 60 minutes ofmath during the school day.3 Given the short amount of time that teachers areable to spend on STEM in their classrooms, some school districts have begun
to offer after-school programs that expose students to STEM in a less structured
environment. More time spent studying STEM topics helps students’ test scores,
and for those who are underrepresented in STEM fields, federal funding for afterschool programs will increase students’ accessibility to quality educational tools.4
Appropriations for after-school programs should favor applications that are jointly
submitted by a local educational agency and a community-based organization orother public or private entity as a way to defray costs and encourage community
engagement.
• Increase Funding for STEM- and AI-Focused Summer Learning Programso STEM- and AI-focused summer learning programs will encourage students toengage in STEM and AI activities during the months when students are typically
unengaged and experience learning loss. The 21st Century Community Learning
Centers Act is an example of a program that funds “academic enrichment
opportunities during non-school hours for children, particularly students who
attend high-poverty and low-performing schools” and has exhibited proven,positive results.5 Much like the after-school initiative, priority should be given to
those applications that are jointly submitted by a local educational agency and acommunity-based organization or other public or private entity.
• Allocate Funds for K-12 STEM Teacher Recruitment, Retention, and Trainingo Teachers are an integral part of the learning experience for STEM subjects. One
inequity is the lack of teachers with the requisite proficiency in STEM. Evidenceshows that STEM teacher training for current teachers is sporadic, ineffective, andnot effective in addressing the specific needs of individual students.6 Moreover,recruiting high-quality K-12 teachers with STEM experience and proficiency isdifficult. This is particularly concerning, as teachers are one of the most influential
aspects of school, having two to three times the impact of other components, suchas leadership and school services.7 As the world continues to integrate technology
into education, teachers must be taught how to use this technology as well as
how to teach students the critical foundations and basic functions that comewith it.8 Support should be given to school districts to create and execute teacher
training in AI concepts, techniques, and curriculum design, with preference given
to professional development courses that count against continuing education
requirements for teacher certification.
• Direct and Fund the National Science Foundation to Create STEM Scholarships andFellowshipso We recommend that the NSF create 25,000 STEM undergraduate scholarships,
5,000 STEM PhD fellowships, and 500 postdoctoral positions over five years toincrease the number and quality of STEM and AI practitioners that will reach the job
market in a few years.9 Growing the nationwide STEM talent pool in high-demandp422

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areas requires a pipeline of students who have studied relevant STEM courseworkduring their undergraduate careers. Between 2000 and 2017, the share of STEM
bachelor’s degrees earned—as a percentage of total bachelor’s degrees earnedin the U.S.—rose from 32% to 35%.10 The sharpest recent increases were among
computer science and engineering majors.11 For AI specifically, a degree in cognitive
science or computer science with concentrations in AI or machine learning (ML)can pave the way for future careers in AI research or practice. AI is rarely offered
as a major at the undergraduate level. Instead, universities offer standalone
courses, a sequence of AI courses, or the option to study a technical major with aconcentration in AI. Until a major in AI is more universally offered at U.S. universities,
STEM scholarships will increase the number of individuals with the skills necessary
to work on AI.
o Scholarship and fellowship recipients should receive full tuition and room andboard. Undergraduate recipients should receive a stipend of $40,000 a year, andgraduate recipients should receive a stipend of $70,000 a year.12 Combined withpostdoctoral positions, this will bring the total cost to $7.2 billion over five years.13
Actions for the Department of Education:• Add Elements of Computational Thinking and Statistics to Student Testingo Computational thinking and statistics are vital for students to understand how AI
works.14 As interdisciplinary fields, the use of computational thinking and statistics
within AI can be found at all stages of discovery, from developing and planningstudies to assessing the results. Critical thinking along with problem-solving arevital skills taught in statistics. Unfortunately, the majority of high schools in America
do not require testing for skills related to computational thinking for graduation.15
There is no way to comprehensively measure U.S. students’ overall abilities or
aptitude for skills related to computational thinking and statistics. Students aretaught what is needed to pass exams. Compared to other countries, many of whichhave statistics in their curriculum, the United States ranks low in math.16 By including
subjects critical for computational thinking and statistics in standardized testingat the state level, the United States can gain a better understanding of students’capabilities and work to implement curriculum and lessons focused more on
computational thinking and statistics in order to ensure students’ success.
Recommendation

Recommendation: Require Statistics in Middle School and Computer Science Principles
in High School
Actions for State Legislatures:• Require statistics as a required course in middle school and computer science
principles in high school. Many fundamental concepts in AI, ML, and theirsubfields are applied statistics in disguise.17 The techniques and algorithms
used are heavily based in statistical methods, such as cluster analysis and model
selection. Statistics and computer science principles are needed to prepare
students for AI courses, concentrations, and internships. Providing training instatistics starting in middle school will better prepare students for the increasingly
advanced analytic techniques in demand for AI and STEM careers. Similarly,
currently only 47% of U.S. high schools offer computer science coursework.18 Thisis much higher than just a decade ago, thanks to nationally organized initiatives,
but this still leaves many high schools without computer science education.

p423

T H E TA L E N T C O M P E T I T I O NMoreover, adoption has been piecemeal and curriculum depth varies widely.
Therefore, state action is needed.
• On their own, neither statistics nor computer science are sufficient to teachstudents the concepts needed to understand AI. Having both allows students toexperience the critical bases that must be covered early on in order to prepare
students for a technological career. Simple math such as basic probability andsummarizing numerical data is applying concepts of statistics and computerscience.
Talent Pipeline: Immigration
Immigration reform is imperative for strengthening the U.S. talent pipeline, particularly given
the significant benefits the United States experiences due to highly skilled immigration.
Therefore, the United States must pursue reforms to accelerate highly skilled immigration
and retention of international students within the United States.
The following recommendations are intended to help the United States lead the world’s
development and implementation of AI by gaining a decisive majority of a critical andlimited resource: AI talent. The recommendations will improve the United States’ ability to
attract talent to the United States and, just as important, away from competing countries.
The United States needs to take bold steps to ensure it wins the competition for international
talent for years to come. Such steps should ensure that our immigration system attracts
students, technical experts, and entrepreneurs; grants stability while they continue tocontribute to the American economy and research environment; and retains students,
entrepreneurs, and experts rather than sending them home or to competing countries. The
best way to accomplish these goals and to send a clear message to AI and STEM talentaround the world is to pass a National Security Immigration Act that specifically helps
STEM talent remain in the United States, reduces the overall burden of the citizenshipprocess, and creates specific paths for entrepreneurs.
Recommendation: Pass a National Security Immigration Act

Recommendation

1) Grant Green Cards to All Students Graduating with STEM PhDs from Accredited
American Universities
This would issue an incredibly clear message to talented young people around the worldthat they are welcome in the United States and would ease their transition to American
citizenship. It is a very aggressive maneuver to gain a larger share of the world’s STEM
talent.
Such a proposal is admittedly bold, but the benefits of attracting vetted, top-tier talent
outweigh the risks. Bold measures are needed to preserve America’s advantages in STEM
fields today and to ensure we out-innovate and outperform competitors in the future.19 Fewp
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other proposals are significant enough to make a dramatic difference in the competition
for talent, or to force China into a dilemma on their domestic front. It is also noteworthy thatsimilar proposals have received bipartisan support in the past.20
Actions for Congress:• Amend 8 U.S.C. 1151(b)(1) to grant lawful permanent residence to any foreign
national who:o Graduates from an accredited United States institution of higher education
with a doctoral degree in a field related to science, technology, engineering, ormathematics in a residential or mixed residential and distance program;o Has a job offer in a field related to science, technology, engineering, ormathematics; ando Does not pose a national security risk to the United States.
• Vetting for national security concerns should be enabled by the FBI andIntelligence Community• Graduates granted lawful permanent residence through this program should notcount against overall or country-of-origin green card caps
2) Double the Number of Employment-Based Green Cards
Whether one aims for the United States to achieve AI dominance, grow gross domestic
product (GDP), stimulate job growth, reduce government deficits, or bolster the solvencyof the U.S. Social Security program, the most straightforward solution is the same: increase
the number of highly skilled permanent residents. Under the current system, employmentbased green cards are scarce: 140,000 per year, fewer than half of which go to the principal
worker.21 This leaves many highly skilled workers unable to gain permanent residency andunable to transfer jobs or negotiate with employers as effectively as domestic workers. If
underpaid, these workers cannot leave their jobs or bargain for better wages without risking
revocation of the employer’s green card sponsorship or even firing and forced departurefrom the United States. This decreases the appeal of joining the American workforce.
The H-1B system is problematic for most employers, as well, with a consistently
oversubscribed “lottery” of 85,000 visas each year (of which 20,000 are reserved foradvanced degree holders from U.S. universities).22 To reduce the backlog of highly skilledworkers, the United States should double the number of employment-based green cards,
with an emphasis on permanent residency for STEM and AI-related fields. If it were easierfor U.S. employers to sponsor global talent for a green card as opposed to an H-1B visa,
the H-1B program could then serve its originally intended function as a vehicle for truly
temporary high-skilled work needs.

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Action for Congress:• Amend 8 U.S.C. 1151(d)(1)(A) by changing “140,000, plus” to “280,000, plus”
3) Create an Entrepreneur Visa
International doctoral students are more likely to want to found a company or become an
employee at a startup than their native peers; but, in practice, they are less likely to pursue
those paths. One reason is the constraints of the H-1B visa system.23 Similarly, immigrant
entrepreneurs without the capital to use the EB-5 route to permanent residency are forced
to use other visas that are designed for academics and workers in existing companies, notentrepreneurs.24 All of these issues make the United States less attractive for international
talent and, just as important, reduce the ability of startups and other small companies, themain source of new jobs for Americans, to hire highly skilled immigrants that have beenshown to improve the odds that the business will succeed.
Actions for Congress:• Create an entrepreneur visa. This visa should serve as an alternative to employeesponsored, investor, or student visas and should instead target promising potential
founders. Legislation should:
o Define an entrepreneur as an alien whose organization and operation of a businesswould provide significant public benefit to the United States if allowed to stay in the
country for a limited trial period to grow a company.
o Prioritize entrepreneurs active in high-priority fields such as AI or in fields that use AI
for other applications, such as agriculture. The National Science Foundation should
update the list of high-priority fields every three years.
o Use capital capture as a screening criterion for entrepreneurs.
o Emphasize job creation for Americans—potentially emphasizing underserved
regions or areas with high unemployment—as a core factor in the assessment ofsignificant public benefit.
4) Create an Emerging and Disruptive Technology Visa
A new nonimmigrant visa designed to attract top technology talent in critical fields would
allow universities and businesses that work on AI and other emerging technologies accessto a greater pool of talent necessary to create cutting-edge research. It would also respond
more flexibly to labor market demands as new technologies emerge. The effect would be to“revitalize our country’s research ecosystem, empower our country’s innovation economy,
and ensure that the United States remains a world superpower in the coming decades.”25

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Action for Congress:• Create an emerging and disruptive technology visa that:o Requires the National Science Foundation to identify critical emerging anddisruptive technologies every three years;o Allows students, researchers, entrepreneurs, and technologists in applicable fields
to apply; ando Does not include emerging and disruptive technology visa holders in any other visa
category cap.
Recommendation

Recommendation: Broaden the Scope of “Extraordinary” Talent to Make the O-1 Visa More
Accessible and Emphasize AI Talent
The O-1 temporary worker visa is for people with extraordinary ability or achievement.26
O-1 visas are valid for three years and can be renewed annually an unlimited number oftimes. There is also no limit on the number of visas issued per year. Currently, about 15,000
to 18,000 new O-1 visas are issued annually.27 For these reasons, the O-1 visa is generally
a more flexible visa category than the H-1B visa, which is, with some exceptions, cappedin duration and number.28
While O-1 visas provide many advantages, they are a poor fit for many highly skilled workers
due to the uncertainty of their criteria and the administrative burden of the application andadjudication process. Adjudicators determine an applicant’s eligibility through subjective
assessments of whether applicants received nationally recognized prizes, have been
published in major outlets, have done original work of major significance, and meet othersimilar criteria. For the sciences and technology, this aligns largely with academic criteriasuch as publications in major outlets and is not well suited for people who excel in industry.
Actions for the U.S. Citizenship and Immigration Service (USCIS):• Issue new guidance with clear and broad standards for regulatory criteria, such as
what counts as a major outlet, nationally recognized prize, or original work.
o For example, if a publication in a top-five academic journal within a scientist’s fieldcounts as a major outlet, many PhD graduates would likely qualify.
• Initiate a regulatory process to decrease the threshold for eligibility for an O-1 visa,
for example by reducing the number of criteria an applicant has to fulfill.
o The current standard is three out of eight criteria.29• Broaden criteria to better accept non-academic AI and STEM accomplishments.
o For instance, some top-tier engineers have not earned an undergraduate degree or
published major papers, instead focusing on developing and monetizing cuttingedge technology in the private sector. New criteria should make O-1 visas moreaccessible to this demographic.

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Recommendation: Implement and Advertise the International Entrepreneur Rule

Recommendation

The International Entrepreneur Rule (IER) allows USCIS to grant a period of authorized
stay to international entrepreneurs who demonstrate that “their stay in the United States
would provide a significant public benefit through their business venture.”30 The IER would
be relatively easy for the Executive Branch to implement and is more directly tied to job
creation than most other immigration proposals, making it more helpful to most Americans.
Action for the President:• An immediate executive action could announce the administration’s intention to
use the IER to boost immigrant entrepreneurship, job creation for Americans, andeconomic growth.
Actions for the USCIS:• Announce that USCIS will give priority to entrepreneurs active in high-priority
STEM fields such as AI, or in fields that use AI for other applications, such as
agriculture.
• Use capital capture as a screening criterion for entrepreneurs.
• Emphasize job creation for Americans—potentially emphasizing underserved
regions or areas with high unemployment—as a core factor in its assessment ofsignificant public benefit.
Recommendation: Expand and Clarify Job Portability for Highly Skilled Workers

Recommendation

The Department of Homeland Security (DHS) published a final rule in November 2016that made a number of reforms to improve temporary work visa programs, including somemeasure of relief for workers tethered to the employer sponsoring their green card petition
during a potentially decades-long waiting period.31 The rule allows workers on H-1B,
O-1, and other temporary work visas to obtain open-market work permits for a one-year
renewable period under compelling circumstances. Compelling circumstances include:• Serious illness or disability faced by the worker or his/her dependents,• Employer retaliation against the worker,• Other substantial harm to the worker, and• Significant disruption to the employer.32
The criteria for compelling circumstances are too limited and ambiguous. Expanding visa
holders’ ability to obtain a work permit would allow for greater rates of entrepreneurship,tighter skill-matching with new employers, and for visa holders to negotiate compensation
on a level playing field with domestic workers.

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Actions for the USCIS:• Clarify when highly skilled, nonimmigrant workers are permitted to change jobs or
employers;• Increase job flexibility when an employer either withdraws their petition for an
H-1B or goes out of business, is acquired, or downsizes; and• Increase flexibility for H-1B workers seeking other H-1B employment.

Recommendation

Recommendation: Recapture Green Cards Lost to Bureaucratic Error
Congress mandates annual caps on the number of green cards that may be issued tocertain family-based immigrants (226,000) and employment-based immigrants (140,000).
33
Because federal agencies do not want to exceed the annual green card caps, theygenerally issue fewer green cards than they are allowed to. Due to this trend, as of 2009,
the Federal Government had not issued more than 326,000 green cards.34 The number
today is likely higher, but DHS has not published updated statistics.
Actions for the Departments of Homeland Security and State:• Publish an annual report on the number of green cards lost due to bureaucraticerror.
• Review whether existing authorities can be used to:o Issue lost green cards the subsequent year without counting against green cardcaps.
o Prioritize highly skilled immigrants who have waited the longest, followed by highly
skilled immigrants with long projected wait times.
• If existing authorities are insufficient, engage with Congress to recapture green
cards lost to bureaucratic error through special legislation.

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BLUEPRINT FOR ACTION: CHAPTER 10

Blueprint for Action: Chapter 10 - Endnotes
1

Pub. L. 85-864.

2
Sputnik Spurs Passage of the National Defense Education Act, U.S. Senate (last accessed Jan. 29,2021), https://www.senate.gov/artandhistory/history/minute/Sputnik_Spurs_Passage_of_National_
Defense_Education_ Act.htm#:~:text=The%20National%20Defense%20Education%20Act%20of%20
1958%20became%20one%20of,and%20private%20colleges%20and%20universities.

Highlights From the 2018 NSSME+, The National Survey of Science and Mathematics Education at
17 (Jan. 2019), http://horizon-research.com/NSSME/wp-content/uploads/2019/01/Highlights-from2018-NSSME.pdf. Additionally, almost half of Americans believe that students don’t spend enoughtime during school hours on STEM subjects. Cary Funk & Kim Parker, Most Americans Evaluate
STEM Education as Middling Compared with Other Developed Nations, Pew Research Center (Jan. 9,2018), https://www.pewsocialtrends.org/2018/01/09/5-most-americans-evaluate-stem-education-asmiddling-compared-with-other-developed-nations/.
3

Kristen A. Malzahn, et al., Are All Students Getting Equal Access to High-Quality Mathematics
Education? Data From the 2018 NSSME+, The National Survey of Science and Mathematics Education
at 15 (Feb. 2020), http://horizon-research.com/NSSME/wp-content/uploads/2020/02/Math-EquityReport.pdf.

4

21st Century Learning Centers, Department of Education (last accessed Jan. 1, 2021), https://www2.
ed.gov/programs/21stcclc/index.html.
5

Successful K-12 STEM Education, National Research Council at 20-21 (2011), https://www.nap.edu/
catalog/13158/successful-k-12-stem-education-identifying-effective-approaches-in-science.

6

Isaac M. Opper, Teachers Matter: Understanding Teachers’ Impact on Student Achievement, RAND
(2019), https://www.rand.org/education-and-labor/projects/measuring-teacher-effectiveness/teachersmatter.html.

78
Amy Johnson, et al., Challenges and Solutions When Using Technologies in the Classroom, Adaptive
Educational Technologies for Literacy Instruction (2016), https://files.eric.ed.gov/fulltext/ED577147.
pdf.

James Manyika & William H. McRaven, Innovation and National Security: Keeping our Edge, Council
on Foreign Relations (Sept. 2019), https://www.cfr.org/report/keeping-our-edge/recommendations/.
9

10
Josh Trapani & Katherine Hale, Trends in Undergraduate and Graduate S&E Degree Awards,
National Science Foundation at Figure 2-6 (Sept. 4, 2019), https://ncses.nsf.gov/pubs/nsb20197/
trends-in-undergraduate-and-graduate-s-e-degree-awards.
11

Id.

12
The $70,000 stipend is intended to incentivize American students to pursue graduate research,rather than transitioning to the private sector directly after completing their undergraduate degree.
Research has shown that higher stipends increase the number and quality of program applicants,
likely “attract[ing] some potentially outstanding science and engineering students who would
otherwise choose other careers.” See Richard Freeman, et al., Supporting “The Best and Brightest”
in Science and Engineering: NSF Graduate Research Fellowships, The National Bureau of Economic
Research and Harvard University at abstract (Mar. 2006), https://users.nber.org/~sewp/Freeman_
NSFstip_Proceedings.pdf.

Based on the Commission staff’s research, the Commission calculates this total allotting an
estimated $175,000 per postdoctoral fellow per year.

13

14
Computational thinking can be defined as “a way of solving problems, designing systems, andunderstanding human behavior that draws on concepts fundamental to computer science.” Centerfor Computational Thinking at Carnegie Mellon (last accessed Feb. 8, 2021), http://www.cs.cmu.
edu/~CompThink/. Some current subjects relevant to computational thinking include computerscience, coding, and statistics.

See 50 State Comparison: High-School Graduation Requirements, Education Commission of
the States (Feb. 2019), https://internal-search.ecs.org/comparisons/high-school-graduationrequirements-01. As shown in this 50-state comparison, unlike algebra, statistics is rarely listed as a
graduation requirement. See Id.

15

p
431

T H E TA L E N T C O M P E T I T I O NErin Richards, Math Scores Stink in America. Other Countries Teach It Differently and
See Higher Achievement, USA Today (Feb. 29, 2020), https://www.usatoday.com/story/
news/education/2020/02/28/math-scores-high-school-lessons-freakonomics-pisa-algebrageometry/4835742002/.

16

17
Statistics includes foundations of probability, hypothesis testing, expected utility, decision analysis,
and causality, and introductions to topics in the broader data sciences, such as basics of patternrecognition and machine learning.
18
2020 State of Computer Science Education: Illuminating Disparities, Code.org Advocacy Coalition,
Computer Science Teachers Association & Expanding Computing Education Pathways Alliance(2020), https://advocacy.code.org/2020_state_of_cs.pdf.
19
According to the National Science Foundation (NSF), in 2018, 179,500 undergraduate and 233,600
graduate international students were enrolled in science and engineering programs in the UnitedStates. Beethika Kahn, et al., The State of U.S. Science and Engineering 2020, NSF (Jan. 15, 2020),
https://ncses.nsf.gov/pubs/nsb20201/u-s-and-global-education#degree-awards. It should not beassumed that all of these students would meet the listed criteria.
20
A 2013 Senate-passed bill would have exempted all PhD and master’s STEM degree holders (U.S.
graduates) and all PhD holders in any field (worldwide graduates) from green card caps. Madeleine
Sumption & Claire Bergeron, Remaking the U.S. Green Card System: Legal Immigration Under theBorder Security, Economic Opportunity, and Immigration Modernization Act of 2013, Migration Policy
Institute 2, at 8 (June 2013), https://www.migrationpolicy.org/research/remaking-us-green-cardsystem-legal-immigration-economic-opportunity.

William Kandel, The Employment-Based Immigrant Backlog, Congressional Research Service at 4-5(March 26, 2020), https://fas.org/sgp/crs/homesec/R46291.pdf.

21

22
H-1B Fiscal Year (FY) 2021 Cap Season, U.S. Citizenship & Immigration Services (last accessed
Jan. 4, 2021), https://www.uscis.gov/working-in-the-united-states/temporary-workers/h-1b-specialtyoccupations-and-fashion-models/h-1b-fiscal-year-fy-2021-cap-season.

Michael Roach, et al., Are Foreign STEM PhDs More Entrepreneurial? Entrepreneurial
Characteristics, Preferences and Employment Outcomes of Native and Foreign Science & Engineering
PhD Students, National Bureau of Economic Research at 12 (2019), https://www.nber.org/papers/
w26225.

23

William R. Kerr, Global Talent and U.S. Immigration Policy: Working Paper 20-107, Harvard Business
School at 14 (2020), https://www.hbs.edu/faculty/Publication%20Files/20-107_0967f1ab-1d23-4d54b5a1-c884234d9b31.pdf.

24

25
Oren Etzioni, What Trump’s Executive Order on AI Is Missing: America Needs a Special Visa
Program Aimed at Attracting More AI Experts and Specialists, Wired (Feb. 13, 2019), https://www.
wired.com/story/what-trumps-executive-order-on-ai-is-missing/.
26
O-1A is the relevant O-1 category for STEM; it also encompasses those in “education, business,or athletics.” O-1 Visa: Individuals with Extraordinary Ability or Achievement, U.S. Citizenship &Immigration Services (last accessed Jan. 29, 2021), https://www.uscis.gov/working-in-the-unitedstates/temporary-workers/o-1-visa-individuals-with-extraordinary-ability-or-achievement.

Nonimmigrant Visas Issued by Classification, U.S. Department of State (last accessed Jan. 29,2021), https://travel.state.gov/content/dam/visas/Statistics/AnnualReports/FY2020AnnualReport/
FY20AnnualReport-TableXVB.pdf.

27

H1-B Fiscal Year (FY) 2021 Cap Season, U.S. Citizenship & Immigration Services (last accessed
Jan. 29, 2021), https://www.uscis.gov/working-in-the-united-states/temporary-workers/h-1b-specialtyoccupations-and-fashion-models/h-1b-fiscal-year-fy-2021-cap-season.

28

29

8 C.F.R. 214.2(o)(3)(iii)(b).

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BLUEPRINT FOR ACTION: CHAPTER 10

Blueprint for Action: Chapter 10 - Endnotes
International Entrepreneur Parole, U.S. Citizenship & Immigration Services (last accessed Jan.
29, 2021), https://www.uscis.gov/humanitarian/humanitarian-parole/international-entrepreneurparole. There is currently no visa category well-suited to entrepreneurship in U.S statutes related toimmigration. The IER, which relies on parole authority, was initiated after legislative avenues were
exhausted. Legislative fixes would be preferable but have so far proven politically infeasible.

30

81 Fed. Reg. 82398, Retention of EB-1, EB-2, and EB-3 Immigrant Workers and Program
Improvements Affecting High-Skilled Nonimmigrant Workers, U.S. Department of Homeland Security(Nov. 18, 2016), https://www.federalregister.gov/d/2016-27540.

31

32

p
433

Id.

T H E TA L E N T C O M P E T I T I O N

Julia Gelatt, Explainer: How the U.S. Legal Immigration System Works, Migration Policy Institute(April 2019), https://www.migrationpolicy.org/content/explainer-how-us-legal-immigration-systemworks.

33

34
A 2009 report to Congress indicates that some 242,000 unused family-based green cards were
ultimately applied to the employment-based backlog. Congress also recaptured some 180,000 outof roughly 506,000 unused employment preference green cards via special legislation, leaving morethan 326,000 green card numbers wasted out of the nearly 750,000 unused green cards. Annual
Report 2010, Department of Homeland Security Citizenship and Immigration Services Ombudsman at
35 (June 30, 2010), https://www.dhs.gov/xlibrary/assets/cisomb_ 2010_annual_report_to_congress.
pdf.

p
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BLUEPRINT FOR ACTION: CHAPTER 11

Chapter 11:
Accelerating AI Innovation
Blueprint for Action

The United States remains the world’s artificial intelligence (AI) leader. However, trends
within the United States indicate underlying weaknesses. The Federal Government holds
the responsibility to provide strategic direction and long-term resources to strengthen
the nation’s foundation for AI innovation. The United States—through government
leadership, and in partnership with industry and academia—must increase the diversity,competitiveness, and accessibility of its AI innovation environment to ensure continued
leadership.
Recommendation

Recommendation: Scale and Coordinate Federal AI R&D Funding
The United States must reinforce the foundation of technical leadership in AI by enacting
a bold, sustained federal push to invest in AI R&D to foster a nationwide landscapeof AI innovation and drive breakthroughs in the next generation of AI technologies by
establishing a National Technology Foundation, funding AI R&D at compounding levels,
establishing additional National AI Research Institutes, and making big bets on talent andinnovative ideas.
Component 1: Establish a National Technology Foundation
In the wake of Russia’s successful launch of the Sputnik satellite in 1957, Congress
made significant investments in the National Science Foundation (NSF) to shore up U.S.
leadership in science and technology.1 Since then, the NSF has supported researchacross the frontiers of science and engineering, funding efforts that contributed to thedevelopment of the Internet, smartphones, and additive manufacturing.2 However, intoday’s heightened geopolitical technology competition, even bolder action is needed tomeet the promise of emerging and disruptive technologies like AI, drive U.S. innovation
toward the national interest, and secure our economic future.
The Commission recommends the creation of a National Technology Foundation (NTF) as
an independent federal agency and sister organization to the NSF to provide the means
to move science more aggressively into engineering and scale innovative ideas into
reality. This will require an organization that is structured to accept higher levels of risk
and empowered to make big bets on innovative ideas and people. It also demands anemphasis on the transition of technology from the lab to the market.

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R& D Ecosystem.

*Representing the current top 10 federal funders of non-defense AI R&D
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The current federal R&D posture lacks an organization that provides the level of investment
and focus in applied research and technology engineering commensurate with the benefit
that technology breakthroughs could bring to the U.S. economy, society, and national
security. In contrast to fundamental science, technology development embodies a morecostly undertaking,3 requires the support of a diverse base of researchers and developers—
including private-sector partners—and involves regular risk-taking. The Defense Advanced
Research Projects Agency (DARPA) does this effectively, but for specific national security–
focused ends and primarily through a prescribed program-based approach.
The NTF would drive technology progress at a national level by focusing on generating value
at intermediate levels of technical maturity, prioritizing use-inspired concepts,4 establishing
infrastructure for experimentation and testing, and supporting commercialization of
successful outcomes. It would work in close concert with the NSF, DARPA, and otherinteragency partners to strengthen investment in domestic science and technology (S&T),
providing the fuel for the development and delivery of AI and other technologies on which
future economic progress and national security advantages rely.
To provide the level of attention to advance technologies of strategic importance, the NTF
should focus efforts around a set of routinely updated priority research areas, such as those
the Commission has identified as technologies critical to U.S. national competitiveness5:
1.
2.
3.
4.

Artificial Intelligence
Biotechnology
Quantum Computing
Semiconductors andAdvanced Hardware

5.
6.
7.
8.

Robotics and Autonomy
5G and Advanced Networking
Advanced Manufacturing
Energy Technology

We do not underestimate the challenge of establishing a new institution; however, we see
it as a strategic imperative. The NTF represents a long-term investment in America’s ability
to lead in AI and other disruptive technologies and apply technology toward efforts ofsocietal importance. It would provide access to the resources and tools that could promotea national culture of experimentation and invention with new technology.

Given the criticality of holistically strengthening the national R&D landscape, the NTF should
not detract from the level of appropriations for NSF, DARPA, or other existing federal R&D
efforts. Rather, it should be instantiated as part of a broader approach that bolsters NSF as
an institution of enduring, critical importance and amplifies federal support for technology
R&D through existing channels as the organization gets off the ground.
Action for Congress:• Authorize and appropriate funding to support the establishment of the NTF.
o To match the envisioned enlargement of U.S. technology efforts, federal investmentp437

A C C E L E R AT I N G A I I N N O VAT I O N

in the NTF should gradually increase from Fiscal Year 2022 to Fiscal Year 2026 foran ultimate estimated operating budget of $20 billion per year.
■

Additional funds for facilities and equipment necessary for the Foundation’s
creation, estimated at around $30 million, should be made available starting in
Fiscal Year 2022.

o A National Technology Board—with members appointed by the President—shouldbe created to provide policy direction to the NTF, supervise the Foundation’s
major initiatives, and ensure that its research focus areas are updated to reflect
technology trends. The Board’s directives and actions should be informed by the
National Technology Strategy proposed by the Commission and, when necessary,coordinated with the Technology Competitiveness Council—both of which areseparately recommended in this report.6
o Jointly, a Director and Deputy Director appointed by the President should
coordinate programming across the Foundation’s directorates and with externalorganizations.
o The NTF should be empowered to implement a portfolio of responsibilities:
■

■

■

■

■

■

■

Distribute funding through grants, cooperative agreements, and contracts
awarded through competitive, risk-acceptant processes to academic andprivate-sector researchers, nonprofits, and consortia.
Manage a component of its funding through an innovation unit modeled on
DARPA in which independent program managers would fund proposals fromboth industry and academia to advance solutions to forward-looking researchquestions.
Promote the transfer of technology advancements to the government as wellas the commercial sector.
Run prize competitions to catalyze research around significant technology
challenge problems.
Manage national technology resources and infrastructure that democratize an
ability to build, test, and experiment.
Contribute to the success of the regional innovation clusters envisioned by theCommission by participating in the proposed technology program office andliaising with industry at Technology Research Centers.
Contribute to international R&D collaborations and standards-setting
dialogues that strengthen U.S. strategic partnerships.

Component 2: Increase Federal Funding for Non-Defense AI R&D at Compounding Levels
and Prioritize Key Areas of AI R&D
Research is the linchpin of America’s global leadership in AI. However, current federal
funding is not adequate to meet the growth of the field, let alone support its continued
expansion.7 The Trump Administration’s proposed budget for non-defense AI R&D in
Fiscal Year 2021 was $1.5 billion,8 a growth from around $1 billion spent in Fiscal Year
2020.9 Further building on this investment, Congress included the National AI Initiativep438

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Act of 2020 in the National Defense Authorization Act for Fiscal Year 2021, which createsa structure for a more strategic approach to harnessing AI and includes authorization for
additional investments in AI at the NSF, Department of Energy (DoE), National Instituteof Standards and Technology (NIST), and the National Oceanic and Atmospheric
Administration (NOAA).10

National AI Initiative Act of 2020
• Created an executive branch entity
within the Office of Science andTechnology Policy to coordinate
federal support for AI research anddevelopment, education and training,
research infrastructure, and international
engagement in order to achieve national
priorities as defined in a regularly
updated strategic plan for AI.11• Included provisions that establisheda National AI Research Resourcetask force, formalized the National AI
Research Institute effort, and authorized
funding for AI research at the National
Science Foundation, the National
Institute of Science and Technology,
the Department of Energy, and the
National Oceanic and Atmospheric
Administration.

The government should build onthese first moves and invest in
AI R&D at compounding levels.
Federal research funding holds thepower to change the trends that are
degrading the ability of the U.S. tocontinue to lead in AI, namely that
academic research is weakening asa result of brain drain of professors
and diversion of graduate students
to industry, the domestic AI talentpipeline is not keeping up withgovernment and industry needs,
and national technical and ethicalstandards for development are
lagging behind the technology.12
Furthermore, federal support canspur the application of AI to otherfields of science and engineering,which holds the potential forsignificant returns on investment.

Through sustained investments, federal support can serve to holistically strengthen
AI R&D by embracing a range of initiatives—to include support for basic and appliedresearch, shared research infrastructure, a network of AI R&D institutes, fellowships, andchallenge competitions. Flowing investments through a diversity of agencies will createa vibrant fabric of funding, both mission-oriented and investigator-driven, that balances
sustainment of evolutionary progress with big bets on revolutionary breakthroughs andsupports innovation in academia and the private sector.
Actions for Congress:• Double annual non-defense AI R&D funding to reach $32 billion by Fiscal Year2026.
o Congress should support compounding levels of federal funding for AI R&D,
doubling investments annually from the baseline of $1 billion in Fiscal Year 2020.
o Investments should be made across federal R&D funding agencies, notably thep439

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proposed National Technology Foundation, DoE, NSF, the National Institutes of
Health (NIH), NIST, and the National Aeronautics and Space Administration (NASA).
o Significant funds should be appropriated to expand fellowship and scholarshipprograms.13 Augmented funding through these vehicles would support additional
undergraduate and graduate students to pursue AI-related fields of study, helping
to strengthen academia, grow the domestic talent pipeline, and provide pathways
into government for technical talent. Similarly, career/faculty fellowship vehicles
supporting researchers in academia would serve to stem the flow of researchers toindustry and invest in top talent to pursue big ideas.
• Commit to spending at least 1% of GDP on federally funded R&D.
o To maintain a strong base of innovation across S&T, Congress should pair AIspecific investments with an overall federal commitment to annually fund R&D
at a level that reaches at least 1% of gross domestic product (GDP). This could beaccomplished through steady growth over the next five years, at a rate of about $15
billion per year.
Actions for the Office of Science and Technology Policy:• Balance Interagency AI R&D Investment Portfolios.
o The National AI Initiative should coordinate federal investments in AI R&D toward
annual doubling benchmarks, through amplified research funding, fellowships, andestablishment of research infrastructure.
o The National AI Initiative should ensure that growth in funding occurs acrossmultiple agencies and embodies a portfolio approach that leverages a diverse
set of mechanisms, focused on a range of outcomes—advancement of basic
science, solving specific challenge problems, and facilitating commercialization of
breakthroughs.
• Prioritize Critical AI Research Areas.
o Research investments should prioritize areas critical to advance AI technology thatwill underpin future national security and economic growth but may not receive
significant private-sector investment, such as:■■

Novel machine learning (ML) directions. To further non-traditional approaches
to supervised ML in an unsupervised or semi-supervised manner as well as
the transfer of learning from one task or domain to another.14 Other directions
include exploration of hybrid AI techniques that combine data-centric AI withdifferent forms of model-based representations and inference methodologies
to capitalize on complementary strengths.15
Test and evaluation, verification and validation (TEVV) of AI systems. To
develop a better understanding of how to conduct TEVV and build checks
and balances into the entire life cycle of an AI system,16 including improved
methods to explore, predict, and control individual AI system behavior so thatwhen AI systems are composed into systems-of-systems their interaction does
not lead to unexpected negative outcomes. Understand context-specificity
and degradation of performance in new and unseen environments.

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Robust and resilient ML. To cultivate more robust methods that can overcome
adverse conditions and advance approaches that enable assessment of types
and levels of vulnerability and immunity. Addressing challenges of multiple
classes of adversarial ML attacks. Includes research on fairness.
Complex multi-agent scenarios. To advance the understanding of interacting
cohorts of AI systems, including research into adversarial vulnerabilities andmitigations, along with the application of game theory to varied and complex
scenarios.
AI for modeling, simulation, and design. To progress the use of rich simulations
as a source of synthetic data and scenarios for training and testing AI systems,
and to use AI to solve complex analytical problems and serve as a generative
design engine in scientific discovery and engineering.
Advanced scene understanding. To evolve perceptual models to incorporate
multi-source and multi-modal information to support enhanced actionableawareness and insight across a range of complex, dynamic environments andscenarios.
Preservation of personal privacy. To assure personal privacy of individuals is
protected in the acquisition and use of data for AI system development andoperation through advancements in anonymity techniques and privacypreserving technologies such as homomorphic encryption, differential privacy
techniques, and multi-party federated learning.
AI system risk assessment. Advance capabilities to support risk assessment
including standard methods and metrics for evaluating degrees of auditability,
traceability, interpretability, explainability, and reliability.
Enhanced human-AI interaction and teaming. To advance the understandingof human-AI teaming, including human-AI complementarity, methods foraugmenting human reasoning abilities, and fluid handoffs in mixed-initiative
systems. Also includes bolstering AI technologies to better perceive andunderstand human intention and communications, including comprehensionof spoken speech, written text, and gestures. Advances in human-machineteaming will enable human interactions with AI-enabled systems to move fromthe current model of interaction where the human is the “operator” to a futurein which humans have a “teammate” relationship with machines.
Autonomous AI systems. To advance a system’s ability to accomplish goals
independently, or with minimal supervision, from human operators inenvironments that are complex and unpredictable.
Toward more general AI. Research persistent challenging problems andmysteries of human intellect, including ability to learn efficiently in an
unsupervised manner; amass and apply commonsense knowledge; build
causal models that provide robust explanations; exercise self-awareness,
assessment, and control; and generalize and leverage knowledge learned
about specific tasks to become proficient at another task.

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Component 3: Triple the Number of National AI Research Institutes
NSF awarded grants for the first National AI Research Institutes in 2020, supporting
seven university-based, multi-institution consortia organized around fundamental andapplied areas of AI research—topics for which were determined through coordination
with interagency and community stakeholders.17 NSF plans to fund a second round of
institutes in 2021, coordinating support not only with interagency partners but also withprivate-sector stakeholders to launch eight additional institutes.18 Congress took steps to
support the initiative through the National AI Initiative Act of 2020, which formalizes theeffort, provides all agencies the authority to financially support formation of a National
AI Research Institute, and directs NSF to bring together the institutes as an “Artificial
Intelligence Leadership Network.”19
Expansion of this initiative would create a nationwide network of AI innovation that supportsa breadth of AI research initiatives—advancing basic AI science, solving domain-specificchallenges, and applying AI to other fields of science and engineering. Their establishment
would increase training opportunities for students and research opportunities for academic
faculty, national lab researchers, and non-profit research organizations; help grow the fieldoutside of leading private universities and regional technology hubs; and strategically steer
research toward areas that could advance the science of AI and applications that serve
broader society and the national interest.
Action for Congress:• Direct and appropriate funds to expand the network of AI institutes.
o Congress should direct and appropriate funds to NSF to expand the network of AI
institutes three-fold over the course of the next three years—ideally resulting in a
broad diversity of participating institutions, regions, and research concentrations.
o This investment would encompass 30 additional institutes, totaling $600 million tosustain the additional institutes for the five-year duration of the grant awards. This
would entail appropriations of $200 million in Fiscal Year 2022, Fiscal Year 2023,
and Fiscal Year 2024.
Action for the Office of Science and Technology Policy:• Integrate the network of institutes with national AI R&D infrastructure investments.
o The National AI Initiative should ensure alignment of the National AI ResearchInstitutes with strategic research priorities and integration with the national networkof open AI test beds and the National AI Research Resource (see discussion of a
National AI Research Infrastructure below).

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Component 4: Invest in Talent that Will Transform the Field
Top talent in AI is a scarce commodity, and investing in talent holds the potential to not
only unlock breakthroughs in the science and application of AI but also to attract andretain top talent in the United States.20 Similarly, investing in research initiatives conductedby integrated, multidisciplinary teams is a proven mechanism to prompt breakthroughs,
address complex problems, and challenge the status quo.21
The launch of an AI Innovator Award and complementary team-based AI award would
strengthen the ability of federal AI research funding to push the boundaries of the field,
providing a mechanism to complement ongoing investments in incremental progress withbets on revolutionary breakthroughs.
Actions for Congress:• Direct and fund establishment of an AI Innovator Award.
o Congress should direct and fund NSF to establish an AI Innovator Award, looselymodeled on the NIH Pioneer Award22 and the Howard Hughes Medical Institute
Investigator Program23 to create a mechanism that provides top researchers theflexibility to pursue big ideas without prescribed outcomes over the course of a fiveyear, renewable grant award.
■

■

■

■

Totaling around $5.5 million per awardee for the five-year term, the awards
would cover the full salary and benefits of the researchers at their respective
institutions as well as a research budget that would support equipment andstaff.24
At its height, the program would support a maximum of 100 researchers at
a time, reaching an annual funding level of around $125 million for researchsupport, with additional funds available for major equipment support.
Eligible researchers would be those at any career stage based at U.S.
universities or research institutions who commit to spending 75% of their timeon research.25
Attention should be paid by the selection committee to the need for diversity
among awardees in terms of gender, race, age, location, and primary focus areaof study, as well as on the communication and leadership skills of applicants.

o Congress should authorize NSF to:
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Fund an external organization to administer the program.26
Annually select 10 to 20 recipients for five-year, renewable terms and conduct
selection through a small, rotating panel of AI experts.27
Ensure selection of innovative candidates through an advocacy model process
in which candidates are ranked in accordance with the maximum scoresprovided by reviewers, thereby placing priority on their upside potential.28
Hold an annual meeting in which all awardees would share their work,
providing a venue for meaningful feedback between review cycles and helping
build a community of innovation among the top U.S.-based minds in AI.

A C C E L E R AT I N G A I I N N O VAT I O No Congress should require NSF to assess the program after seven years of operation
to determine whether the program should continue to expand or operate at a lower
number of awards and to evaluate the impact of the funding level and award termon the research conducted by participants.
• Direct and fund establishment of a team-based AI research award.
o Congress should direct and fund NSF to work with the same external organization
as the AI Innovator Award to create a team-based award to support bold,interdisciplinary research initiatives that apply AI to solve complex challenge
problems or pursue use-inspired basic research efforts.
■

The program should begin with an annual budget of $50 million, growing to asustained annual budget of $250 million by its fifth year of operation.

o Congress should authorize the NSF to:■■Fund an external organization to administer the program.
Select five to 10 teams annually for non-renewable, five-year terms, awarding$4 million to $10 million per year for the five-year term of the award.29

Recommendation: Expand Access to AI Resources through a National AI Research
Infrastructure

Recommendation

If not addressed, the growing divide between “haves” and “have nots” in AI R&D will
degrade the long-term research and training functions performed by U.S. universities,
limit the ability of small businesses to innovate, and exacerbate the lack of diversity in thefield.30 While developments in the past five years have dramatically increased access tobaseline ML tools and cloud-based computation, progress on the cutting edge of many
important AI approaches requires significant amounts of data and computing power,
expensive infrastructure, and substantial hardware and software engineering.
The United States should foster the world’s leading environment for AI innovation throughdemocratized access to AI R&D that supports more equitable growth of the field andexpansion of AI expertise across the country; enables application of AI to a broad rangeof fields of science and engineering, commercial sectors, and public services; and fuels
the next waves of innovation.
Component 1: Launch the National AI Research Resource
Since the explosion of deep learning in 2012 and accompanying growth in use of
specialized hardware for AI computing, there has arisen what some have termed the“compute divide”—a disparity in access between large technology companies and elite
universities and mid- and lower-tier universities to the resources necessary for cuttingedge AI research.31 Availability and type of compute resources have been found to levy
“outsized” influence in the direction of research pursued by researchers, as has theascendency of the well-equipped firms in shifting the overall direction of AI research
toward applied, “narrow AI” efforts.32
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To bridge the compute divide, the Federal Government should establish a National AI
Research Resource (NAIRR) to provide verified researchers and students with access tocompute resources, co-located with AI-ready government and non-government data sets,
educational tools, and user support.33 This infrastructure should leverage public-private
partnerships and cutting-edge private-sector technology and build on existing government
efforts34 —avoiding high startup costs of a government-run data center. Congress has taken
the first step in the Fiscal Year 2021 National Defense Authorization Act, implementing acomponent of the Commission’s prior recommendation to create a task force to develop
a roadmap for a NAIRR.35 The result of this effort will be due to Congress 18 months after
appointment of task force members.
Action for Congress:• Authorize and appropriate $30 million for implementation of the NAIRR roadmap.
o Congress should authorize and appropriate funds to immediately implement theroadmap developed by the NAIRR task force.
■■

The resource should be sustained at an initial level of $30 million annually,
amplified by contributions from private-sector partners, and scaled as it
matures and gains users.
Funding would support staffing of the program and the cloud resources,augmented through public-private partnerships. Staff would be responsible
for maintaining and improving the architecture solution, curating data sets,building interfaces and tools, and providing support to researchers.

Component 2: Create a Network of National AI Testbeds to Serve the Academic andIndustry Research Communities
Sponsored through various federal agencies, this network of national AI testbeds would
provide real-world, domain-specific resources open to the academic, business, andgovernment research communities to drive basic and applied research to address complex
problems and develop robust, usable AI systems ripe for commercialization (for example,
a self-driving vehicle test range, an instrumented humanitarian aid and disaster relief test
site, or an instrumented home environment). Such resources would help establish andmaintain benchmarking standards that enable measurable research progress throughcomparable approaches and reproducibility testing.
Testbeds should support experimentation with both novel software and hardware, equippedwith rich simulation capabilities to model the physical world. Supported by simulated, live,
and blended environments, these platforms would support research and experimentationthat tackles open-ended, real-world problems. Furthermore, they should be architected
to collect valuable data that could be made accessible to the community for training andevaluation, providing additional fuel for progress.

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Action for the Office of Science and Technology Policy:• Coordinate agency investments in AI R&D testbed facilities.
o The National AI Initiative should coordinate agency investments in AI testbed
facilities through the annual budget process, aligning investments withresearch priorities issued in the initiative’s strategic plan. Attention should focus
on modernizing existing resources to support data-driven and AI-enabledtechnologies.36
Action for Federal Agencies:• Invest in domain-specific AI R&D testbeds through upgraded or purpose-built
facilities.
o Investment in the suite of national AI testbeds should be made across multiple
federal agencies, facilitating creation of domain-specific resources open to thebroader research community. Focus areas of each testbed should be aligned withpriority AI research areas and in support of existing federal AI investments.
o Testbeds should be set up as “user facilities” that maintain a hybrid approach ofawarding grants for use and charging fees to those not selected for grant funding.
User fees would assist in maintaining the testbeds and supplementing the amountof funding available for grants.
Action for Congress:• Support agency funding requests for establishment of AI R&D testbeds.
Component 3: Invest in Large-Scale, Open Training Data
Data is critical currency for today’s popular AI approaches. Promising work in the realmof low-shot learning, semi-supervised learning, and learning from synthetic data provides
glimpses of a future in which performance of an AI system is not directly tied to big data,
and the Federal Government should continue to prioritize funding for research in theseareas. However, balancing these bets on the future with investments in resources to further
U.S. leadership in the current leading AI approaches would strengthen the foundation ofboth current and future AI-based technology and applications.
Building AI systems and solutions for new domains and application areas relies on availabilityof specialized data that have been cleaned and organized for use. Federal support forwell-designed, publicly-available data sets and provision of AI-ready government data
sets would help drive research progress in AI and its application to other fields of study.
Currently, a sizable amount of government data that is legal to share with trusted nongovernment researchers is not being shared due to a lack of confidence in cybersecurity
and privacy-protecting technologies and a lack of willingness to accept risk.

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Responsibly creating pipelines for the curation, hosting, and maintenance of complex data
sets would set the foundation for future AI capabilities, help strategically steer the researchcommunity toward issues in the public interest, and advance technology around data set
lifecycle maintenance.
These data investments could be further augmented by and created in support of thedomain testbeds recommended above and hosted through the NAIRR. This integration
could foster creation of data sets to support benchmarks within the testbeds as well as
generate rich data from testing that could be provided back out to serve the researchcommunity. Access to resources should be granted to researchers with verified research
efforts and governed by appropriate compliance controls based on the type of data andmetadata contained in the data set.
Actions for the Executive Branch:• Issue a common policy and set of best practices.
o Leveraging the work of NIST,37 the U.S. Chief Data Officer should issue a common
policy and set of best practices to support release of AI-ready government data to
the public and work with industry and academia to adopt compatible policies andbest practices for reciprocal sharing and documentation.
• Provide incentives to industry and academia to make available select data sets.
o The U.S. Chief Data Officer should develop incentives for industry and academia tomake available select data sets on the NAIRR that would be managed and accessedalongside government-owned data sets.
• Support NSF-funded cybersecurity and privacy researchers to make government
data accessible for research purposes.
o The National AI Initiative should coordinate NSF-funded cybersecurity and privacy
researchers to undertake rotational assignments at federal agencies38 and work
closely with agency personnel and data stewards to responsibly unlock access tomore of the government’s data holdings for the purpose of stimulating AI researchand innovation.
o Researchers would apply promising methodologies for protecting data and privacy
in a controlled manner, providing a proving ground for new approaches andobjective evidence to justify evolving data-sharing policies and practices. This
could include creating secure environments for verified researchers to access moresensitive government-held data.
Actions for Congress:• Unlock public data for AI R&D.
o Congress should fund teams of data engineers and data scientists organizedthrough the U.S. Digital Service to unlock public data currently held by thegovernment for use by the AI research community.39

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A C C E L E R AT I N G A I I N N O VAT I O No These teams would prioritize, clean, and curate non-sensitive public data sets
to make them AI-ready and structure enduring processes to capture, clean, andregularly update data that would be hosted on a platform such as NAIRR, accessible
by verified U.S.-based researchers.
• Fund an AI data program at the Department of Energy.
o Congress should appropriate $25 million40 per year for the next five years to DoE
to administer an AI data program that would create exemplar, complex data sets
and maintain them as living, regularly updated resources. These could include
specialized data sets in physical, biological, earth, and engineering sciences, as well
as social sciences.41
o The program should be coordinated through the National AI Initiative to ensure data
sets created steer the research community in desired directions.
o Congress should direct DoE to work closely with NIST to develop standards for the
data—to include standards for documentation, data modeling, data engineering,and data formats—as well as to advance the methods and tools necessary tosupport the data lifecycle.
Component 4: Sponsor an Open Knowledge Network
Open knowledge networks (or repositories) with massive amounts of world knowledge
could fuel the next wave of AI exploration, driving innovations from scientific research tothe commercial sector. Today, only the biggest tech companies have the resources todevelop significant knowledge graphs and networks.
Various federal agencies have invested in specialized, domain-specific knowledge
networks that could provide a starting point for an open knowledge network.42 Beginningwith a push to federate and map together existing specialized knowledge networks andgovernment data platforms, and then building in real-world knowledge and context, thegovernment could sponsor an Open Knowledge Network that would serve verified U.S.based companies and researchers of all backgrounds to use world knowledge to develop
AI systems that operate effectively and efficiently. This type of resource, particularly ifpaired with the complementary research infrastructure above, could unlock frontiers oftechnology yet unexplored.
Action for the Office of Science and Technology Policy:• Hold an innovation sprint to build an open knowledge network roadmap.
o Leveraging prior work undertaken through the Networking and Information
Technology Research and Development (NITRD) program Big Data Interagency
Working Group,43 the Office of Science and Technology Policy should hold an
innovation sprint to build a roadmap to establish an open knowledge network in aphased manner.

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Action for Congress:• Direct and fund implementation and management of the open knowledge network.
o Congress should direct and fund the NSF to implement and manage the open
knowledge network, appropriating $25 million per year for the next five years
and encouraging NSF to leverage partnerships with industry stakeholders where
possible.44
Recommendation

Recommendation: Leverage Both Sides of the Public-Private Partnership
U.S. companies are at the forefront of AI R&D, and their investments benefit consumers
globally through the rapid development and adoption of AI-enabled products. But the
impact of AI-enabled products on U.S. society and national security has largely come as
an afterthought. The speed of technology development by the private sector has vastly
outpaced federal policies and regulations. To address these challenges, the public andprivate sector must share responsibility for the safety, security, and well-being of Americans.
The following recommendations would make the government a better partner for industry,broaden the benefits of strategic emerging technologies like AI through regional innovation
clusters, and expand opportunities to access AI research and education through privatesector philanthropy.
Component 1: Create Markets for AI and Other Strategic Technologies
The government’s buying power cannot compete with a global consumer market, but
it can influence investment decisions in technologies essential to overall U.S. technicalleadership.45 Many potential public-sector applications of AI, such as education and labor,
fall under agencies with limited R&D budgets. As the government increases investment inbasic research, it must also fully leverage its purchasing power to support AI and otherstrategic technologies.46 The scale of government funding can influence the research
priorities and viability of early-stage startups, which often succeed or fail in the first year;
and, if leveraged collectively, it can draw private-sector resources toward areas of strategic
priority. This makes investors and technology companies important partners for AI R&D
that can build future defense and national security capabilities.
Yet the government remains a difficult customer—especially for small and medium-sizedbusinesses—because of its complex contracting process and unique requirement. Making
the U.S. government a more compelling customer and effective buyer of commercial
technology will help drive technology development in the commercial sector that is in thenational interest. It will also assist the government in almost every aspect of its mission,from providing basic public services to driving economic policy and protecting national
security.

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Actions for the General Services Administration (GSA):• Promote the application of AI across the U.S. Government.
o In fulfilling its mandate to facilitate the adoption of AI technologies in the Federal
Government,47 the AI Center of Excellence (AI CoE) should look first to readily
available commercial off-the-shelf (COTS) technology that can be tailored forgovernment use.
■■

AI CoE should work with Federal technical leadership,48 including the U.S.
Chief Technology Officer, Chief Information Officer Council, and the National
AI Initiative,49 to identify government needs and opportunities and expedite theadoption of commercial AI applications across federal agencies.
The AI CoE should leverage existing digital governance efforts across the
Executive Branch, including GSA’s 18F and the U.S. Digital Service, andtechnical talent exchange programs, including GSA’s Presidential Innovation
Fellowship, to bring sufficient technical expertise and commercial proficiency
to this effort.50• Communicate federal AI capability priorities to the private sector.
o The AI CoE should add federal procurement priorities and agency capability needs
to its publicly available website, which contains information regarding programs,
pilots, and other initiatives.51
Actions for the U.S. Small Business Administration:• Publish a digital technology “playbook” for small businesses.
o A playbook for small businesses should outline paths for companies interested indoing business with the U.S. government and explain in a single place52 how tonavigate challenges like obtaining access cards to government facilities. Such a
resource would make the acquisitions process more transparent and reduce theneed for companies to hire outside help.
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The playbook should be developed and reviewed by personnel with technicaland commercial proficiency, for example Presidential Innovation Fellows or
staff from the U.S. Digital Service, and written in language that technology
startups with no prior government experience can understand.
The playbook should be aggressively publicized to increase its visibility.

• Bridge public and private investment through the Small Business Innovation
Research (SBIR) Program.53
o Support the efforts of participating federal agencies to modernize SBIR to more
effectively develop and deploy AI solutions and encourage broader participation ofAmerican technology startup and small-business companies.
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Expand pilot programs that offer supplemental funding to bridge the gap
between current SBIR/Small Business Technology Transfer (STTR) Phase IIawards and Phase III scaling efforts.54

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Expand pilot programs that offer larger funding amounts55 and privatesector matching opportunities to support higher technology readiness levels
common in DoD SBIR contracts.56

o Update SBIR Policy Directive to allow programs to require matching private-sector
funds as early as Phase II.57
Actions for Department of Defense and Intelligence Community:• Adopt a “hoteling” model to allow small- and medium-sized technology
companies to access classified facilities on a flexible basis.
o The Digital Ecosystem described in Chapter 2 of this report would establish
prototypical platform environments for contributors and users, including cleared
personnel from AI companies. Flexible access to classified spaces would speed
development cycles and help companies more regularly engage with current or
potential customers within the national security enterprise, leading to more tailoredand effective solutions delivered more quickly.
• Simplify the contracting process to attract non-traditional vendors.
o Amend the Defense Federal Acquisition Regulation to allow commercial
performance to be considered more widely in the contracting process. The U.S.
government can benefit from broader adoption of best-in-class commercial AI
software.
o Allow for pilot use of commercially available digital application tools and accessportals for SBIR and other non-traditional contracting vehicles.58• Commit to growing the national security innovation base.
o DoD should set a target of increasing its contracts with early-stage technology
firms by four times over the five-year Future Years Defense Program.59 This will also
require growing the budgets of successful but nascent innovation initiatives such as
the Defense Innovation Unit.60■

To this point, DoD has focused on a large number of small bets without
following up with larger later-stage investments. Larger contracts for laterstage companies would help scale validated solutions that meet militaryrequirements.

o The Under Secretary of Defense for Acquisition and Sustainment and the Service
Acquisition Executives should encourage Acquisition Category programs of all sizes
to solicit bids from at least one non-traditional contractor per program.
• Strengthen return on SBIR investments.
o Review, modernize, and streamline SBIR processes to encourage broader
participation of American technology startup and small-business companies.61
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Program officers should clearly communicate pathways to transition, including
milestone criteria and dollar amounts, to SBIR awardees so that they can planand resource accordingly.
Explicitly allow SBIR contracts to leverage any “color of money” as matchingfunds up to the amount of SBIR funding.

A C C E L E R AT I N G A I I N N O VAT I O No Enable successful prototypes to scale through sufficient funding, early accessto customers and operators, and better due diligence on the commercialization
prospects of a company.62■■

Military Service and Office of the Secretary of Defense (OSD) SBIR programs
should allocate a portion of SBIR funding for scaling successful SBIR projects
through Phase II enhancements.63
Program Offices should provide program dollars alongside matching SBIR
funds to increase the likelihood of transition.

o Continue efforts to align SBIR program with Department technology priorities tofocus investments on subsets of key technologies on which private-sector R&D canhelp advance.64■

The Office of the Under Secretary of Defense for Research and Engineering
should introduce a special solicitation on AI that invites solutions across adiversity of AI approaches65 and a range of technology readiness levels.66

Component 2: Form a Network of Regional Innovation Clusters Focused on Strategic
Emerging Technologies
Competition is critical to a vibrant national security innovation base.67 If a strategic industry
lacks competition, one wrong bet by an incumbent can place the nation’s technologicalleadership in jeopardy.68 The U.S. government should create an environment in which
innovative startups are able to disrupt inefficient or outdated ways of doing business andgrow into industry leaders themselves. The right mix of policies and incentives can help
firms overcome mounting barriers to entry at the cutting edge of emerging technologies like
AI.69 This approach will promote innovation in industries that are essential to U.S. leadershipin AI and the nation’s economic and technological competitiveness more broadly.70
As the Commission noted in its 2019 Interim Report, the clustering of technology firms
in regions like Silicon Valley yields a more dynamic and globally competitive industry byexpediting knowledge sharing and sharpening domestic rivalry.71 However, this trend has
benefited some regions and demographics more than others.72 To spur regional innovation
across a broader swath of the nation, the U.S. government should support the growth oftechnology clusters in regions with latent innovation potential. Broader in mission and scopethan existing models within the U.S. government, such an initiative would democratize
access to federal R&D resources so that small firms could compete in industries with highbarriers to entry like AI. By facilitating the exchange of technology and talent between the
public and private sectors, the U.S. government would also be well positioned to establish
new contracts and intellectual property sharing agreements for commercial technologies
that are critical to U.S. national security.

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Actions for Congress:• Establish an interagency program office responsible for coordinating a network ofregional innovation clusters focused on R&D and commercialization of strategic
emerging technologies.
o The program office should be hosted by the Department of Commerce at NIST andstaffed by representatives from U.S. departments and agencies with experiencein and missions related to strategic emerging technologies.73 The program officeshould also draw on expertise from the private sector and academia through talentexchange programs and external advisory arrangements.
o Congress should authorize $5 million for the creation of the program office and task
it with designating regional innovation clusters in qualified locations throughout
the United States via a competitive process, as described below in detail. As a firststep, the program office should solicit bids for financial assistance from applicants
focused on the R&D and commercialization of strategic emerging technologies. In
assessing bids, the program office should consider the following criteria:■■■

Location. Clusters should be equitably distributed throughout the UnitedStates in regions with latent innovation potential, taking into account factors
such as proximity to federal R&D facilities, the level of support from state andlocal governments, the presence of and value proposition for leading firms andresearch institutions, and the size and education level of the local workforce.74
Subject area. Clusters should be organized around the research, development,
and commercialization of strategic emerging technologies that are criticalto U.S. national competitiveness. Of particular interest are technologies thatenable advances in adjacent sectors and whose domestic production would
directly benefit U.S. national security, such as microelectronics.75
Economic feasibility. To maximize the impact of federal resources and ensureself-sustainability of the clusters, financial assistance should only be awarded
to applicants that demonstrate the existence of a nascent cluster in theirregion.76

o The program office should establish Technology Research Centers (TRCs)
for each cluster to facilitate collaboration between participants. By forming
sustained partnerships with anchor institutions, each TRC should strive toadvance the research, development, and commercialization of strategic emerging
technologies.77
■

■

■

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453

Leverage talent. TRCs should host researchers on temporary assignments from
U.S. departments and agencies, establish talent exchanges with local firms
and research institutions, and fund multi-year, postdoctoral fellowships for thecommercialization of research.78
Encourage technology transfer. TRCs should host program managers from U.S.
departments and agencies responsible for transitioning basic research into
commercially viable technologies, identifying national security use cases andend users within the U.S. government, and initiating new government contracts
for those products.
Generate intellectual property. TRCs should establish intellectual property–
sharing agreements with cluster participants to encourage government
adoption of commercial technologies. When appropriate, research should beA C C E L E R AT I N G A I I N N O VAT I O N

published in the open-source domain to encourage advances in the broader
science and technology community.
■

Bring government resources to bear. TRCs should facilitate participants’
access to federal computing resources, curated government data sets, testing
infrastructure and ranges, and other R&D facilities at low cost.79

o The program office should play a high-level coordination role that includes
supervising the operation of TRCs, facilitating R&D collaboration between clusters,
and promoting the commercialization of technologies with national security use
cases.
• Enact a package of provisions that incentivizes industry and academia toparticipate in clusters.
o Provisions should include tax incentives to locate near the cluster, competitive
research grants, loan guarantees, and seed funding. A complementary approachshould be taken by state and local governments. These policies could be modeledon Opportunity Zones, which have stimulated investment in regional economies.80■

Investment tax credits. To compete with incentives offered by foreign countries,
Congress should establish investment tax credits for firms participatingin regional innovation clusters. While the details of these tax credits willvary by sector, one example is the investment tax credit for semiconductor
manufacturing facilities and equipment proposed in Chapter 13 of this report.

• Provide funding to each cluster for at least five years, with matching investmentsfrom public- and private-sector partners.
o Within one year, the program office should request from Congress the necessary
funding for the designation of up to 10 clusters. This funding should be matchedat least 1:1 by investment from private companies, state and local governments,and federal agencies, with a target of each cluster initially receiving a total of $50
million annually. This annual amount should increase as demand and capacity ateach cluster expands over time.81 These funds would be used to operate the TRCs,
maintain R&D facilities, issue research grants, and seed startups.
Component 3: Establish a Private Sector–Led Competitiveness Consortium
The private sector shares responsibility with the government to strengthen the foundationsof the R&D ecosystem that underpins breakthroughs they will commercialize and thetraining pipeline needed to meet their increasing demand for technical talent.
Companies are already struggling to find these qualified applicants for technical roles, with
one estimate showing more than 400,000 open computing jobs nationwide.82 Furthermore,
as described above, researchers in academia who will undertake the high-risk, high-gain
research that will push the frontiers of the field are finding themselves locked out from thecomputing and data resources needed to fuel this work. How well the nation addresses
this looming challenge has widespread implications for the economy, society, and U.S.
global competitiveness.

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Chapter 10 of this report describes in detail recommendations to revamp the U.S.
educational system to equip Americans for the jobs of the future, and this chapter details
the extensive investments the Federal Government should make in AI R&D. However,corporations should also consider their responsibility to prepare citizens for the future they
are inventing and maintain the strong foundation of national innovation from which they
benefit. Toward that end, many firms are already having a positive impact beyond theirbottom lines through corporate social responsibility efforts. STEM education programs
and job training feature prominently in the charitable-giving arms of leading tech firms.83
Yet the scale of the challenge is too broad for individual firms to address in insolation,
despite their generosity.
Actions for the Private Sector:• Donate $1 billion over five years.
o Providing every American an opportunity to increase their technical proficiency
requires bold action from government, academia, and industry to coordinate,prioritize, and scale programs that broaden AI research opportunities andinstill digital proficiency.84 For the private sector to meet this call to action, theCommission calls upon industry to donate $1 billion over the next five years to
support AI education and upskilling and provide data and compute resources todemocratize and fuel best-in-class AI research efforts.
o These funds would lay the foundation for broader digital transformation andeconomic empowerment. Government officials should publicly highlight the impactof this effort and the role of the firms contributing to it.
o Similar to the Partnership on AI’s work coordinating development of bestpractices across AI firms,85 this effort should be managed by an independent
non-profit organization that can link and scale firms’ efforts to build digital skills
and democratize AI research. The U.S. Digital Service Academy proposed by theCommission could also contribute expertise, volunteers, curriculum development,
and other in-kind support.86• Expand research exchanges between industry and academia.
o Leading technology firms should invest in or expand exchange programs designed
to combine top academic talent with world-class private-sector computing
resources. Rotational exchanges of this type would both democratize computing
access for researchers and simultaneously shed light on new pathways for nextgeneration AI products that could be commercialized by industry.
Action for the U.S. Bureau of Labor Statistics:• Standardize and report data on digital skills in the job market.
o The U.S. Bureau of Labor Statistics should lead an effort in coordination with otheragencies such as the Department of Education to collect and regularly updatestatistics on the digital proficiency of demographic groups and regions, with entries
describing specific digital skills needed by firms with job openings. This will enablep455

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academic institutions, firms, and other organizations to prioritize their efforts foreducating, reskilling, upskilling, and digital transformation.
Recommendation: Tackle Some of Humanity’s Biggest Challenges

Recommendation

If the investments detailed above are implemented, they will set the conditions to harness
AI to tackle some of the biggest challenges in science, society, and national security.
Examples of promising initiatives that could improve societal well-being and advancescientific frontiers include, but are not limited to:• Enable long-term quality of life. AI technology that can help the elderly live
independently longer, assisting in managing health and daily tasks and improving thequality of life. This can include application of AI to biomedicine to address acute andchronic illnesses and enhance healthy aging.
• Revolutionize education and lifelong learning. AI tools that personalize education,
training, and retraining at appropriate challenge levels and intuitively evaluate
development to optimize standard curricula to promote individual learning success.
• Transform energy management. Smart infrastructure for cities that can effectively
respond to surges in energy demand and emergencies (both man-made and natural
disasters).
• Effectively predict, model, prepare for, and respond to disasters. Accurate, near–realtime weather, earthquake, and fire line detection and prediction of escalation to aid
in emergency response and planning for optimized deployment of limited resources.
Autonomous robots for search, rescue, and cleanup in the wake of natural or man-madedisaster, providing force-multiplying support to first responders and hazardous materials
professionals.
Action for the Office of Science and Technology Policy:• Direct the National AI Initiative to align federal investments in AI R&D to tackle
significant scientific, technological, and societal challenges.
o The National AI Initiative should identify and oversee realization of opportunities toharness federal R&D investments to take on audacious scientific and technologicalchallenges that could lead to breakthroughs that benefit society and national
security.87
o Prioritization of these efforts should be coordinated with the national security
research community and informed by the Technology Competitiveness Council
proposed by the Commission88 to define areas of research where the application of
AI could contribute to progress that provides strategic advantages.

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Blueprint for Action: Chapter 11 - Endnotes
The National Science Foundation: A Brief History, National Science Foundation (July 15, 1994),
https://www.nsf.gov/about/history/nsf50/nsf8816.jsp#chapter3 (“In fiscal year 1958, the year before
Sputnik, the Foundation’s appropriation had leveled at $40 million. In fiscal 1959, it more than tripled
at $134 million, and by 1968 the Foundation budget stood at nearly $500 million.”).

1

2
12 Irreplaceable Innovations Made Possible by NSF, National Science Foundation (last accessed
Feb. 11, 2021), https://www.nsf.gov/news/special_reports/btyb/innovation.jsp. A recent report
produced by Computer Science and Telecommunications Board of the National Academies of
Sciences, Engineering, and Medicine traces the interplay between fundamental research ininformation technology (IT) in academia and industry and its effects on capabilities of IT and nonIT sectors. For an illustration of the how the research funded by NSF and others has influenced
the technologies that have transformed our everyday lives, see Information Technology Innovation:
Resurgence, Confluence, and Continuing Impact, National Academies of Sciences, Engineering, andMedicine at 14 (2020), https://doi.org/10.17226/25961.

We recommend an estimated operating budget of $20 billion per year. For comparison, NSF has anannual budget of $8.5 billion (FY 2021), while five U.S. technology firms—Alphabet, IBM, Facebook,
Microsoft, and Amazon—spent an estimated $80.5 billion on AI R&D alone in 2018. See About the
National Science Foundation, National Science Foundation (last accessed Feb. 11, 2021), https://www.nsf.gov/about/; Martijn Rasser, et al., The American AI Century: A Blueprint for Action, CNAS
(Dec. 17, 2019), https://www.cnas.org/publications/reports/the-american-ai-century-a-blueprint-foraction.

3

As argued by Donald Stokes in 1997, research should be conceived not as a dichotomy
between basic and applied research, but on a quadrant along the axes of “quest for fundamental
understanding” and “considerations of use.” Research in the upper-right quadrant is defined as
use-inspired basic research—research that advances fundamental knowledge but is driven by aclear purpose. Stokes calls this “Pasteur’s quadrant” after the work of Louis Pasteur, whose research
pushed scientific boundaries and had practical applications. See Cherie Winner, Pasteur’s Quadrant,
Washington State Magazine (2009), https://magazine.wsu.edu/web-extra/pasteurs-quadrant/.

4

See Chapter 16 of this report for additional details on each of these technologies and why theCommission believes they are critical to future U.S. national competitiveness.

5

For additional details on the Commission’s proposed National Technology Strategy and theTechnology Competitiveness Council, see Chapter 9 of this report.

6

For example, NSF, which provides 85% of federal funding for computer science, funded $188 million
in core AI research in 2019 but did not have room in the budget to fund another $178 million worthof highly rated proposals. This was an improvement from 2018, when it funded $165 million but left
$185 million of highly rated work unfunded. Furthermore, NSF (in partnership with the Department ofAgriculture) funded seven National AI Research Institutes in 2020 but was unable to fund the morethan 30 that were judged worthy of supporting. NSF presentation to NSCAI (January 2020).

7

The Networking & Information Technology Research & Development Program Supplement To The
President’s FY2021 Budget, National Science & Technology Council at 4 (Aug. 14, 2020), https://www.
nitrd.gov/pubs/FY2021-NITRD-Supplement.pdf.

8

The Networking & Information Technology Research & Development Program Supplement To The
President’s FY2020 Budget, National Science & Technology Council at 11 (Sept. 2019), https://www.
nitrd.gov/pubs/FY2020-NITRD-Supplement.pdf.

910
Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year
2021, 134 Stat. 3388 (2021).

The legislation tasks an interagency committee overseen by the National AI Initiative Office
to develop every three years a strategic plan for AI that determines and prioritizes areas of AI
R&D requiring Federal Government leadership and investment; supports long-term funding forinterdisciplinary AI research; provides or facilitates the availability of curated, standardized, secure,
representative, aggregate, and privacy-protected data sets for AI R&D; provides or facilitates thenecessary computing, networking, and data facilities for AI R&D; supports and coordinates Federal
education and workforce training activities; and supports and coordinates the network of AI ResearchInstitutes.
11

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12See Interim Report, NSCAI at 24-28 (Nov. 2019), https://www.nscai.gov/previous-reports/; Craig
Willis, Analysis of Current and Future Computer Science Needs via Advertised Faculty Searches for
2019, CRA Bulletin (Dec. 7, 2018), https://cra.org/analysis-of-current-and-future-computer-scienceneeds-via-advertised-faculty-searches-for-2019/.

Expanded funding could go through programs across federal agencies, notably the following.
For NSF: CAREER fellowship; Graduate Research Fellowship Program; CyberCorps: Scholarshipfor Service; Historically Black Colleges and Universities Undergraduate Program; and ResearchTraineeship. For DoE: Early Career Research Program; Computational Science Graduate Fellowship.
For NASA: Space Technology Research Fellowship program. For DoD: DARPA Young FacultyAward; Vannevar Bush Faculty Fellowship; Science, Mathematics, and Research for Transformation
Scholarship for Service Program; National Defense Science and Engineering Graduate Fellowship
Program; and Historically Black Colleges/Universities and Minority-Serving Institutions Researchand Education Program. See Chapter 10’s recommendation for the passage of a National Defense
Education Act.

13

14
Learning techniques such as unsupervised, semi-supervised, self-supervised, one- or zero-shot,and reinforcement learning enable training AI models with less reliance on large data sets of labeled
data, albeit often with lower accuracy than with using supervised learning. See Dr. Bruce Draper,
Learning with Less Labeling, DARPA (last accessed Dec. 19, 2020), https://www.darpa.mil/program/learning-with-less-labeling. Reducing reliance on large amounts of labeled data is important when
supporting applications where data is scarce or labeling data is cost prohibitive. See NSCAI Interim
Report - Beyond Deep Learning, NSCAI at 55 (Nov. 20100), https://www.nscai.gov/wp-content/
uploads/2021/01/NSCAI-Interim-Report-for-Congress_ 201911.pdf.

Hybrid AI approaches include integrating statistical machine learning with other techniques such as
symbolic AI, knowledge representations, game theory, search, and planning. Hybrid AI approaches
are often used in applications of robotics, battle management systems, and resilient systems. NSCAI
Staff Correspondence with DARPA (Feb. 22, 2021).

15This is a particular challenge for long-lived, autonomous AI systems operating over long durationsof time. These systems will likely continuously evolve their mission sets and capabilities, utilizing
dynamic learning, along with in-field, in situ updating. All this requires advancing the disciplineof TEVV to continuously monitor and ensure such a system’s operation remains compliant toperformance requirements over its missional lifetime.

16

17
The topics were Trustworthy AI, Foundations of Machine Learning, AI-Driven Innovation in
Agriculture and the Food System, AI-Augmented Learning, AI for Accelerating Molecular Synthesis
and Manufacturing, and AI for Discovery in Physics. The Department of Agriculture teamed with
NSF to provide funding toward two of the institutes to support AI research on developing the next
generation of and resilience in agriculture. Artificial Intelligence at NSF, NSF (Aug. 26, 2020), https://www.nsf.gov/cise/ai.jsp.
18
Around the topics of Human-AI Interaction and Collaboration, Advances in Optimization, AI andAdvanced Cyberinfrastructure, Advances in AI and Computer and Network Systems, Dynamic
Systems, AI-Augmented Learning, AI to Advance Biology, and AI-Driven Innovation in Agriculture andthe Food System. The institutes are funded at a rate of $4 million per year for five years, totaling $20
million. See Id.
19
Pub. L. 116-283, sec. 5201(b), William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021).
20
A 2019 evaluation of the grants made as a component of the National Institutes of Health (NIH)
high-risk, high-reward program—which include large, longer-term investments in talent through the
NIH Director’s Pioneer Award, NIH Director’s New Innovator Award, and the NIH Director’s Early
Independence Award—found that these awards funded highly productive research compared to
the work funded under traditional NIH research grants and that they result in a higher technologicalimpact. The high-risk, high-reward program was created to accelerate the pace of biomedical,
behavioral, and social science discoveries by supporting creative scientists with highly innovative
research. See Report of the ACD Working Group on High-Risk, High-Reward Research, National
Institutes of Health Advisory Committee to the Director (June 2019), https://www.acd.od.nih.gov/
documents/presentations/06132019HRHR_B.pdf.

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Blueprint for Action: Chapter 11 - Endnotes
Studies have found that research that effectively combines diversity of knowledge is more likely toprompt breakthroughs and that interdisciplinary research lends itself to complex problem-solving,developing new research thrusts, and challenging the status quo. See Lee Fleming, Recombinant
Uncertainty in Technological Search, Management Science (Jan. 2001), https://funginstitute.berkeley.
edu/wp-content/uploads/2012/10/Recombinant-Uncertainty-in-Technological-Search.pdf; Andrew
Barry, et al., Logics of Interdisciplinarity, Economy and Society (Feb. 2008), http://users.sussex.
ac.uk/~ir28/IDR/Barry2008.pdf.
21

22
The NIH Director’s Pioneer Award supports researchers at any career stage who propose boldresearch projects with unusually broad scientific impact. The program supports awardees with
$3.5 million over five years and requires 51% of time spent on research in the first three years. See
NIH Director’s Pioneer Award, National Institutes of Health (last accessed Jan. 1, 2021), https://commonfund.nih.gov/pioneer. Competition for participation in the program is high; reportedly the
success rate for applicants is just 1%. See Roberta B. Ness, The Creativity Crisis, Oxford University
Press at 87 (2015).

Established in 1978, the Howard Hughes Medical Institute (HHMI) supports more than 250
investigators across the United States. Thirty current or former HHMI investigators have been awarded
the Nobel Prize. The HHMI Investigator Program is organized around the core belief in the power ofindividuals to make breakthroughs over time. Through the program, which selects 20 investigators per
year, HHMI aims to expand a community of basic researchers and physician scientists who catalyze
discovery research in basic and biomedical sciences, plant biology, evolutionary biology, biophysics,
chemical biology, biomedical engineering, and computational biology. See Investigator Program,
HHMI (last accessed Feb. 3, 2021), https://www.hhmi.org/programs/biomedical-research/investigatorprogram; see also Competition to Select New HHMI Investigators, HHMI (2020), https://www.hhmi.org/
sites/default/files/programs/investigator/investigator2021-program-announcement-200714.pdf.

23

This mirrors the HHMI structure and cost model, with HHMI awarding $8 million over a seven-year
term. HHMI updated the length of their award in 2018, extending the term from five to seven years.
See HHMI Bets Big on 19 New Investigators, HHMI (May 23, 2018), https://www.hhmi.org/news/hhmibets-big-on-19-new-investigators.

24

Should researchers move institutions over the course of the program, the award would move with
them.
25

26
This could be conducted through a cooperative agreement, mirroring the relationship NSF formed
with the Computing Research Association to launch the Computing Innovation Fellows program in2009 to support postdoctoral PhDs imperiled in finding academic appointments by the downturn ofthe economy. See CIFellows, Computing Community Consortium (last accessed Jan. 1, 2021), https://cra.org/ccc/leadership-development/cifellows/. Furthermore, this entity would be able to accept
supplemental funding from individuals, corporations, or other non-profits to further strengthen andexpand the program.

They would provide meaningful feedback to selectees throughout their participation in the program.
The quality of feedback provided by reviewers was identified by researchers as a key factor in the
success of HHMI investigators. Pierre Azoulay, et al., Incentives and Creativity: Evidence from theAcademic Life Sciences, NBER (Dec. 2011), https://www.nber.org/papers/w15466.

27

Pierre Azoulay & Danielle Li, Scientific Grant Funding, MIT & NBER (March 4, 2020), https://
mitsloan.mit.edu/shared/ods/documents/?PublicationDocumentID=6296. See also the “gold award”
model used by the Gates Foundation. How Grand Challenges Explorations Grants Are Selected, Bill
& Melinda Gates Foundation Global Grand Challenges (last accessed Feb. 3, 2021), https://gcgh.
grandchallenges.org/how-grand-challenges-explorations-grants-are-selected.

28

The amount of the award would be adjusted in accordance with the specificities of the project.
Eligible teams would be composed of researchers based in U.S. academic or research institutions
proposing innovative work related to AI.

2930
The annual Taulbee Survey that tracks the field of computer science (CS) found that womenmake up 21.0% of CS bachelor graduates and 20.3% of CS doctoral graduates, and domestic
underrepresented minorities account for 14.7% of CS bachelor graduates and only 3.1% of doctoral
graduates. Stuart Zweben & Betsy Bizot, 2019 Taulbee Survey, Computing Research Association at
4-5, 22, (May 2020), https://cra.org/wp-content/uploads/2020/05/2019-Taulbee-Survey.pdf.

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Nur Ahmed & Muntasir Wahed, The De-democratization of AI: Deep Learning and the Compute
Divide in Artificial Intelligence Research, ArXiv (Oct. 22, 2020), https://arxiv.org/abs/2010.15581.

31

32
Joel Klinger, et al., A Narrowing of AI Research? ArXiv (Nov. 18, 2020), https://arxiv.org/
pdf/2009.10385.pdf.

This program may be realized as a single cloud resource or a federation of resources, the pros andcons of which should be considered by the task force with determinations made within their resulting
roadmap.

33

34
Such as the NSF’s CloudBank, which brokers cloud access to specific NSF-funded researchers,
and the COVID-19 High Performance Computing Consortium, a public-private partnership that grants
access to a range of computing resources to serve COVID-19 –related research. See CloudBank (lastaccessed Jan. 2, 2021), https://www.cloudbank.org/; the COVID-19 High Performance Computing
Consortium (last accessed Jan. 2, 2021), https://covid19-hpc-consortium.org/.

Pub. L. 116-283, sec. 5106, William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021).

35

36
For example, AI testbeds could be hosted by DoE’s existing national laboratory facilities and highperformance computing resources, by DoD’s existing testing and evaluation infrastructure, or by
facilities managed by the Department of Transportation, NIH, NIST, or the Department of Agriculture.

The National AI Initiative Act of 2020 tasks NIST to develop standards for AI data sharing anddocumentation. See Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act
for Fiscal Year 2021, 134 Stat. 3388 (2021).

37

Through such mechanisms as the Intergovernmental Personnel Act mobility program.
Intergovernmental Personnel Act, U.S. Office of Personnel Management (last accessed Feb. 1, 2021),
https://www.opm.gov/policy-data-oversight/hiring-information/intergovernment-personnel-act/.

38

Executive Order 13859 on AI called on federal agencies to “enhance access to high-quality federal
data, models, and computing resources to increase their value for AI R&D.” See Donald J. Trump,Executive Order on Maintaining American Leadership in Artificial Intelligence, The White House (Feb.
11, 2019), https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-maintainingamerican-leadership-artificial-intelligence/.

39

This would provide for creation of five initial data sets, as well as maintenance over their lifetimeand creation of additional data sets as the program matures.

40

The DoE is well placed to manage such a program, leveraging the cross-disciplinary expertise
resident throughout the laboratory network, the unique computing and user facilities housed at the
17 laboratories, and the ability to create and maintain secure data environments. User Facilities at a
Glance, U.S. Department of Energy: Office of Science (last accessed Jan. 2, 2021), https://science.
osti.gov/User-Facilities/User-Facilities-at-a-Glance#0. The program could build on the pathfinder
Open Data Initiative launched by Lawrence Livermore National Laboratory in partnership with the
University of California San Diego, which hosts complex, labelled data sets for testing solutions
for scalable ML platforms. See New Partnerships Results in Increased Access to Compelling “RealWorld Data,” UC San Diego (April 21, 2020), https://library.ucsd.edu/news-events/new-partnershipresults-in-increased-access-to-compelling-real-world-data/; Open Data Initiative, Lawrence Livermore
National Laboratory (last accessed Jan. 2, 2021), https://data-science.llnl.gov/open-data-initiative.

41

For example, NSF, NASA, NIH, and DARPA have all sponsored or created data resources relevant
to an open knowledge network. In addition, government and community-led efforts to pool data tobuild solutions to the COVID-19 pandemic could be leveraged.

42Open Knowledge Network: Summary of the Big Data IWG Workshop, National Science & Technology
Council (Nov. 2018), https://www.nitrd.gov/pubs/Open-Knowledge-Network-Workshop-Report-2018.
pdf.

43

This would build on ongoing efforts through NSF’s Convergence Accelerator track on Open
Knowledge Networks. NSF Convergence Accelerator Awards Bring Together Scientists, Businesses,
Nonprofits to Benefit Workers, NSF (Sept. 10, 2019), https://www.nsf.gov/news/special_reports/
announcements/091019.jsp.

44

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Blueprint for Action: Chapter 11 - Endnotes
For additional details and recommendations on technologies associated with AI that are important
to U.S. technology leadership, see Chapter 16 of this report. A strategic industry is considered by thegovernment to be very important to a country’s economy or safety. In the national security context,
it is considered critical to the country’s competitive advantage over an adversary. While the UnitedStates’ 16 critical infrastructure sectors refer to large segments of the economy “whose assets,systems, and networks, whether physical or virtual, are considered so vital to the United States,” astrategic industry refers to a much more specific group of companies or businesses. See Critical
Infrastructure Sectors, Cybersecurity and Infrastructure Agency (last accessed Jan. 4, 2020), https://www.cisa.gov/critical-infrastructure-sectors; see also Strategic Industry, Cambridge Dictionary (lastaccessed Jan. 4, 2020), https://dictionary.cambridge.org/dictionary/english/strategic-industry.

45

46
U.S. federal agencies collectively have an annual information technology (IT) budget of $90
billion—one-tenth the annual revenue of the top five U.S. tech firms—yet the majority of government
systems are “outdated and poorly protected.” An American Budget, U.S. Office of Management andBudget at 9 (Feb. 2018), https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/02/budgetfy2019.pdf.

Congress, in the Consolidated Appropriations Act, 2021, called on the General Services
Administration (GSA) to create a five-year program to be known as the “AI Center of Excellence”
to “(1) facilitate the adoption of artificial intelligence technologies in the Federal Government; (2)improve cohesion and competency in the adoption and use of artificial intelligence within the Federal
Government; and (3) carry out paragraphs (1) and (2) for the purposes of benefiting the public andenhancing the productivity and efficiency of Federal Government operations.” Rules Committee Print
116-68, Text of the House Amendment to Senate Amendment to H.R. 133, U.S. House Committee on
Rules at 378 (Dec. 21, 2020), https://rules.house.gov/sites/democrats.rules.house.gov/files/BILLS116HR133SA-RCP-116-68.pdf (referring specifically to section 103 of the Consolidated Appropriations
Act, 2021).

47

The Consolidated Appropriations Act, 2021, outlines AI CoE’s duties to include “advising the
Director of the Office of Science and Technology Policy on developing policy related to researchand national investment in artificial intelligence.” Rules Committee Print 116-68, Text of the House
Amendment to Senate Amendment to H.R. 133, U.S. House Committee on Rules at 380 (Dec. 21,2020), https://rules.house.gov/sites/democrats.rules.house.gov/files/BILLS-116HR133SA-RCP-116-68.
pdf.

48

The National AI Initiative Act of 2020 directs the Director of OSTP to establish a “National Artificial
Intelligence Initiative Office” within OSTP to “(1) provide technical and administrative support
to the Interagency Committee and the Advisory Committee; (2) serve as the point of contact onFederal artificial intelligence activities carried out under the Initiative for Federal departments andagencies, industry, academia, nonprofit organizations, professional societies, State governments,and such other persons as the Initiative Office considers appropriate to exchange technical andprogrammatic information; (3) conduct regular public outreach to diverse stakeholders, including
through the convening of conferences and educational events, the publication of information aboutsignificant Initiative activities on a publicly available website, and the dissemination of findings andrecommendations of the Advisory Committee, as appropriate; and (4) promote access to and early
adoption of the technologies, innovations, lessons learned, and expertise derived from Initiative
activities to agency missions and systems across the Federal Government, and to industry, including
startup companies.” Pub. L. 116-283, sec. 5102, William M. (Mac) Thornberry National Defense
Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).

49

50
The Consolidated Appropriations Act, 2021, outlines AI CoE’s duties to include “advising theAdministrator, the Director, and agencies on the acquisition and use of artificial intelligence throughtechnical insight and expertise, as needed.” Rules Committee Print 116-68, Text of the House
Amendment to Senate Amendment to H.R. 133, U.S. House Committee on Rules at 379 (Dec. 11,2020), https://rules.house.gov/sites/democrats.rules.house.gov/files/BILLS-116HR133SA-RCP-116-68.
pdf.

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51
The Consolidated Appropriations Act, 2021, outlines AI CoE’s duties to include “(1) regularly
convening individuals from agencies, industry, Federal laboratories, nonprofit organizations,institutions of higher education, and other entities to discuss recent developments in artificial
intelligence, including the dissemination of information regarding programs, pilots, and otherinitiatives at agencies, as well as recent trends and relevant information on the understanding,
adoption, and use of artificial intelligence; (2) collecting, aggregating, and publishing on a publicly
available website information regarding programs, pilots, and other initiatives led by other agencies
and any other information determined appropriate by the Administrator.” Rules Committee Print
116-68, Text of the House Amendment to Senate Amendment to H.R. 133, U.S. House Committee on
Rules at 378-79 (Dec. 21, 2020), https://rules.house.gov/sites/democrats.rules.house.gov/files/BILLS116HR133SA-RCP-116-68.pdf.

The digital playbook should consider recent upgrades made to Acquisition.gov. An importantelement of the development of an effective playbook is ensuring its interface and content accounts
for different user profiles. Critical among those user profiles is that of a small-business or nontraditional government contractor that may be unfamiliar with the process to even begin eligibility
for a government contract. Access the Federal Acquisition Regulation, U.S. General Services
Administration (last accessed Feb. 18, 2021), https://www.acquisition.gov/.

52

53
The SBIR program is one of the largest and longest-standing programs for federally funded R&D insmall businesses. It was established in 1982 as part of the Small Business Innovation Development
Act, and Federal agencies with extramural research and development budgets that exceed $100
million set aside 3.2% of their budgets to fund the SBIR program. The program is structured in threephases: Phase I awards of approximately $50,000 to $250,000 for six months to vet “technical merit,feasibility, and commercial potential”; Phase II awards of $750,000 to $1,700,000 for two years to
support successful efforts initiated in Phase I; and Phase III, which is not funded by SBIR dollars, topursue commercialization. The program issues a higher number of Phase I awards but allocates more
funding toward Phase II, with the goal of placing many small bets on novel technologies and only
scaling those that show real promise. NSCAI Engagement (Sept. 25, 2020); see also About, Small
Business Innovation Research (last accessed Feb. 3, 2021), https://www.sbir.gov/about.

For example, AFWERX’s Supplemental Funding Pilot Program (TACFI and STRATFI) andUSD(R&E)’s Accelerated Transition funding program.

54“As of November 2020, agencies may issue a Phase I award (including modifications) up to$259,613 and a Phase II award (including modifications) up to $1,730,751 without seeking SBA
approval. Any award above those levels will require a waiver.” About, Small Business Innovation
Research (last accessed Feb. 3, 2021), https://www.sbir.gov/about. See also Small Business
Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Program Policy Directive,
U.S. Small Business Association (May 2, 2019), https://www.sbir.gov/sites/default/files/SBIR-STTR_
Policy_Directive_ 2019.pdf.

55

The Air Force, in partnership with Air Force Research Lab (AFRL) and the National Security
Innovation Network (NSIN), developed Open SBIR Topics, which includes a “few big bets” (strategic
financing): rewards of up to $15 million, with 1:1:2 Program-SBIR-Private Matching options. SBIR Open
Topics, U.S. Air Force AFWERX (last accessed Feb. 3, 2021), https://www.afwerx.af.mil/sbir.html.

56

Specifically, on page 74 of the SBA SBIR/STTR Policy Directive, the line “For example, someagencies administer Phase IIB awards that differ from the base Phase II in that they require third party
matching of the SBIR/STTR funds.” could be changed to “For example, some agencies administer
Phase II or IIB awards that require third party matching of the SBIR/STTR funds.” Small Business
Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Program Policy Directive,
U.S. Small Business Administration at 74 (May 2, 2019), https://www.sbir.gov/sites/default/files/SBIRSTTR_Policy_Directive_ 2019.pdf.

57

The current application portals for beta.sam.gov and the “Defense SBIR/STTR Innovation
Portal” are significant barriers to entry for non-traditionals trying to work with the DoD. NSCAI staff
engagement (Feb. 9, 2021).

58

59
Future of Defense Task Force Report 2020, U.S. House Committee on Armed Services at 68 (Sept.
23, 2020), https://armedservices.house.gov/_cache/files/2/6/26129500-d208-47ba-a9f7-25a8f82828b
0/424EB2008281A3C79BA8C7EA71890AE9.future-of-defense-task-force-report.pdf.

For more examples of innovation initiatives and recommendations to scale their impact, see Chapter
2 of this report and its associated Blueprint for Action.

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Blueprint for Action: Chapter 11 - Endnotes
Contracts must be easier to understand and fill out, review periods shortened and clearlycommunicated, and oversight streamlined to keep pace with the current rate of technology innovation.
61

Phase II and supplemental awards should be based on a broader diligence process that includes
the long-term health and viability of the company. This assessment should consider as a starting point
the firm’s technical capabilities, financial structure, management structure, and the larger commercial
market opportunities.

62

63
Phase II enhancements, sometimes called Phase IIB/II.5 contacts, have become a common
method to extend SBIR dollars to promising projects that fail to secure Phase III funding. The Navy
Commercialization Readiness Program oversees the distribution of Phase II.5 contracts “to further
develop SBIR technologies and to accelerate transition for existing Phase II projects.” Navy Phase
II.5 Structure and CRP, U.S. Navy (last accessed Feb. 3, 2021), https://www.navysbir.com/cpp.htm.
The Air Force’s AFWERX, Army, and DARPA, as well as several Federal agencies outside the DoD,
also use Phase IIB awards. The Office of the Secretary of Defense (OSD) Transitions SBIR Technology
Pilot Program provides SBIR awardees the opportunity to apply for Phase II Enhancement (e) and
Accelerated Transition funding for the funding sponsor. However, current funding limits set by SBA
reduce their efficacy by including Phase II enhancements under the Phase II cap of SBIR dollars.
NSCAI staff engagement (Sept. 23, 2020). For further detail, see Interim Report and Third Quarter
Recommendations, NSCAI at 52-57 (Oct. 2020), https://www.nscai.gov/previous-reports/.

This effort would be informed by the Technology Annex to the National Defense Strategy
recommended in Chapter 2 of this report.

64

The future will likely be defined by a fusion of many different AI approaches including expert
systems, model-based AI, symbolic-based AI, statistical ML, and new and evolving AI approaches
such as neurosymbolic AI. See Neuro-Symbolic AI, MIT-IBM Watson AI Lab (last accessed Feb. 3,2020), https://mitibmwatsonailab.mit.edu/category/neuro-symbolic-ai/.
65

66
DARPA’s SBIR program, for example, is unique in its long time horizon. Most of its investments are
pre-commercial and will take another eight to 10 years to develop before results can be scaled formilitary or commercial use.

David E. Cooper, Defense Industry Consolidation: Competition Effects of Mergers and Acquisitions,
Statement before the U.S. Senate Committee on Armed Services Subcommittee on Acquisition andTechnology (March 4, 1998), https://www.gao.gov/assets/110/107240.pdf.
67

68
For example, Intel’s recent chip missteps have jeopardized U.S. leadership in the design andmanufacturing of advanced semiconductors. See Michael Kan, Intel: Sorry, But Our 7nm Chips Will Be
Delayed to 2022, 2023, (July 23, 2020), https://www.pcmag.com/news/intel-sorry-but-our-7nm-chipswill-be-delayed-to-2022-2023.

For example, small firms have difficulty affording the cost of compute resources and data fortraining sophisticated ML models. Nur Ahmed & Muntasir Wahed, The De-democratization of AI: Deep
Learning and the Compute Divide in Artificial Intelligence Research, arXiv (Oct. 22, 2020), https://
arxiv.org/abs/2010.15581.
69

70
Michael Porter, The Competitive Advantage of Nations, Harvard Business Review (1990), https://hbr.
org/1990/03/the-competitive-advantage-of-nations.
71
Interim Report, NSCAI at 26 (Nov. 2019), https://www.nscai.gov/previous-reports/; see also MichaelPorter, Clusters and the New Economies of Competition, Harvard Business Review (1998), https://hbr.
org/1998/11/clusters-and-the-new-economics-of-competition.

William R. Kerr & Frederic Robert-Nicoud, Tech Clusters, Journal of Economic Perspectives at 63(2020), https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.34.3.50.

72

The program office could be modeled on the Advanced Manufacturing National Program Office thatcoordinates Manufacturing USA, a network of manufacturing innovation institutes. See Manufacturing
USA (last accessed Feb. 3, 2021), https://www.manufacturingusa.com/.

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For example, proximity to research facilities operated by the departments of Defense and Energy oraccess to technically oriented military installations should be prioritized.

74

75
See the Chapter 13 Blueprint for Action for more details on the importance of U.S. access to trusted
and assured microelectronics for national security use cases. The Commission also proposes a
preliminary list of strategic emerging technologies that are critical to U.S. national competitiveness in
Chapter 16 of this report.
76
The existence of a nascent cluster suggests industry has already passed the market test. Mark
Muro & Bruce Katz, The New “Cluster Moment”: How Regional Innovation Clusters Can Foster the
Next Economy, The Brookings Institution (Sept. 21, 2010), https://www.brookings.edu/research/thenew-cluster-moment-how-regional-innovation-clusters-can-foster-the-next-economy/. Resources likethe U.S. Cluster Mapping Project will also be essential to identify which locations are economically
viable. See U.S. Cluster Mapping (last accessed Feb. 3, 2021), http://clustermapping.us/.
77
Anchor institutions are firms, not-for-profit institutions, and research universities that locate near thecluster and pursue joint R&D with federal agencies or other cluster participants.
78
Overview: The New Federal Role in Innovation Clusters, Clustering for 21st Century Prosperity:
Summary of a Symposium, The National Academies Press (2012), https://www.nap.edu/read/13249/
chapter/3#31.
79

For example, the clusters may be co-located with DoE’s national laboratories or military test ranges.

According to the Council of Economic Advisors, Opportunity Zones (OZs) incentivize private
investment in low-income communities by lowering capital gains taxes on businesses investing in
the region, which could be a revenue-neutral way of lifting people out of poverty due to the expected
reduction in transfer payments. Investors receive tax benefits for investing in Qualified Opportunity
Funds, which can be used to make equity investments in partnerships or corporations that operate
in an OZ. The funds can also be used to purchase tangible property for use in the fund’s trade orbusiness. See The Impact of Opportunity Zones, The Council of Economic Advisors (Aug. 2020),
https://trumpwhitehouse.archives.gov//wp-content/uploads/2020/08/ The-Impact-of-OpportunityZones-An-Initial-Assessment.pdf.

80

Private-sector contributions may comprise cost sharing in joint R&D projects, donations, ormembership dues, if such a model is adopted.

81

82

CODE Advocacy Coalition (last accessed Jan. 2, 2021), https://advocacy.code.org/.

83
See, e.g., Microsoft Philanthropies: TechSpark, https://query.prod.cms.rt.microsoft.com/cms/api/am/
binary/RE4s6AL; Carolina Milanesi, STEM Education as a Diversity Driver in Tech, Amazon (Sept. 14,
2020), https://www.aboutamazon.com/news/community/stem-education-as-a-diversity-driver-in-tech;
Applied Digital Skills: Teach and Learn Practical Digital Skills, Google (last accessed Jan. 2, 2021),
https://applieddigitalskills.withgoogle.com/s/en/home.
84
Michael Wade, Corporate Responsibility in the Digital Era, MIT Sloan Management Review (April 28,2020), https://sloanreview.mit.edu/article/corporate-responsibility-in-the-digital-era/.

Partnership on AI has a mission to shape best practices, research, and public dialogue about
AI’s benefits for people and society, with partners from more than 100 companies and researchorganizations. Partnership on AI (last accessed Feb. 3, 2021), https://www.partnershiponai.org.

85

86
See Chapter 6 of this report for further discussion of the Commission’s proposed U.S. Digital
Service Academy.

One way this could be enacted is by assigning “national mission managers” to oversee each
opportunity identified.

87

88

As recommended in Chapter 9 of this report.

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Chapter 12:
Intellectual Property
Blueprint for Action

America’s intellectual property (IP) laws and institutions must be considered as criticalcomponents for safeguarding U.S. national security interests, including advancing
economic prosperity and technology competitiveness. Prioritization of IP policy is especially
important given China is both leveraging and exploiting IP policies as a tool within its
national strategies for emerging technologies. The United States must, at a minimum,
articulate and develop national IP reforms and policies with the goal of incentivizing,
expanding, and protecting artificial intelligence (AI) and emerging technologies,1 at home
and abroad. Such policies should be developed and proposed via the Executive Branchwith a process that integrates the disparate departments and agencies that serve importantroles in promoting U.S. innovation.
Recommendation

Recommendation: Develop and implement national IP policies and regimes to incentivize,
expand, and protect AI and emerging technologies as part of national security strategies.
Action for the President:• Issue an Executive Order to prioritize IP policies for AI and critical emerging
technologies.
o The President should issue an Executive Order to recognize IP policy as a national
priority and establish a comprehensive process to reform and establish new IP
policies and regimes for AI and critical emerging technologies that further national
security, economic, and technology competitiveness strategies.
o The Executive Order should:■■

Direct the Vice President, as Chair of the Technology Competitiveness Council(TCC)2 or otherwise as chair of an interagency task force,3 to oversee thecomprehensive process;Direct the Secretary of Commerce to:• Lead, on an ongoing basis, the development of proposals (Executive and/or Legislative Branch actions) to reform and establish new IP policies andregimes to incentivize, expand, and protect AI and emerging technologies;• In executing these responsibilities, coordinate with the Under Secretaryof Commerce for Intellectual Property, the Director of the U.S. Patent andTrademark Office (USPTO), and other relevant Executive Branch agencies;p465

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consult with the Director of the U.S. Copyright Office; and convene publicdeliberations, to include at a minimum academia and industry;■

Direct the USPTO Director, in his capacity as advisor to the President,4 to:• Submit, within 90 days, a report to the Vice President, in their capacity
as the head of the TCC or interagency task force, that (1) identifies andanalyzes metrics, trends, and data necessary to inform IP policymaking,particularly as prioritized in the Executive Order; and (2) identifies theassociated U.S. Executive Branch departments and agencies that will be
required to provide any requisite data;• Submit, within 12 months from issuance of the first report, a second
report, or portions on a rolling basis, to the Vice President that (1)comprehensively assesses the weaknesses in the current U.S. IP policies
and regimes, relative to IP regimes of other nations, for incentivizing,
expanding, and protecting innovation in AI and emerging technologies
and supporting national strategies; (2) examines the non-exhaustive listof “IP considerations” (see second recommendation); and (3) proposescorresponding executive and legislative actions for reforming andestablishing new IP policies and regimes;• Provide all necessary information and advice to the Vice President toenable a fulsome analysis of the IP proposals;■

Direct the Vice President to:• Lead an ongoing assessment of IP policies, regimes, and reform
proposals from the Secretary of Commerce that should be implemented
and integrated into national security, economic, and technology
competitiveness strategies;• Empower the Secretary of Commerce to facilitate implementation of IP
policies and regimes assessed as critical to national security, economic,
and technology competitiveness strategies; and■Direct Executive Branch departments and agencies to resource and support
the Secretary of Commerce in executing these Executive Order efforts,
including providing the identified metrics and trends.

Actions for the Secretary of Commerce and USPTO Director:• Establish, as necessary, in consultation with the Director of the USPTO, acommittee of multidisciplinary experts, from within and outside the U.S.
government, to provide technical and IP-related expertise and advice in
implementing this Executive Order.
• Convene public deliberations, to include at a minimum academia and industry,in executing these Executive Order responsibilities. The outcome of thesedeliberations should inform proposed IP policies and regimes.
• Assess metrics and data necessary to inform IP policy.
o In assessing the proper metrics and data necessary to inform IP policy deliberation
as required by the Executive Order, the Secretary of Commerce and USPTO

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Director should take a whole-of-government approach. Due to the breadth of the IP
considerations, including those delineated in this report, as well as the far-reaching
impact of IP upon many segments of the U.S. economy and innovation ecosystem,
there are many U.S. government entities that may already track relevant metrics or
have the capability to expand their analyses to address the necessary prioritizationof IP for AI and emerging technologies.
■■

For example, innovation and investment trends based on patent filings, and,
where possible, licensing data—in various technology sectors, including byforeign countries, particularly China—should be analyzed (e.g., to assess
quality and research trends5), with care not to rely solely on patent counting.
Other potential metrics include but are not limited to tracking of patents selfdeclared as standard essential in comparison to patents actually licensed;
licensing to unrelated parties; the impact of prior art on the U.S. patent andtrademark examination systems; international filings for IP protections on U.S.funded research, particularly without U.S. funders’ or inventors’ awareness; theratio of U.S. companies filing for IP protections, as well as pursuing IP-related
litigation, in the U.S. versus abroad; and patent assignment data.

Action for the Department of Justice:• Advise courts on ensuring consistency on patentability decisions.
o The Department of Justice, through the Solicitor General and the Civil Appellate
Section, should advise federal courts on eliminating confusing, inconsistent, oroverly restrictive patentability decisions to ensure consistency with national security
policies.
Action for Congress:• Prioritize proposed IP-related legislation to bolster U.S. national strategies,
including for national security, economic interests, and technology
competitiveness.
o Congress should prioritize legislative recommendations for IP policies and regimes
elevated by the Vice President, as Chair of the TCC or an interagency task force.
This is particularly important given Congress is responsible for passing patent andIP legislation that the USPTO and other relevant stakeholders execute and follow.
Additionally, the U.S. Copyright Office is housed as a federal department within the
Library of Congress as the principal advisor to Congress on copyright matters andadministers copyright registrations.6

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Executive Order to Prioritize IP Policies for AI
and Emerging Technologies**This illustration is not comprehensive of all relevant U.S. government entities with intellectual property
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Recommendation

Recommendation: The Secretary of Commerce should assess and examine the following
non-exhaustive list of “IP considerations,” in coordination with the Under Secretary ofCommerce for IP and the Director of the USPTO, as part of developing and proposing
reforms and new IP policies and regimes to the Vice President.
Action for the Secretary of Commerce:• Assess and examine the following non-exhaustive list of 10 considerations forintellectual property as part of the reports submitted to the Vice President as
mandated by the Executive Order.
1. Patent Eligibility: The Secretary of Commerce should assess and articulate the impactof current patent eligibility laws on innovation in AI and emerging technologies from an
economic, trade, and national security policy perspective to better inform the legislative
and agency efforts on patent eligibility reform. America’s IP regime has spurred American
ingenuity since the late 18th century. By protecting “any new and useful process, machine,
manufacture, or composition of matter” through stable legal institutions governed by the ruleof law, inventors and investors have relied on America’s IP system to provide the certainty
necessary to justify large and risky R&D investments,7 which are critical for technologies.8
A strong and robust patent system is equally critical to incentivizing American innovationin AI and emerging technologies that affect national security.9 Unfortunately, recent
patent eligibility court rulings have narrowed the scope of inventions that are eligible forpatent protection. This has resulted in a broad swath of innovation that is now ineligible
for patent protection in both digital technologies and biopharma, among others.10 The
legal uncertainty for U.S. innovators and companies as to whether their inventions will be
eligible for patent protection or susceptible to invalidation once granted is pervasive.11 This
uncertainty in turn has impacted investments in AI and technologies critical to national
security. Empirical studies have proven that patents are causally linked to venture capital
investments in startups, and, as a result, are causally linked to the success of startups.12
Recent reports, however, reveal that investments in patent-intensive U.S. startups thatdevelop critical technologies (e.g., computer hardware, semiconductors, medical devices
and supplies, and pharmaceuticals and biotechnology) have declined relative to non–patent-intensive companies.13 This is consistent with investors consistently reporting thatpatent eligibility is a key factor in their decisions whether to invest in a particular company’s
technologies or bring a new product to market.14
Legislation appears to be the only practical means to reform patent eligibility doctrine.
The Judiciary, specifically the Supreme Court, has indicated an unwillingness to revisit
its decisions in the past decade that have created this fundamental problem in patenteligibility doctrine.15 The USPTO has adopted a framework for assessing patent eligibility
during the examination process of patent applications, which has had positive results inproviding greater certainty to patent applicants,16 but the Federal Circuit does not seem
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Efforts to reform the patent eligibility doctrine by amending the relevant provision in thepatent statutes failed in 2019.18 Efforts continue to restart the legislative reform process. A
national security point of view has not been expressed on the impact of patent eligibility
law on technologies critical to national security, such as AI, microelectronics, 5G
telecommunications, quantum computing, and biotechnology. A national security pointof view on the impact of current patent eligibility laws on AI and emerging technologies
should inform a national IP strategy.
2. Counter China’s narrative on winning the innovation competition: The Secretary ofCommerce, in coordination with relevant departments and agencies (e.g., Department ofState, USTR), should address how the United States might best counter China’s efforts toshape the narrative that it is winning the innovation competition based in part on its patentapplication filings and other interventions in its technology markets.19 China has become
the domestic forum with the highest number of patent application filings, and China’s
companies and inventors are the most prolific AI patent application filers globally.20 Thisbenchmark helps to shape the narrative that China has become the leader in innovation
because intensive patenting has been shown to generally correlate to economic growth.21
China also is garnering this reputation when it comes to emerging technologies suchas AI.22 Sources claim that China is outpacing the United States in filing worldwide AIrelated patent applications.23 However, high levels of patenting output is not necessarily
indicative of high levels of inventive output.24 Specifically, non-market factors driven bystate-sponsored interferences can distort filings.25 Moreover, China often files patents as a“numbers game,” which can lead to mischaracterizing its technological prowess. Similarly,
China’s 5G companies declare the most patents as “standard essential,” appearing tomarry China’s concerted, top-down strategy to advance its AI and emerging technology
agenda by influencing international standards setting with its goals to dominate numericbenchmarks.26 The Secretary of Commerce should examine what measures need to beundertaken to counterbalance the narrative of China’s technological dominance based on
selective patenting data.
3. Impact of China’s patent application filings on USPTO and U.S. inventors: The Secretaryof Commerce, in coordination with the USPTO Director, should assess whether the USPTO
requires additional resources, both human and technical, to ensure high-quality patentexamination and recommend policies to address any concerns. In doing so, the Secretaryof Commerce should assess the impacts of increased filings from China and AI-generated
prior art (the term in patent law for the worldwide scientific and technical knowledge bywhich an invention is evaluated to determine if it is new). The large body of often lowquality prior art created by China’s high-volume patenting has the potential to adversely
impact global patent examination systems, including those of the USPTO.27 At the sametime, U.S. inventors may face hurdles in patenting around massive amounts of low-quality
Chinese prior art.28 The USPTO has also noted that stakeholders have raised the issues ofwhether AI may generate a proliferation of prior art, making it difficult to find relevant priorart for examination.29

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4. Impediments to AI public-private partnerships and international collaboration: The
Secretary of Commerce should assess any impediments to the IP contractual ecosystem tostrengthen AI partnerships among national security departments and agencies, industry,
and international collaboration. This should include assessing and addressing ambiguities
in the Federal Acquisition Regulation and the Defense Federal Acquisition Regulation
Supplement relevant to AI and data. AI development presents unique IP contractual issues.
For example, industry AI developers will likely need access to relevant U.S. Government
training data to develop AI-enabled government solutions or applications. If the solution
or application is dual-use, the private entity may want to provide a license for the U.S.
Government agency to access the AI application, but retain the IP in the AI model tolicense to others. But there are unanswered questions as to whether the U.S. Government
agency has any IP rights or ownership in the model that was trained on its data.30 The U.S.
Government agency may also want to retain IP rights in order to avoid “vendor lock.”31
These outstanding questions about IP rights and ownership issues could also arise
in international AI system R&D collaboration, where impediments can be amplified byconflicting national laws on IP and/or data protections.
5. IP protection for data: The Secretary of Commerce should assess whether there is aneed for sui generis protection or additional IP-type of protections for data and propose
policies and/or legislation if protection is deemed necessary. Data is critical to AI andmachine learning (ML), but gaps may exist in current protection regimes afforded bypatent or copyright. Inadequate protections for data may disincentivize the necessary
investments in developing these critical data sets as well as public disclosure and sharing
agreements.32 While protections for data might be a future need, the U.S. should be
proactive in assessing and addressing the necessity of such protections. The Secretary ofCommerce also should explore ways to protect and incentivize creation of data sets whileallowing the data to be shared at some point, particularly with smaller entities that might
not otherwise be able to enter the market.33 An analysis of the strengths and weaknessesof the European sui generis database protections should inform this assessment.34
6. Combat IP theft: The Secretary of Commerce, in coordination with relevant departments
and agencies (e.g., USTR, Intellectual Property Enforcement Coordinator, the National
Science Foundation, the Office of Science and Technology Policy,35 as well as the
Departments of Homeland Security,36 Justice,37 and State) should assess and identify
additional efforts that the Executive Branch should undertake to counter IP theft threats,
including actions in collaboration with allies and partners.38 In particular, the Secretary ofCommerce should clearly articulate that the U.S. counter–IP theft strategy will contain both
criminal and civil economic dimensions. The Department of Commerce should utilize allavailable tools for establishing a deterrence regime to punish firms guilty of stealing U.S.
IP and deter future IP theft to level the playing field for U.S. and allied firms. These tools
should include placing offending companies on the Bureau of Industry & Security entity
list,39 blocking visas of key employees, or levying tariffs against products derived fromstolen IP. Solutions that should be explored include training for allies and partners to stopp471

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counterfeits at borders and efforts to increase individuals’ respect for IP and recognitionof and ways to avoid counterfeits. In addition, the Secretary should assess methods
and means for strengthening and updating existing mechanisms available to American
victims of trade-secret theft, including reintroducing legislation to strengthen the Economic
Espionage Act by, for example, increasing damages available to trade-theft victims andextending the statute of limitations.40
7. Inventorship by AI: The Secretary of Commerce should assess the need for policychanges for issues raised by AI-generated inventions and creations, particularly astechnologies evolve. The USPTO has determined that under current legal doctrine, aninventor must be a natural person and denied a patent application naming a machine as
the inventor.41 The U.S. is not alone in this position.42 The USPTO also issued extensive
requests for public comments on a variety of AI IP policy issues, including AI’s impact
on inventorship and ownership, as well as impacts on non-patent IP protections, such as
copyright. As a result, the USPTO issued a comprehensive report of public views on AI
and IP policy. The majority of commenters agreed that, given that current AI capabilities
are limited to “narrow AI” (AI systems that are trained and perform individual tasks in welldefined domains) and artificial general intelligence is not yet a reality, current AI could
neither invent nor author without human intervention.43 The Secretary of Commerce should
consult with allies and partners to ensure continued harmonization around the various
IP issues raised by AI-generated inventions and creations and gain an understanding ofChina’s strategies for addressing these issues, particularly as AI technologies move pastnarrow AI.
8. Global IP alignment: The Secretary of Commerce, in coordination with relevant
departments and agencies (e.g., USPTO, IPEC, USTR, Department of Defense, Departmentof State), should work with partners and allies to develop global disincentives for IP theft andalleviate any inconsistencies in patent regimes that make it overly difficult for companies
to protect their patents in multinational markets. In doing so, the Secretaries should
leverage the Commission’s recommendation that the United States and allies—throughthe Emerging Technology Coalition—explore coordinated approaches to IP (as part of
the NSCAI-proposed critical area No. 4: Promoting and Protecting Innovation44), including
a mutual agenda within the WIPO’s Conversation on AI and IP and forums with broader
mandates. The Secretaries also should assess whether current forums for dialogues on
global IP alignment are sufficient or whether new forums or venues are necessitated,particularly given any changes to domestic IP policies or regimes identified during thereview of the other IP considerations. For example, if the U.S. determines new protections
or policies are needed for data, it may need to work with key allies and partners—bilaterally
and multilaterally—to ensure global harmonization.
9. Democratize innovation and IP ecosystems: The Secretary of Commerce should assess
whether additional Executive Branch efforts are necessary to expand the innovation baseand democratize access to and create more jobs in the innovation and IP ecosystem.45

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The USPTO, in collaboration with the Secretary of Commerce, has undertaken initiatives toexpand the U.S. innovation base by creating the National Council for Expanding American
Innovation (NCEAI) to develop a comprehensive national strategy to increase equity andfuel the U.S. innovation ecosystem by encouraging, empowering, and supporting all future
innovators.46 The Secretary of Commerce should ensure that the USPTO has the full supportof the Executive Branch in these initiatives. As part of the NCEAI initiative, the Secretaryof Commerce also should focus on assessing and identifying potential actions and toolsthat can fast-track processes and streamline guidance for startups seeking IP protections
and ensuring resources for assisting small and medium-sized entities. Such a focus isparticularly important when comparing the impact of litigation costs and potentially overly
burdensome processes in the U.S., relative to other countries, on U.S. inventors’ decisions
to pursue IP protections in the United States.47
10. “Standard essential” patents process48: The Secretary of Commerce, in coordination
with relevant departments and agencies (e.g., USPTO, NIST, and the Department of State),should assess policies by which the U.S. can serve a leadership role in and ensure U.S.
firms are able to fully participate in the processes by which “standard essential” patents areclaimed and asserted.49 This would help ensure the continuing legitimacy of the standardsetting process, a privately developed method for efficiently coordinating developmentp473

INTELLECTUAL PROPERTY

and deployment of new technologies in the marketplace, and deflect Beijing’s attempt
to dominate or manipulate these processes through its own coordination of firms from
China. Chinese Communist Party leadership has articulated a linkage between patent
leadership in emerging technologies like AI and the standards-setting processes for thesesame technologies.50 Current trends confirm China’s intention to use both patents andstandards to lead in technological innovation.51 Additional mechanisms may be necessary
to protect the integrity of international standards-setting as well as to protect and promote
U.S. innovation, such as identifying efforts by foreign governments to influence, directly
or indirectly, standard-setting organizations. This would also include identifying foreign
governments subsidizing or otherwise incentivizing the over-declaration of patents as“standard essential”52 or creating barriers to U.S. participation in foreign standard-setting
bodies. The Secretary of Commerce also should explore how the U.S. government might
support smaller U.S. companies and inventors fully participating in the standard-setting
process and encourage the observation of licensing or legal disputes in foreign jurisdictions
by U.S. government officials from U.S. Embassies and Missions. Relatedly, the Secretaryof Commerce, in coordination with the Director of the USPTO, should assess foreign court
rulings on licensing that may impact U.S. national sovereignty to determine a coherent U.S.
position or response.53

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Blueprint for Action: Chapter 12 - Endnotes
For a discussion of the U.S. government’s efforts to define and prioritize critical emerging
technologies, as well as the Commission’s recommended eight emerging technologies key to U.S.
national competitiveness, see Chapter 16 of this report and its associated Blueprint for Action.

1

NSCAI recommended the creation of a Technology Competitiveness Council in its 2020
Interim Report and Third Quarter Recommendations. See Interim Report and Third Quarter
Recommendations, NSCAI at 180 (Oct. 2020), https://www.nscai.gov/previous-reports/ (“Technology
Competitiveness Council, led by the Vice President and with a Commissioned Assistant to
the President as the day-to-day coordinator, to fill this role.”) If the TCC is not established asrecommended by the Commission, the Commission recommends that the Vice President should leadthese efforts.

2

If the TCC is not established, the President, through an Executive Order, should establish a task
force to address the mandate recommended here.

3

The USPTO Director “shall advise the President, through the Secretary of Commerce, on nationaland certain international intellectual property policy issues.” 35 U.S.C. § 2.

4

As an example, an examination of China’s patents can provide insight into its biotechnology andgenomics research and plans. See Kristy Needham, Exclusive: China Gene Firm Providing Worldwide
COVID Tests Worked with Chinese Military, Reuters (Jan. 30, 2021), https://www.reuters.com/article/
us-china-genomics-military-exclusive/exclusive-china-gene-firm-providing-worldwide-covid-testsworked-with-chinese-military-idUSKBN29Z0HA.

5

6Overview of the Copyright Office, U.S. Copyright Office (last accessed Feb. 2, 2021), https://www.
copyright.gov/about/.

NSCAI staff engagement with Professor Adam Mossoff, Antonin Scalia Law School, George Mason
University (Oct. 7, 2020); David J. Kappos, National Security Consequences of U.S. Patent (In)eligibility, Morning Consult (Nov. 4, 2019), https://morningconsult.com/opinions/national-securityconsequences-of-u-s-patent-ineligibility/.

7

8For example, the Supreme Court’s controversial 1980 decision in Diamond v. Chakrabarty, whichclassifies a genetically modified bacterium as a patentable innovation (under Section 101), “was a
key factor in spurring the explosive growth in the biotech industry in the ensuing decade in the U.S.
The Chakrabarty Court’s recognition that the products of biotech research are patentable, especiallywhen such products are living organisms or represent the building blocks of life, paved the way fordramatic advances in the life sciences and in medical treatment, such as in cancer research.” While
the U.S. was the first country to patent genetic modification of living organisms (critical for cancer
research), other countries refused to patent such innovations for more than a decade. This led to the
U.S. becoming the birthplace of the biotech revolution. Similarly, the Supreme Court’s 1981 decisionin Diamond v. Diehr that an invented process using “a computer program was not automatically an‘abstract idea’ or ‘algorithm’ that precluded patent protection” was key for providing reliable patentrights that enabled the high-tech revolution of the late 20th century. Kevin Madigan & Adam Mossoff,
Turning Gold to Lead: How Patent Eligibility Doctrine Is Undermining U.S. Leadership in Innovation,
George Mason Law Review, Vol. 24 at 942-946 (2017), https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=2943431.

Technologies critical to national security interests include AI, microelectronics, 5G
telecommunications, quantum computing, and biotechnology. For more information on various U.S.
government efforts to define and prioritize critical emerging technologies and the Commission’s
recommended list of critical emerging technologies, see Chapter 16 of this report and its associatedBlueprint for Action. See also Interim Report and Third Quarter Recommendations, NSCAI at 138 (Oct.
2020), https://www.nscai.gov/previous-reports/. There also is a convergence of technologies with theinfusion of AI across all technologies. See Joint Written Testimony of Dr. Eric Schmidt et al. before
the House Committee on Armed Services, Subcommittee on Intelligence and Emerging Threats andCapabilities, Interim Review of the National Security Commission on Artificial Intelligence Effort andRecommendations (Sept. 17, 2020), https://docs.house.gov/meetings/AS/AS26/20200917/110996/
HHRG-116-AS26-Wstate-SchmidtE-20200917.pdf.

9

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10
See Alice Corp. v. CLS Bank Int’l, 134 S. Ct. 2347, 2360 (2014) (holding that a computer program forfacilitating complex international financial transactions is an abstract idea and cannot be patented);
see also Association for Molecular Pathology v. Myriad Genetics, Inc., 133 S. Ct. 2107, 2117 (2013)(holding that isolated DNA for laboratory and medical uses is an unpatentable natural phenomenon);Mayo Collaborative Services v. Prometheus Laboratories, Inc., 566 U.S. 66, 72-73 (2012) (holdingthat a diagnostic medical treatment for an autoimmune disorder is an unpatentable discovery of a lawof nature); Bilski v. Kappos, 561 U.S. 593, 609 (2010) (holding that a business method for hedging
investment risk is an abstract idea and not a patentable invention); Kevin Madigan & Adam Mossoff,
Turning Gold to Lead: How Patent Eligibility Doctrine Is Undermining U.S. Leadership in Innovation,
George Mason Law Review, Vol. 24 at 946-952 (2017), https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=2943431.
11
A former Chief Judge of the Federal Circuit lamented this uncertainty while testifying before theU.S. Senate Judiciary Committee’s Intellectual Property Subcommittee: “It is important for me, as aretired [Federal Circuit] judge, to acknowledge that the courts alone created this problem. … If I, asa judge with 22 years of experience deciding patent cases on the Federal Circuit’s bench, cannot
predict outcomes based on case law, how can we expect patent examiners, trial judges, inventors
and investors to do so? ” See Testimony of Judge Paul R. Michel (Ret.), U.S. Court of Appeals for theFederal Circuit, before the U.S. Senate Committee on the Judiciary, Subcommittee on Intellectual
Property, The State of Patent Eligibility in America: Part I (June 4, 2019), https://www.judiciary.senate.
gov/imo/media/doc/Michel%20Testimony.pdf. The U.S. Chamber of Commerce recently observedthat uncertainty surrounding patent-eligible subject matter and the viability of biopharmaceuticalcompanies’ business models is posing “an existential threat to the United States’ position as theundisputed global leader in biopharmaceutical innovation.” Art of the Possible: U.S. ChamberInternational IP Index, U.S. Chamber of Commerce, Global Innovation Policy Center at 10 (2020),
https://www.theglobalipcenter.com/wp-content/uploads/2020/02/023881_GIPC_IP_Index_ 2020_
FullReport_ A _04b.pdf. The former Director of the USPTO similarly emphasized the importance of
certainty to innovation in the U.S.: “[t]o ensure that our nation remains at the forefront of AI and othertechnologies, we must, among other things, provide a reliable and predictable legal framework
to incentivize and protect innovation here at home.” See USPTO Responses to Questions for the
Record by Senator Tillis, Hon. Andrei Iancu, Under Secretary of Commerce for Intellectual Property
and Director of the U.S. Patent and Trademark Office, as Witness, U.S. Senate Committee on the
Judiciary, Subcommittee on Intellectual Property, Oversight of the U.S. Patent and Trademark Office at
11 (hearing held March 13, 2019, responses submitted Aug. 15, 2019), https://www.judiciary.senate.
gov/imo/media/doc/Iancu%20Responses%20to%20QFRs2.pdf.

See Joan Farre‐Mensa, et al., What Is a Patent Worth? Evidence from the U.S. Patent “Lottery,”National Bureau of Economic Research (Dec. 2018), https://www.nber.org/papers/w23268 (finding analmost double increase in chance of a startup receiving venture capital investments if it has a patent,
and further finding this causally linked to a higher rate of success in startups); Stuart J.H. Graham,
et al., High Technology Entrepreneurs and the Patent System: Results of the 2008 Berkeley Patent
Survey, Berkeley Technology Law Journal, Vol. 24, No. 4 at 255-327 (July 4, 2009), https://papers.
ssrn.com/sol3/papers.cfm?abstract_id=142904.

12

Surveys and industry reports demonstrate that “investment has shifted away from patent-intensive
industries.” Mark F. Schultz, The Importance of an Effective and Reliable Patent System to Investment
in Critical Technologies, Alliance for U.S. Startups and Investors for Jobs at 24-37 (July 2020),
https://static1.squarespace.com/static/5746149f86db43995675b6bb/t/5f2829980ddf0c536e713
2a4/1596467617939/USIJ+Full+Report_Final_ 2020.pdf. For example, a look at a subset of patentreliant technologies (core internet networking, wireless communications, internet software, operating
system software, semiconductors, pharmaceuticals, drug discovery, surgical devices, and medical
supplies) shows a significant decrease in funding, from 21% of total venture capital funding in 2004 to
only 3.2% in 2017. U.S. Startup Company Formation and Venture Capital Funding Trends 2004 to 2017,
Alliance for U.S. Startups and Investors for Jobs at 9 (June 2019), https://static1.squarespace.com/
static/5746149f86db43995675b6bb/t/5d14b7bb46692200012463e0/1561638845187/USIJ+--+U.S.+S
tartup+Formation+Trends+--+2014-2017.pdf.

1314David Taylor, Patent Eligibility and Investment, Cardozo Law Review at 2055-2056 (2020), http://
cardozolawreview.com/patent-eligibility-and-investment/.

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Blueprint for Action: Chapter 12 - Endnotes
See, e.g., Hikma Pharmaceuticals USA Inc. v. Vanda Pharmaceuticals Inc., No. 18-817 (Jan. 13,
2020) (cert. denied); Athena Diagnostics, Inc. v. Mayo Collaborative Services, LLC, No. 19-430 (Jan.
13, 2020) (cert. denied); HP Inc. v. Berkheimer, No. 18-415 (Jan. 13, 2020) (cert denied). In Athena,
all 12 active judges of the Federal Circuit, the appellate court from which the decision was appealed
to the Supreme Court, agreed that the diagnostic methods at issue should be patent eligible, but
the majority indicated that they had to find the inventions ineligible for patent protection pursuant toSupreme Court precedent. Athena Diagnostics, Inc. v. Mayo Collaborative Services, LLC, No. 19-430(Jan. 13, 2020) (cert. denied). On Jan. 29, 2021, however, the Supreme Court asked for a response toa petition for certiorari appealing a decision from the Federal Circuit that a drive shaft is not eligible
for patent protection because the alleged invention is based on a natural law. American Axle &Manufacturing Inc. v. Neapco Holdings LLC, No. 20-891 (Jan. 29, 2021). See also Rebecca Lindhorst,
Two-Stepping Through Alice’s Wasteland of Patent-Eligible Subject Matter: Why the Supreme Court
Should Replace the Mayo/Alice Test, Case Western Reserve Law Review at 759 (2019), https://scholarlycommons.law.case.edu/cgi/viewcontent.cgi?article=4813&context=caselrev.

15

In January 2019, the USPTO published the initial framework in a Revised Guidance and requested
public comment on the Guidance. See 84 Fed. Reg. 50, United States Patent and Trademark Office:2019 Revised Patent Subject Matter Eligibility Guidance, U.S. Patent and Trademark Office (Jan.
7, 2019), https://www.federalregister.gov/documents/2019/01/07/2018-28282/2019-revised-patentsubject-matter-eligibility-guidance. Once the USPTO received comments, it issued an Update to the
Guidance: October 2019 Update: Subject Matter Eligibility, U.S. Patent and Trademark Office (Oct.
2019), https://www.uspto.gov/sites/default/files/documents/peg_oct_ 2019_update.pdf. The Revised
Guidance and the Update were later incorporated into the newest edition of the USPTO’s Manualof Patent Examining Procedure when it was revised in June 2020. See Manual of Patent Examining
Procedure, United States Patent and Trademark Office at § 2103 –2106.07(c) (June 2020), https://www.uspto.gov/web/offices/pac/mpep/index.html. Since the USPTO issued the patent eligibility
guidance, uncertainty in the examination process has significantly decreased for technologies
affected by the Alice decision. Office of the Chief Economist, Adjusting to Alice: USPTO Patent
Examination Outcomes After Alice Corp. v. CLS Bank International, United States Patent andTrademark Office at 6-7 (April 2020), https://www.uspto.gov/sites/default/files/documents/OCEDH_ AdjustingtoAlice.pdf (demonstrating with statistical significance that the Guidance decreased
uncertainty as to patent eligibility determinations in the first-action stage of examination by 44% for
Alice -affected technologies).

16

17
Though the USPTO Guidance on patent eligibility applies at the USPTO, the Federal Circuit has
held that it is not bound by the Guidance and, if any conflicts arise between it and case precedentfrom the Federal Circuit and the Supreme Court, precedent will override the Guidance. See Cleveland
Clinic Foundation v. True Health Diagnostics LLC, 760 F. App’x 1013, 1020 (Fed. Cir. 2019) (nonprecedential) (“While we greatly respect the PTO’s expertise on all matters relating to patentability,
including patent eligibility, we are not bound by its guidance.”); see also In re Rudy, 956 F.3d 1379,
1383 (Fed. Cir. 2020) (precedential) (citing Cleveland Clinic Foundation, 760 F. App’x at 1021 (“To theextent the Office Guidance contradicts or does not fully accord with our caselaw, it is our caselaw,
and the Supreme Court precedent it is based upon, that must control.”).
18
Michael Borella, The Zombie Apocalypse of Patent Eligibility Reform and a Possible EscapeRoute, Patent Docs (Feb. 4, 2020), https://www.patentdocs.org/2020/02/the-zombie-apocalypseof-patent-eligibility-reform-and-a-possible-escape-route.html?utm_source=feedburner&utm_
medium=feed&utm_campaign=Feed%3A+PatentDocs+%28Patent+Docs%29 (citing an interview
wherein Senator Thom Tillis, Chairman of the Senate Judiciary Committee’s Subcommittee onIntellectual Property, recognized that his 2019 patent eligibility reform proposal did not have a “pathforward” to become a bill in that Congress).

Solely relying on patent counting is not reflective of innovation. See Jonathan Putnam, et al.,Innovative Output in China, at 32 (Aug. 2020) (pending revision), https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=3760816.
19

20
Patrick Thomas & Dewey Murdick, Patents and Artificial Intelligence: A Primer, Center for Security
and Emerging Technology at 10 (Sept. 2020), https://cset.georgetown.edu/wp-content/uploads/CSETPatents-and-Artificial-Intelligence.pdf.
21
Jonathan M. Barnett, Patent Tigers and Global Innovation, CATO at 14 (Winter 2019/2020), https://www.cato.org/sites/cato.org/files/2019-12/v42n4-2.pdf.

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22
WIPO’s Patent Cooperation Treaty (PCT) procedure allows inventors to indicate an intent tofile patent applications in multiple countries. However, while in the subsequent national phaseapplications, a third country examines the patent and makes its own determination to grant. Therefore,
experts assert that national phase applications are a better indicator for monitoring high-quality
patent filings than filings under the PCT system. For information on PCT and national phase process,see PCT FAQs, WIPO (April 2020), https://www.wipo.int/pct/en/faqs/faqs.html; WIPO Technology
Trends 2019: Artificial Intelligence, WIPO at 61-63, https://www.wipo.int/edocs/pubdocs/en/wipo_
pub_1055.pdf; George Leopold, China Dominates AI Patent Filings, EnterpriseAI (Aug. 31, 2020),
https://www.enterpriseai.news/2020/08/31/china-dominates-ai-patent-filings/ (“Beijing has becomea fierce defender of intellectual property linked to what planners consider a strategic technology”).
Although China has a high level of PCT filings, the associated national phase applications are
significantly lower. See Patent Cooperation Treaty Yearly Review 2020: The International Patent
System, WIPO at 50 and 55 (2020), https://www.wipo.int/edocs/pubdocs/en/wipo_pub_901_ 2020.pdf.

Yuki Okoshi, China Overtakes U.S. in AI Patent Rankings, Nikkei Asia (March 10, 2019), https://asia.nikkei.com/Business/Business-trends/China-overtakes-US-in-AI-patent-rankings (“Chinesecompanies have surged ahead of their U.S. counterparts on a Nikkei ranking of the top 50 patent
filers for artificial intelligence over the past three years, expanding their presence in the world’s mostprominent high-tech battleground.”); Andrew Snowdon, UK Ranked Fourth in the World for Number
of Blockchain Patents Filed But Is Falling Behind for AI Patents, UHY Hacker Young (Jan. 21, 2019),
https://www.uhy-uk.com/insights/uk-ranked-fourth-world-number-blockchain-patents-filed-fallingbehind-ai-patents (“New Artificial Intelligence technology developments dominated by Chinesecompanies”).

23

5G Technological Leadership, Hudson Institute at 2 (Dec. 2020), https://s3.amazonaws.com/media.
hudson.org/Hudson_5G%20Technological%20Leadership.pdf (“There are important limitations withusing patent counting as a measure of innovative output, as economists and statisticians have longrecognized. … This is why economists consider information about the number of patents to be a‘noisy’ indicator of innovative output. … What matters is the quality, not the quantity of patents.”);Jonathan Putnam, et al., Innovative Output in China, at 32 (Aug. 2020) (pending revision), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3760816; Jonathan M. Barnett, Patent Tigers andGlobal Innovation, CATO (Winter 2019/2020), https://www.cato.org/sites/cato.org/files/2019-12/v42n42.pdf.

24

25
Michael Mangelson, et al., Trademarks and Patents in China: The Impact of Non-Market Factors on
Filing Trends and IP Systems, U.S. Patent and Trademark Office at 1 (Jan. 2021), https://www.uspto.
gov/sites/default/files/documents/USPTO-TrademarkPatentsInChina.pdf (discussing China’s subsidies
for trademark and patent application filings); Testimony of Mark Cohen, Senior Counsel on China in
the Office of Policy and International Affairs in the United States Patent and Trademark Office, Before
House Committee on the Judiciary, International Antitrust Enforcement: China and Beyond (June7, 2016), https://www.uspto.gov/about-us/news-updates/statement-mark-cohen-house-committeejudiciary (discussing numerous strategies used by China to increase patent filings).

See Meeting the China Challenge: A New American Strategy for Technology Competition,
Working Group on Science and Technology in U.S.-China Relations at 29 (Nov. 16, 2020),
https://asiasociety.org/sites/default/files/inline-files/report_meeting-the-china-challenge_ 2020.
pdf [hereinafter Meeting the China Challenge]; Matthew Noble, et al., Determining WhichCompanies Are Leading the 5G Race, IAM (July/Aug. 2019), https://www.twobirds.com/~/
media/pdfs/news/articles/2019/determining-which-companies-are-leading-the-5g-race.
pdf?la=en&hash=8ABA5A7173EEE8FFA612E070C0EA4B4F53CC50DE. For example, as ofFebruary 2020, Huawei and ZTE filed the most number of “standard essential” patents (SEP)s for 5G
technologies, but assessments of these filings are critical of the quality of these patents. Jed John
Ikoba, Huawei Has Filed the Most 5G Patents Globally as of February 2020 –A Report, Gizmochina(June 2, 2020), https://www.gizmochina.com/2020/06/02/huawei-has-the-most-5g-standardessential-patents-globally/.

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Blueprint for Action: Chapter 12 - Endnotes
The potential impact of Chinese patent prior art that must be examined at the USPTO can belikened to what is happening to the USPTO trademark application process. An influx of fraudulent
trademark applications from China, supported by monetary incentives from the Chinese government,
is likely damaging the integrity of the U.S. trademark registration process, including by imposing
unpredictability in examination process schedules. Hearing on Fraudulent Trademarks: How They
Undermine the Trademark System and Harm American Consumers and Businesses, U.S. Senate
Committee on the Judiciary, Subcommittee on Intellectual Property (Dec. 3, 2019), https://www.
judiciary.senate.gov/meetings/fraudulent-trademarks-how-they-undermine-the-trademark-systemand-harm-american-consumers-and-businesses; Barton Beebe & Jeanne C. Fromer, Are We Running
Out of Trademarks? An Empirical Study of Trademark Depletion and Congestion, Harvard Law Review(Feb. 9, 2018), https://harvardlawreview.org/2018/02/are-we-running-out-of-trademarks/; Josh
Gerben, Massive Wave of Fraudulent US Trademark Filings Likely Caused by the Chinese Government
Payments, Gerben (last accessed Jan. 3, 2021), https://www.gerbenlaw.com/blog/chinese-businesssubsidies-linked-to-fraudulent-trademark-filings/.

27

Jeanne Suchodolski, et al., Innovation Warfare, North Carolina Journal of Law & Tech at 201 (Dec.
2020), https://ncjolt.org/articles/volume-22/volume-22-issue-2/innovation-warfare/.

28

Public Views on Artificial Intelligence and Intellectual Property Policy, U.S. Patent and Trademark
Office at iii (Oct. 2020), https://www.uspto.gov/sites/default/files/documents/USPTO_ AIReport_ 2020-10-07.pdf [hereinafter USPTO AI IP policy report].

29

30
Richard Vray & Jane Mutimear, Artificial Intelligence: Navigating the IP Challenges, Mobile
World Live (Feb. 16, 2019), https://www.mobileworldlive.com/intellectual-property-news/artificialintelligence-navigating-the-ip-challenges.

David Deptula, The Growing Importance of Data Rights in Defense Acquisition, Forbes (Oct. 16,2018), https://www.forbes.com/sites/davedeptula/2018/10/16/the-growing-importance-of-data-rightsin-defense-acquisition/?sh=165063242a04.
31

32
In the USPTO report surveying stakeholders for perspectives on IP policy for AI, “commenterswere nearly equally divided between the view that new intellectual property rights were necessary
to address AI inventions and the belief that the current U.S. IP framework was adequate to address
AI inventions. Generally, however, commenters who did not see the need for new forms of IP rights
suggested that developments in AI technology should be monitored to ensure needs were keeping
pace with AI technology developments. The majority of opinions requesting new IP rights focusedon the need to protect the data associated with AI, particularly ML.” USPTO AI IP policy report at 15;
Id. at 38 (“[a] smaller number of commenters did suggest a reconsideration of whether additional
protections of datasets and databases could be useful to spur investment in high-quality data ofvetted/assured provenance.”).
33

See USPTO AI IP policy report at 15.

34
Protection of Databases, European Commission (June 1, 2018), https://ec.europa.eu/digital-singlemarket/en/protection-databases; USPTO AI IP policy report at 38.
35

This includes the Joint Committee on the Research Environment (JCORE).

36

This includes U.S. Customs and Border Protection.

37

This includes the Computer Crime and Intellectual Property Section (CCIPS).

Meeting the China Challenge at 16 (“In concert with allies and like-minded countries, the U.S.
should investigate, punish, and condemn such acts and identify ways to induce changes in China’s
maneuvers through counter-espionage, law enforcement, diplomatic pressure, and professional
training in scientific integrity.”).

38

Press Release, The U.S. Department of Commerce, Statement from Secretary Ross on The
Department’s 77 Additions to the Entity List for Human Rights Abuses, Militarization of the South
China Sea and U.S. Trade Secret Theft (Dec. 18, 2020), https://www.commerce.gov/news/pressreleases/2020/12/statement-secretary-ross-departments-77-additions-entity-list-human.

39

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40Charles Barquist & Maren Laurence, How a Biden Administration Would Shape IP Policy, Law 360(Oct. 19, 2020), https://www.law360.com/articles/1319716/how-a-biden-administration-would-shapeip-policy; Sean Lyngaas, As China Tensions Mount, U.S. Officials Outline Efforts to Combat Economic
Espionage, CyberScoop (Dec. 12, 2018), https://www.cyberscoop.com/china-tensions-mount-u-sofficials-outline-efforts-combat-economic-espionage/; see also 18 U.S.C. § 1831 (regarding economic
espionage); 18 U.S.C. §1832 (regarding theft of trade secrets).

Robert Bahr, Decision on Petition: Application No. 16/524,350, U.S. Patent and Trademark Office(2020), https://www.uspto.gov/sites/default/files/documents/16524350_ 22apr2020.pdf.

41

Consistent with U.S. policy that an inventor must be a human “natural person,” in January 2020
the European Patent Office (EPO) and the UK Intellectual Property Office (UKIPO) rejected twopatent applications that identified the AI machine as the inventor. The EPO and UKIPO found that theapplications met the requirements for patentability, but they rejected the applications because theinventor was not a “human being.” See Emma Woollacott, European Patent Office Rejects World’sFirst AI Inventor, Forbes (Jan. 3, 2020), https://www.forbes.com/sites/emmawoollacott/2020/01/03/
european-patent-office-rejects-worlds-first-ai-inventor/?sh=2915e17d5cd0; Angela Chen, Can an
AI Be an Inventor? Not Yet, MIT Technology Review (Jan. 8, 2020), https://www.technologyreview.
com/2020/01/08/102298/ai-inventor-patent-dabus-intellectual-property-uk-european-patent-officelaw/; EPO Provides Reasoning for Rejecting Patent Applications Citing AI as Inventor, IPWatchdog(Jan. 28, 2020), https://www.ipwatchdog.com/2020/01/28/epo-provides-reasoning-rejecting-patentapplications-citing-ai-inventor/id=118280/.

42

43

USPTO AI IP policy report at ii-iii.

See the Chapter 15 Blueprint for Action and its associated Annex for more details on the proposed
critical areas for international alignment for the Emerging Technology Coalition. Critical Area No. 4,
as detailed in the Blueprint for Action and Annex, is “Promoting and protecting innovation, including
through intellectual property alignment.” Recognizing the importance of IP to promote and protect
innovation, the critical area proposes coordination on assistance to nations in developing strong andaligned IP regimes, coordinated efforts to stop IP theft and counter-cyberespionage, and aligning ona mutual agenda within IP-related multilateral forums.

44“To maintain our technological leadership, the United States must seek to broaden our intellectual
property ecosystem demographically, geographically, and economically.” Expanding Innovation,
USPTO (last accessed Jan. 3, 2021), https://www.uspto.gov/initiatives/expanding-innovation (quoting
USPTO Director Andrei Iancu).

45

46
Remarks by Commerce Secretary Wilbur L. Ross at the First Meeting of the National Council for
Expanding American Innovation, U.S. Department of Commerce (Sept. 14, 2020), https://www.
commerce.gov/news/speeches/2020/09/remarks-commerce-secretary-wilbur-l-ross-first-meetingnational-council; Support the National Council for Expanding American Innovation, USPTO (lastaccessed Jan. 3, 2021), https://www.uspto.gov/initiatives/expanding-innovation/national-councilexpanding-innovation/support-national-council.

“A significant proportion of lawyers are advising clients with products in the global market to patentin China, Germany, and even the U.K. instead of the U.S. The U.S. is losing the fight to be the major
center of patents, investment, and tech because it is easier and less expensive for companies to fileand ensure their patents are enforced in other countries than in the U.S.” NSCAI staff engagementwith Robert Taylor, owner of RPT Legal Strategies, PC (Oct. 8, 2020).

47

Through the standards-setting process, standards-setting bodies (e.g., ISO, IEC, IEEE, ITU, andothers) often require that patent owners self-identify patents that may be deemed essential in a
future standard. This requirement aims to ensure transparency and often requires commitments bythese patent owners to license their patents fairly, reasonably, and non-discriminatorily. However,these standards-setting bodies do not assess whether a patent is essential or not, leaving these
determinations to private companies negotiating licenses or, if there is a dispute, to courts. See IEEE
SA Standards Board Bylaws, IEEE, https://standards.ieee.org/about/policies/bylaws/sect6-7.html#loa.

48

p
480

BLUEPRINT FOR ACTION: CHAPTER 12

Blueprint for Action: Chapter 12 - Endnotes
See Chapter 15 of this report and its associated Blueprint for Action for the coordinated U.S.
national plan to support international technology efforts and its first component on shaping
international technical standards. Also see the Chapter 15 Annex for more details on proposedinternational technical standards-setting recommendations for NIST, the Department of State, andother critical Departments and Agencies. NSCAI recommends that the U.S. government provide
greater attention to and resourcing for international technical standardization efforts; increaseinteragency coordination on AI-related standards-setting; strengthen partnerships and collaboration
with the private sector, particularly through a federal advisory committee and a grant program toenable small and medium-sized U.S. AI companies to participate in international standardization
efforts; and increase international alignment with key partners and allies. See also Meeting the China
Challenge at 27.

49

50
Dai Hong, the director of China’s National Standardization Committee’s Industrial Standards
Department, stated in January 2018, as the research for China Standards 2035 was launched: “Intoday’s world, industry, technology, and innovation are developing rapidly. The new generation of
information technology industry represented by artificial intelligence, big data, cloud computing, etc.
is emergent. International technology research and development and patent distribution have notyet been completed. Global technical standards are still being formed. This offers the opportunity torealize the transcendence of China’s industry and standards.” See translated quote from January 20,
2018, on the China News Network in Emily de la Bruyère & Nathan Picarsic, China Standards 2035:
Beijing’s Platform Geopolitics and ‘Standardization Work in 2020,’ Horizon Advisory at 6 (April 2020),
https://www.horizonadvisory.org/china-standards-2035-first-report. Additionally, the GuangdongHigh People’s Court published an October 2013 opinion piece that argued “for Chinese enterprises
to make a revival, there is only one road to take: strengthen our capacity for innovation, and only
by gaining control over SEPs can Chinese companies avoid being ‘led by the nose.’” It cited Chief
Judge Qiu Yongqing, who ruled against the U.S. firm InterDigital in its lawsuit against Huawei andargued that “Chinese enterprises should bravely employ anti-monopoly lawsuits to break technology
barriers and win space for development.” See David Cohen & Douglas Clark, China’s Anti-Monopoly
Law as a Weapon Against Foreigners, IAM-media (Nov./Dec. 2018), https://kidonip.com/wp-content/
uploads/2018/11/IAM92_China-anti-monopoly_section_0.pdf.

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INTELLECTUAL PROPERTY

51
Jeanne Suchodolski, et al., Innovation Warfare, North Carolina Journal of Law & Tech at 201 n. 130(Dec. 2020), https://ncjolt.org/articles/volume-22/volume-22-issue-2/innovation-warfare/ (China’s
firms recognize the strategic importance of standards-setting activities and that participation in
those forums provides the legal means to both access and influence developing technologies). “Inrecent years the PRC government decided that promoting Chinese standards in global standards
bodies via the work of Huawei and other Chinese companies is key to realizing techno-nationalist
goals for technological ascension. Viewed in this context, Huawei is in the vanguard of the Chinese
effort to establish dominance in both the number and significance of Chinese patents that are
deemed “standard essential” to 5G standards … it is in the U.S. interest to deflect Beijing’s attempt
to dominate the standard-setting process.” See Meeting the China Challenge at 29. See also Matthew
Noble, et al., Determining Which Companies Are Leading the 5G Race, IAM (July/August 2019),
https://www.twobirds.com/~/media/pdfs/news/articles/2019/determining-which-companies-areleading-the-5g-race.pdf.

Over-declaration is already present in 5G. See Matthew Noble, et al., Determining WhichCompanies Are Leading the 5G Race, IAM (July/August 2019), https://www.twobirds.com/~/media/
pdfs/news/articles/2019/determining-which-companies-are-leading-the-5g-race.pdf.

52

53Countries are increasingly seeking to attract inventors by setting favorable global royalty rates (seethe U.K.’s decision in Unwired Planet v. Huawei ) or by controlling the jurisdiction in which companies
may file for injunctive relief or pursue litigation. For example, licensing disputes have recently led toadditional satellite litigation involving broader issues of international law and comity between China
and other legal jurisdictions. Experts predict disputes to increase and warn of cycle of anti-suit, “antiantisuit,” and “anti-anti-antisuit” injunctions. See Mark Cohen, Wuhan and Anti-Suit Injunction, China
IPR Blog (Dec. 28, 2020), https://chinaipr.com/2020/12/28/wuhan-and-anti-suit-injunctions/; Dani
Kass, FRAND Rate ‘Nightmare’ Raises Call For International Tribunal, Law360 (Jan. 14, 2021), https://www.law360.com/articles/1343824/frand-rate-nightmare-raises-call-for-international-tribunal/; Michael
Renaud, et al., Key Considerations for Global SEP Litigation– Part 1, National Law Review (Oct. 30,2019), https://www.natlawreview.com/article/key-considerations-global-sep-litigation-part-1; Michael
Renaud, et al., Key Considerations for Global SEP Litigation– Part 2, National Law Review (Nov. 5,2019), https://www.natlawreview.com/article/key-considerations-global-sep-litigation-part-2; Zhao
Qishan & Lu Zhe, Statistics of Chinese SEP Cases in 2011-2019, LexField (2020), https://chinaipr2.
files.wordpress.com/2020/07/statistics-of-chinese-sep-cases-in-2011-2019-lexfield9892.pdf.

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BLUEPRINT FOR ACTION: CHAPTER 13

Chapter 13:
Microelectronics
Blueprint for Action

Regaining microelectronics leadership requires meeting an explicit objective: Stay at least
two generations ahead of China in state-of-the-art microelectronics and maintain multiple
sources of cutting-edge microelectronics fabrication in the United States. To do this,the Executive Branch must prepare and implement a National Microelectronics Strategy
while Congress simultaneously institutes new tax credits, subsidizes the construction ofsemiconductor manufacturing facilities, and grows federal microelectronics R&D and
infrastructure funding. Achieving this goal will require roughly $30 billion in additional
federal funding, but these funds should attract more than five times as much privatesector investment. Additional federal funding on this scale will likely boost economic
activity domestically and could add more than $100 billion to U.S. gross domestic product(GDP).1 Inside the U.S. government, agencies must also expand access to trustworthy,
high-performance microelectronic components by shifting from serial to concurrent
development of hardware and software to catch up to the commercial sector and makeuse of new microelectronics produced in the United States.
Five-Year Microelectronics Fundingp483

Category

AmountFederal Grants for Microelectronics
Manufacturing*$3 billion per project ($15 billion total)

Microelectronics R&D$12 billion

Microelectronics Infrastructure$7 billion

DoD Trusted & Assured Microelectronics$0.5 billion

Total$35 billion

MICROELECTRONICS

Recommendation: Issue an Executive Order on Microelectronics Strategy and Leadership

Recommendation

The United States needs a National Microelectronics Strategy to coordinate semiconductor
policy, funding, and incentives within the Executive Branch and externally with industry
and academia.
Actions for the President:• Issue an Executive Order on Microelectronics National Strategy and Leadership.
o The first step in rebuilding microelectronics leadership is clearly stating that
it is a Presidential priority to stay at least two generations ahead and maintainmultiple sources of cutting-edge microelectronics fabrication in the United States.
The Administration should also highlight the importance of the legislatively
required National Microelectronics Strategy and create a durable structure for its
development, implementation, and revision by issuing an Executive Order requiring
the National Defense Authorization Act (NDAA)-mandated Subcommittee on
Microelectronics Leadership to lead a process to develop a clear federal strategy for
microelectronics leadership. Draft text to inform the development of an Executive
Order for this purpose is included as an Annex to this Blueprint for Action.
Recommendation: Revitalize Domestic Microelectronics Fabrication

Recommendation

Existing U.S. incentives offset the cost of semiconductor foundry construction attributableto capital expenses, operating expenses, and taxes by 10% to 15%.2 Yet additional tax
credits and subsidies are needed to make the United States a globally competitive market
for semiconductor manufacturing, especially leading-edge logic facilities. Other leading
semiconductor manufacturing nations such as South Korea, Taiwan, and Singapore offer
25% to 30% cost reduction, roughly double what the United States currently offers.3 Thisgap in incentives is one driving factor behind the lack of an advanced logic merchantfoundry in the United States. Closing the gap will encourage U.S. firms to constructfacilities domestically while also attracting foreign firms. In fact, a program of the size
described here is projected to attract roughly 14 new fabs in the United States over 10
years.4 Additionally, increasing demand in the United States for high-end semiconductor
manufacturing equipment (SME) will create new business opportunities for SME
manufacturers from allied countries, particularly Japan and the Netherlands, which could
increase their governments’ willingness to align their export control policies with U.S.
policies prohibiting the export of such equipment to China.5 A refundable investment tax
credit should be instituted in combination with funding for federal grants for the expansion,
construction, and modernization of SME authorized in the NDAA.6
Action for Congress:• Create a 40% refundable investment tax credit for domestic semiconductor
manufacturing.

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BLUEPRINT FOR ACTION: CHAPTER 13

o Congress should pass legislation establishing a 40% refundable federal investment
tax credit for semiconductor manufacturing facilities and equipment required to
produce state-of-the-art logic chips. This incentive would reduce a semiconductor
firm’s tax bill by 40% on SME and facilities through 2024, followed by reduced tax
credit rates of 30% and 20%, respectively, through 2025 and 2026. Althoughintroduced as part of the Creating Helpful Incentives to Produce Semiconductors
for America Act (CHIPS for America Act), Congress has not yet passed legislation
establishing this credit.7• Appropriate funding authorized in the FY 2021 NDAA for domestic semiconductor
manufacturing incentives, including matching funds for semiconductor fabrication
facilities.
o The FY 2021 NDAA authorizes the Secretary of Commerce to establish a Federal
financial assistance program to incentivize investment in facilities and equipmentin the United States for semiconductor fabrication, assembly, testing, advanced
packaging, or R&D. Under the program, the Secretary may authorize up to $3 billion
per project to finance the construction, expansion, or modernization of facilities andequipment for semiconductor manufacturing. Larger subsidies are also permitted
if the project significantly increases the proportion of semiconductors relevant fornational security and economic competitiveness that can be met through reliable
domestic production. However, this judgment requires the concurrence of theSecretary of Defense and Director of National Intelligence.
o Although authorized in the FY 2021 NDAA, funds have not yet been appropriatedtoward this program. Congress should appropriate at least $15 billion to subsidize
several facilities in the United States to meet the end goal of multiple state-of-theart sources for domestic fabrication.
Recommendation

Recommendation: Double Down on Funding for Research and Infrastructure to Lead the
Next Generation of Microelectronics
Four research arms of the U.S. government focused on medium- and long-term
microelectronics breakthroughs through engagement with academia and industry are
the Department of Energy (DOE), the Defense Advanced Research Projects Agency(DARPA), the National Science Foundation (NSF), and the Department of Commerce.
Their suite of existing programs, such as DARPA’s Electronics Resurgence Initiative (ERI),
are targeting the right research areas but must be expanded by an order of magnitude toachieve the necessary breakthroughs to maintain U.S. competitiveness. Additional funding
should support not only research projects, but also the capital-intensive infrastructure
for microelectronics development, including the National Semiconductor Technology
Center and advanced packaging prototyping programs authorized in the FY 2021 NDAA.
In line with the existing focus areas of these programs and the Commission’s priorrecommendations, funding should pursue breakthroughs in promising technologies suchas 3D chip stacking, photonics, carbon nanotubes, gallium nitride transistors, domainspecific hardware architectures, electronic design automation, and cryogenic computing.
In particular, funding should prioritize the development of manufacturing equipment andtools to reach 3nm and beyond at production scale. However, this funding should notsolely be directed to classical computing technologies. The U.S. government should
also support efforts to research and develop hybrid quantum-classical techniques thatp485

MICROELECTRONICS

leverage noisy intermediate-scale quantum computers. The Commission offers detailed
recommendations on this subject in Chapter 16 of this report.
Action for Congress:• Appropriate $1.1 billion for semiconductor R&D in FY 2022 and continue increasing
funding over the next five years for a total of $12 billiono Congress should appropriate an additional $1.1 billion in FY 2022. Consistent withthe amounts in the CHIPS for America Act, this funding should include $400
million for DARPA ERI, $300 million for NSF semiconductor research, and $400
million for DOE semiconductor research. These funding levels should be grownover the following five years to roughly $7 billion per year and $12 billion total.
Recognizing it will take time to build capacity among agencies to administer
programs at the necessary scale, these amounts should start at funding levels thatcan be absorbed by agencies and ramped up over time.
• Appropriate $1 billion in FY 2022 and $5 billion total over five years for the
Advanced Packaging National Manufacturing Program.
o Novel packaging techniques such as heterogeneous integration and 3D stacking—combined with domain-specific architectures—will be critical to the developmentof artificial intelligence (AI) as traditional architectures of silicon-based chipsets
encounter diminishing marginal performance improvements. Congress should
also appropriate $1 billion in initial FY 2022 funding to establish the Advanced
Packaging National Manufacturing Program led by the National Institute ofStandards and Technology (NIST), as authorized by the FY 2021 NDAA.8 This
funding should be continued through FY 2027 for a total of $5 billion.
• Appropriate $100 million in FY 2022 and $2 billion over five years to establish the
National Semiconductor Technology Center.
o A National Semiconductor Technology Center would serve as a microelectronics
research hub while also conducting prototyping of advanced semiconductors inpartnership with the private sector. Early-stage semiconductor startups currently
face difficulties scaling due to the high costs of microelectronics design andfabrication. The incubator component of the center could provide resources topromising, early-stage microelectronics startups while also giving them accessto fabrication facilities, design tools, and shared intellectual property (IP) toassist with early-stage development costs. It could also partner with the U.S.
International Development Finance Corporation (DFC) to provide loan guarantees
to microelectronics firms if DFC’s authorities are expanded and extended to rebuild
domestic supply chains for a broader range of strategic emerging technologies.9
This laboratory could grow into a center of expertise in high-performing, trusted
microelectronics, ensuring continued U.S. leadership in this field over the ensuing
years.
Recommendation: Continue DoD’s Trusted Microelectronics Program and Adopt Agile
Hardware Development

Recommendation

Semiconductor manufacturing has moved offshore, expanding threat vectors to hardware
security and leaving the U.S. government unable to trust sensitive electronic components
it needs for defense systems. And while the U.S. government is now recognizing that itp
486

BLUEPRINT FOR ACTION: CHAPTER 13

must take steps to adopt modern software practices, there has been less attention on
incorporating hardware into the agile development process. Both issues require attention
from the Department of Defense (DoD) and other government agencies. The U.S.
government needs to inject security and agility into its microelectronics acquisition anddevelopment process to leverage the best technology possible for defense systems.
Actions for the Department of Defense:• Continue growing the Trusted & Assured Microelectronics Program to include AIenabling hardware.
o DoD’s Trusted and Assured Microelectronics research, development, test, andevaluation (RDT&E) funding has grown to more than $500 million annually foradvanced component development and prototyping and system development
and demonstration.10 These programs improve access to advanced packaging andtesting; support the development of quantifiable assurance and secure design;develop foundry access standards; expand access to non-complementary metaloxide semiconductor state-of-the-art microelectronics; support disruptive R&D;
and promote education and workforce development. These are foundational
microelectronics capabilities that will also enable the development and applicationof AI and machine learning (ML) capabilities across national security mission
areas. In FY 2021 and beyond, USD(R&E) should expand the program to focus
on developing AI-enabling capabilities and apply $50 million of funding toward
developing AI multi-chip packages.
• Shift to a more agile approach to hardware development and procurement.
o Just as agile development has transformed software, there is an opportunity tobring agile hardware design practices to speed development cycles, lower costs,
and increase performance. Rather than designing through a serial process, thecommercial sector has developed best practices to integrate hardware and softwaredevelopment processes concurrently. While DoD has made strides in agile softwaredevelopment, it remains behind the commercial sector in applying these lessons
to hardware. Broader adoption of hardware emulation and moving to a commonand secure design environment for the chip, package, and board would also
accelerate system development and improve security. This requires the combined
efforts of USD(R&E) and USD(A&S) to continue improving software acquisition anddevelopment practices to incorporate hardware.

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Blueprint for Action: Chapter 13 - Endnotes
Sparking Innovation: How Federal Investment in Semiconductor R&D Spurs U.S. Economic Growthand Job Creation, Semiconductor Industry Association at 2 (June 2020), https://www.semiconductors.
org/wp-content/uploads/2020/06/SIA _Sparking-Innovation2020.pdf; Semiconductor Incentives,
Semiconductor Industry Association at 2 (Oct. 9, 2020), https://www.semiconductors.org/wp-content/
uploads/2020/10/Incentives-Infographic-2020.pdf.

1Antonio Varas, et al., Government Incentives and US Competitiveness in Semiconductor
Manufacturing, Boston Consulting Group and Semiconductor Industry Association at 19 (Sept. 2020),
https://web-assets.bcg.com/27/cf/9fa28eeb43649ef8674fe764726d/bcg-government-incentives-andus-competitiveness-in-semiconductor-manufacturing-sep-2020.pdf.

2

3

Id.

4

Id.

5

See Chapter 14 of this report for additional details regarding export controls on SME.

Total matching funding will vary based on the number of projects approved but should have a ceilingof at least $10 billion to $15 billion.

6

7

S. 3933, 116th Cong. (2020); H.R. 7178, 116th Cong. (2020).

Pub. L. 116-283, sec. 9906, William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021).

8

See the Chapter 16 Blueprint for Action for further details on extending and expanding DFC’s loan
guarantee program through executive action.

9

10
Pub. L. 116-260, Division C, Department of Defense Appropriations Act (2021), https://docs.house.
gov/billsthisweek/20201221/BILLS-116RCP68-JES-DIVISION-C.pdf.

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BLUEPRINT FOR ACTION: CHAPTER 13

Chapter 13 Annex: Executive Order on Microelectronics Strategy
By the authority vested in me as President by the Constitution and the laws of theUnited States of America, including section 9906 of the National Defense Authorization Act(NDAA) for Fiscal Year 2021 (Public Law 116-283), it is hereby ordered as follows:
Section 1. Findings. The United States relies heavily on imports of certain
microelectronics that are vital to the Nation’s security and economic prosperity. Thisdependency on semiconductor imports creates strategic economic and militaryvulnerabilities to supply chain disruptions for electronics, including adverse foreign
government actions and natural disasters. Despite tremendous expertise in microelectronics
research, development, and innovation across the country, the United States is limited bya lack of domestically located semiconductor fabrication facilities, especially for stateof-the-art semiconductors. This limitation compounds the risk that the United States
may be outpaced in microelectronics design and fabrication. Focusing the efforts of theUnited States Government, industry, and academia to develop domestic microelectronics
fabrication facilities will reduce the Nation’s dependence on imports, preserve U.S.
leadership in technological innovation, support job creation, strengthen national security
and balance of trade, and enhance the technological superiority and readiness of theArmed Forces, which are important consumers of advanced microelectronics.
Section 2. Policy. To maintain the Nation’s security and economic prosperity, it
shall be the policy of the United States to stay at least two generations ahead of potential
adversaries in state-of-the-art microelectronics and maintain multiple sources of cuttingedge microelectronics fabrication in the United States.
Section 3. Establishment of Subcommittee on Microelectronics Leadership.
(a) There is hereby established in the National Science and Technology Council
a subcommittee on matters relating to leadership and competitiveness of the UnitedStates in microelectronics technology and innovation to be named the Subcommittee on
Microelectronics Leadership (Subcommittee).
(b) The Subcommittee shall be composed of the following members:(i) The Secretary of Commerce, who shall be Chair of the Subcommittee;(ii) The Secretary of State;(iii) The Secretary of Defense;(iv) The Secretary of Energy;(v) The Secretary of Homeland Security;p489

MICROELECTRONICS(vi) The Director of the Office of Management and Budget;(vii) The United States Trade Representative;(viii) The Director of National Intelligence;(ix) The Director of the National Science Foundation;(x) The Assistant to the President for Science and Technology;(xi) The Assistant to the President for Technology Competitiveness;(xii) The Assistant to the President for National Security Affairs;(xiii) The Assistant to the President for Economic Policy;(xiv) The Assistant to the President for Domestic Policy; and(xv) The heads of other executive departments and agencies and othersenior officials within the Executive Office of the President, as determined by the
Chair.
(c) Sunset. The Subcommittee shall terminate on January 1, 2031.
Section 4. Functions of the Subcommittee on Microelectronics Leadership.
Consistent with applicable law, the Subcommittee shall:(a) advise the President on matters involving policy affecting microelectronics;(b) develop, within 270 days of the date of this order, and no less than once every
five years thereafter, a National Strategy on Microelectronics Research, Development,
Manufacturing, and Supply Chain Security (Strategy), which shall address the following
elements:(i) methods to accelerate the domestic development and production of
microelectronics and strengthen the domestic microelectronics workforce;(ii) methods to ensure that the United States is a global leader in the fieldof microelectronics research and development;(iii) activities that may be carried out to strengthen engagement andoutreach between Federal agencies and industry, academia, and international
partners of the United States on issues relating to microelectronics;p490

BLUEPRINT FOR ACTION: CHAPTER 13(iv) priorities for research and development to accelerate the advancement
and adoption of innovative microelectronics and new uses of microelectronics andcomponents;(v) the role of diplomacy and trade in maintaining the position of the UnitedStates as a global leader in the field of microelectronics;(vi) the potential role of a Federal laboratory, center, or incubator exclusively
focused on the research and development of microelectronics, as described in
section 231(b)(15) of the NDAA for Fiscal Year 2017 (as added by section 276 of
the NDAA for Fiscal Year 2021) in carrying out the Strategy; and(vii) such other activities as the Subcommittee determines may beappropriate to overcome future challenges to the innovation, competitiveness, andsupply chain integrity of the United States in the field of microelectronics; and(c) coordinate the policymaking process with respect to microelectronics-related
research, development, manufacturing, and supply chain security activities and budgetsof Federal agencies and ensure such activities are consistent with the Strategy required bythis section.
Section 5. General Provisions. (a) If any provision of this order or the application of suchprovision is held to be invalid, the remainder of this order and other dissimilar applicationsof such provision shall not be affected.
(b) This order is not intended to, and does not, create any right or benefit,
substantive or procedural, enforceable at law or in equity by any party against the UnitedStates, its departments, agencies, or entities, its officers, employees, or agents, or any
other person.
(c) Nothing in this order shall be construed to impair or otherwise affect:(i) the authority granted by law to an executive department or agency, orthe head thereof; or(ii) the functions of the Director of the Office of Management and Budget
relating to budgetary, administrative, or legislative proposals.
(d) This order shall be implemented consistent with applicable law and subject to theavailability of appropriations.

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492

BLUEPRINT FOR ACTION: CHAPTER 14

Chapter 14:
Technology Protection
Blueprint for Action

This Blueprint for Action provides detail for how the United States must craft technology
protection policies to ensure it retains existing advantages in technology areas with national
security applicability but avoids stifling innovation. U.S. research, entrepreneurship,
and talent development remain the key ingredients of success. However, as dualuse technologies become more important to U.S. national security, the margin of U.S.
technological advantage narrows, and foreign efforts to acquire American know-how
and technology increase, the United States must also reexamine how it can protect its
commercial and academic ecosystem from foreign exploitation. The United States faces
substantial challenges in adapting its technology protection regime to address threats
related to emerging, dual-use technologies such as artificial intelligence (AI) withouthindering the free flow of commerce or its open research environment, both of which aresystemic U.S. strengths. This Blueprint for Action proposes reforms for (1) modernizing
export controls and investment screening and (2) protecting the U.S. research environment
in ways which are consistent with U.S. national security, commercial interests, and values.
Modernizing Export Controls and Investment Screening
How the U.S. Government regulates competitors’ access to sophisticated U.S. technologies
with national security applications will be one of the principal challenges of current andfuture geoeconomic competition. The United States must modernize its export control
and investment screening regimes to better address the challenges posed by dual-use
emerging technologies, to include AI. These reforms are necessary to allow the government
to implement technology protection policies in ways which maximize their impact on the
military capabilities of U.S. strategic competitors and minimize any resulting harms to U.S.
industry.
Recommendation

Recommendation: Clearly State the Overarching Principles to Guide Future U.S. Dual-Use
Technology Protection Policies
The U.S. Government must clearly state the principles that will guide future U.S. decisions
regarding policies to protect critical technologies. This will enable more consistent andcohesive technology protection policies and provide clarity to industry regarding how thegovernment intends to utilize these regulatory tools in the current competitive environment,
thereby reducing uncertainty for U.S. businesses. No such framework currently exists.

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TECHNOLOGY PROTECTION

Action for the President:• Issue an Executive Order outlining the principles which will guide U.S. policies forprotecting dual-use technologies.1
o The President should issue an Executive Order to clarify guiding principles which
will guide U.S. policies to protect critical dual-use technologies, including AI. The
Executive Order should include the following guiding principles:
■

■

■

■

U.S. technology controls will not supplant investment and innovation.
U.S. strategies to promote and protect U.S. technology leadership will be
integrated and mutually reinforcing.
The United States will be judicious in applying export controls to AI-related
technologies, targeting discrete chokepoints and coordinating policies withallies.
The United States will broaden investment screening to protect AI-related
technologies.

Recommendation: Enhance U.S. Capacity to Carry Out Effective Technology Protection
Policies

Recommendation

Departments and agencies responsible for protecting U.S. technologies lack theorganizational and technical capacity to design and implement effective policies to preventthe transfer of the national security–sensitive components of emerging technologies suchas AI. They suffer from a dearth of technical talent needed to identify effective new policies
and lack the analytical capacity to enforce their policies efficiently, especially on dualuse goods. Filling these gaps in key elements of the Executive Branch––particularly in
the Departments of Commerce, the Treasury, and State––will enhance the government’s
ability to craft targeted export controls that have the greatest strategic impact and pose theleast harm to U.S. competitiveness.
Actions for the Department of Commerce:• Designate a network of FFRDCs and UARCs to serve as a shared technical resourceon export controls.2
o To deepen its internal technical expertise, the Department of Commerce should
establish a network within existing federally funded research and development
centers (FFRDCs) and university-affiliated research centers (UARCs) to provide
technical expertise to all departments and agencies for issues relating to export
controls on emerging technologies. This network should be coordinated by the
Department of Commerce and encompass a regional distribution of FFRDCs and
UARCs that are either located in U.S. technology hubs or have significant expertisein emerging technologies.
o As an initial step, the Department of Commerce should identify the FFRDCs and
UARCs with existing expertise in emerging technologies under consideration forexport controls. This should be followed by a request for funding in the Fiscal Year
(FY) 2022 President’s Budget to support and expand work of FFRDCs and UARCs
focusing on export controls.
p494

BLUEPRINT FOR ACTION: CHAPTER 14• Require all new technology protection rules on emerging technologies to becoordinated with existing technical advisory groups that include outside experts.3
o The Secretary of Commerce should require that the Bureau of Industry andSecurity (BIS) solicit and receive feedback on any proposed controls on emerging
or foundational technologies, to include proposed rules and regulations, fromthe Emerging Technology Technical Advisory Committee (ETTAC) and any otherrelevant technical advisory groups.4 More frequent and effective use of such existing
advisory committees would provide flexible technical expertise to key departments,
help prevent publishing counterproductive controls, and ensure that policymakers
hear the perspective of industry and academia before controls go into effect.
Actions for the Departments of Commerce, the Treasury, and State:• Expedite and automate export licensing and CFIUS filing processes.5
o The Departments of Commerce and the Treasury should partner with FFRDCs,
UARCs, and other contracted entities to build an integrated, smart system foranalyzing export license applications and filings with the Committee on Foreign
Investment in the United States (CFIUS). This system should utilize AI to conduct
a preliminary analysis of filings and attempt to score levels of risk before humanreview. In the near term, this would help identify which transactions are very low risk
and which are very high risk to aid subsequent human review. In the longer term, it
could prove more accurate than human review and make decisions without human
involvement, allowing for precise, rapid, and less labor-intensive reviews.
• Encourage allies to implement legal reforms authorizing them to implement
unilateral export controls and enhance investment screening procedures.
o The Departments of State and Commerce must urge all allies which have notalready done so to pass domestic legislation to overhaul their export control
regimes, increasing their bureaucratic capacity and providing them the authorities
to implement unilateral export controls. Currently, many allies lack such domestic
legal authorities and instead defer all decisions about regulations to multilateralorganizations such as the Wassenaar Arrangement and the European Union.6
These reforms are needed to allow allies to implement targeted, rapid, and effective
export controls on emerging dual-use technologies, which are evolving quickly.
Technology protection regimes on globally available products are only as strongas their weakest link, necessitating U.S. cooperation with allies and strong allied
regulatory capacity. This builds on existing work, which has been productive andshould continue with an immediate focus on countries that have a strong domestic
emerging technology base and weak regulatory regimes.7
o The Departments of State and the Treasury should expedite efforts to enhance
the investment screening capabilities of close allies and partners. Existing efforts
have shown some success but now require increased urgency, given the threats
allies face from adversarial capital and the U.S. desire to exempt some firms inallied nations from certain CFIUS requirements.8 State and the Treasury should also
regularly share data about patterns in investment flows in the United States andallied countries to assist allied efforts to block predatory investments and illustrate
the nature of the threat.
• Ensure that the offices responsible for export controls and investment screening
policies have sufficient resources and technical capacity.
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TECHNOLOGY PROTECTION

o The Departments of Commerce, the Treasury, and State must ensure that theoffices responsible for designing and implementing export controls and investment
screening provisions on emerging technologies are sufficiently resourced andhave sufficient technical capacity. Agencies should rely on external sources
such as FFRDCs, UARCs, and advisory boards for deep technical expertise on
particular technologies. However, they also must ensure that the offices principally
responsible for managing the policy processes regarding controls on thesetechnologies have adequate staffing, resources, and baseline technical capacity tokeep pace with the rapidly evolving security challenges associated with dual-use
technologies.
Recommendation: Identify “Emerging” and “Foundational” Technologies Which Must Be
Controlled, as Required by the Export Control Reform Act of 2018

Recommendation

The Export Control Reform Act of 2018 (ECRA) and the Foreign Investment Risk Review
Modernization Act of 2018 (FIRRMA) are intended to overhaul the U.S. export control andinvestment screening regimes to better accommodate emerging technologies. ECRA
requires the Department of Commerce to develop a regular, formal interagency process
to identify “emerging and foundational technologies that … are essential to the national
security of the United States,” and are not otherwise controlled.9 Any such technologies
identified by Commerce become subject to U.S. export controls, and any foreign
investment in a U.S. company which “produces, designs, tests, manufactures, fabricates,or develops” one or more such technologies must be reviewed by CFIUS.10 This list must
be distinct from efforts within the Commission-proposed National Technology Strategy(NTS) to define emerging technologies key to U.S. national competitiveness and national
security. The ECRA list must be more narrowly defined and focused only on specifictechnologies for which export controls are necessary, whereas the TCC and NTS’ focus
should be on identifying broader technologies and particular platforms in which continued
U.S. leadership is essential.
However, as of March 2021, the Department of Commerce has yet to identify a single
emerging or foundational technology as mandated by ECRA. While there is reason to be
judicious in developing this list, given its implications on U.S. industry, and Commerce
faces legitimate capacity and resourcing limitations, the magnitude of the delay isunacceptable. The delay has garnered bipartisan criticism, created uncertainty for firms
working in fields that could be labeled as emerging or foundational technologies, anddelayed the government’s ability to either control the export of, or more importantly gain
insight into transactions involving, critical technologies that are not otherwise controlled.11
Identifying this list of technologies is critical to enabling the United States to fully implement
both ECRA and FIRRMA. As ECRA and FIRRMA are structured, until the Departmentof Commerce defines a technology which is not otherwise controlled as “emerging
and foundational” as part of this review process, with rare exceptions CFIUS cannot
require foreign companies to disclose non-controlling investments in U.S. technologyp496

BLUEPRINT FOR ACTION: CHAPTER 14

firms. Although the Commission also recommends breaking CFIUS’ reliance on this
ECRA list for mandatory disclosures (see recommendations on reforming CFIUS for
emerging technology competition, below), currently Commerce’s delay in identifying suchtechnologies is hindering the full implementation of both ECRA and FIRRMA.
Action for the Department of Commerce:• Direct the Bureau of Industry and Security to develop proposed rules containing
initial lists of both “emerging” and “foundational” technologies by December 31,
2021.12o The Secretary of Commerce should direct the BIS to work with the U.S. interagency
to develop initial versions of the lists of “emerging” and “foundational” technologies
by December 31, 2021. Beyond 2021, these lists should be regularly revised in an
iterative manner to meet ECRA’s mandate to Commerce to continually refine thelists. As part of this iterative review process, Commerce must also regularly engage
with industry as technologies develop and mature. Finalizing initial versions of theselists, if properly scoped and defined, would control critical technologies, clarify
to industry how Commerce intends to implement ECRA, and ensure that suchtechnologies are included within CFIUS.

Recommendation

Recommendation: Reform CFIUS for Emerging Technology Competition
CFIUS is not currently postured to address the range of threats that the United States
faces from adversarial capital from strategic competitors such as China and Russia.
The Department of the Treasury has little insight into Russian and Chinese investments
in U.S. emerging technology firms, as CFIUS filings are still largely voluntary for noncontrolling investments in industries such as AI, semiconductors, quantum computing, andtelecommunications equipment. While FIRRMA took positive steps in broadening CFIUS’authorities, it also left critical gaps in the investment screening regime. Additional steps
are necessary to enable CFIUS to protect sensitive U.S. industries from adversarial capital,
while ensuring the continued free flow of capital from trusted investors from allied nations.
Action for Congress:• Amend CFIUS’ authorizing legislation to require competitors to disclose
investments in “sensitive technologies” to CFIUS.
o Congress should amend CFIUS’ authorizing legislation to mandate CFIUS filings
for all non-controlling investments from “countries of special concern” in “sensitivetechnologies.” The Commission recommends that the legislation:
■

■

p
497

Define “countries of special concern” as states subject to export restrictions
pursuant to section 744.21 of title 15 within the Code of Federal Regulations(China, Russia, and Venezuela) or any state that the Secretary of State
designates as a state sponsor of terrorism (Iran, North Korea, and Syria).13
Require the Treasury Department to define a separate list of “sensitivetechnologies” for the purposes of CFIUS. Only investors from “countries of

TECHNOLOGY PROTECTION

special concern” would be required to submit CFIUS filings for investments in“sensitive technologies.” Treasury currently lacks authorities to broaden CFIUS’mandatory filing requirements, which are linked to lists of technologies that areexport controlled.14
o Mandating CFIUS filings from select competitors in a broader set of sensitive
industries—such as national security–relevant applications of AI, semiconductors,
quantum computing, and advanced telecommunications equipment—will provide
the Treasury with better visibility into Russian and Chinese investments in U.S. firms
in key sectors. This allows CFIUS to operate with more precision and insight andfocus attention on the riskiest investments.
o Additionally, de-linking CFIUS disclosure requirements from export controls
recognizes that there are instances in which it may be appropriate to screen
investments prior to enacting export controls.15 Without this change, the only way toincrease such disclosure requirements would be to place export controls on entire
industries, which would significantly hamper commerce.
Action for the Department of the Treasury:• Expedite CFIUS exemption standards for allies and partners and create fast tracks
for exempting trusted investors.
o The Department of the Treasury should issue clear guidance regarding what
investment screening policies allied nations must implement to achieve CFIUSexempted status.16 Clearly defining the standards for investment screening
mechanisms in foreign nations necessary for investors to be exempted from CFIUS
will create a powerful incentive for allied nations to adopt stronger screening
mechanisms against adversarial capital. The sooner the Treasury takes this action,
the more impact it will have on allied regulations. The Treasury should prioritize
engagement with Five Eyes intelligence-sharing partners, Japan, South Korea,
India, Israel, Singapore, Taiwan, and the European Union to enable investment fromallied nations in U.S. high-tech firms.
o Treasury should also issue new regulations creating a waiver for “trusted investors”
from foreign countries that have a strong track record of CFIUS approval to exempt
them from or lessen their CFIUS requirements. Currently there is no certification
for investors with a trusted track record, and CFIUS treats foreign investors that are
submitting for the first time the same as ones which have already submitted andbeen approved 100 times. Creating such a waiver would allow CFIUS to fast-track
investments from low-risk, trusted investors with a strong history of CFIUS approval,
facilitating legitimate foreign investment and focusing CFIUS’ resources on higherrisk investments.
Recommendation: Utilize Targeted Export Controls on Key Semiconductor Manufacturing
Equipment

Recommendation

Although the Commission believes that export controls on AI algorithms would likely be
ineffective given their widespread availability and commercial use, export controls on
specific hardware components are capable of constraining competitors’ AI capabilities
with national security applications and slowing their advancement. Policymakers mustp498

BLUEPRINT FOR ACTION: CHAPTER 14

be judicious in their application of such controls, as sweeping controls on general-use
semiconductors are likely to cause substantial damage to the U.S. semiconductor industry
and could have a net negative effect on overall U.S. competitiveness in microelectronics.
However, targeted controls on key components that only the United States—or the UnitedStates and a small group of close allies—produce which are essential for cutting-edge
defense applications could have a significant strategic impact at a relatively minimal cost.
The primary target for such controls should be select, high-end semiconductor
manufacturing equipment (SME) needed to produce high-end chipsets, particularly
photolithography equipment.17 China is the world’s largest importer of SME, accounting for
29% of global imports from 2014 to.2018, and none of the largest or most sophisticated SME
manufacturing firms are located in China.18 Simultaneous to implementing such controls,
as discussed in Chapter 13 of this report, the United States should also fund efforts toprioritize the domestic development and manufacturing of SME tools and components
needed to produce chips at scale at the 3nm node and beyond.19
Action for the Departments of Commerce and State:• Align the export control policies of the United States, the Netherlands, and Japanto restrict the export of high-end SME to China, including EUV and ArF immersion
lithography equipment.20
o The Departments of State and Commerce should work to align the export control
policies of the United States, the Netherlands, and Japan regarding high-end
SME, particularly extreme ultraviolet lithography (EUV) equipment and argon
fluoride (ArF) immersion lithography equipment, which is capable of producing
chips at the 16nm node and below.21 All three states should establish a policy ofpresumptive denial of export licenses for exports of such equipment to China.22
This should include EUV scanner tools as well as specialized components for those
tools, such as resist processing tools and EUV light sources, mirrors, and laser
amplifiers. If such controls are effective, it will be difficult for China’s government
to cultivate indigenous, cutting-edge semiconductor fabrication capabilities andwill degrade its advanced trailing-edge fabrication capabilities by complicating
equipment repairs. Coupled with the refundable investment tax credit to promote
U.S. semiconductor leadership recommended in Chapter 13 of this report, this willfurther the Commission’s proposed U.S. policy goal of remaining two generations
ahead of China in cutting-edge microelectronics design and fabrication.23• Assess the effectiveness of existing U.S. export controls on SME on China’s
semiconductor industry and assess whether targeted controls on additional
equipment are viable and necessary.
o The Departments of Commerce and State should assess the effectivenessof existing U.S. export controls on SME on China’s indigenous advanced
semiconductor industry. Pending the results of that review and whether the
Netherlands and Japan agree to align controls related to EUV and ArF immersion
equipment, the United States could subsequently consider controls on additional
SME chokepoints. If existing controls have failed to slow China’s development ofadvanced fabrication capabilities, the United States could consider implementing
controls on other targeted equipment chokepoints controlled by firms in alliedp499

TECHNOLOGY PROTECTION

countries, such as atomic layer etching tools in conjunction with Japan and theUnited Kingdom.24
Recommendation: Utilize End-Use Export Controls to Prevent Malicious Use of AI

Recommendation

Export controls that restrict transfer of dual-use items for specific end uses will not beeffective at preventing technology transfer to determined adversaries, but they can stillplay a role in preventing the involvement of U.S. firms and technology in human rights
abuses. For specific, high-end, dual-use equipment prone to facilitating uses of AI whichenable human rights abuses, such as mass surveillance, U.S. firms should be required tocertify that the equipment will not be used for specific nefarious ends and keep logs oftheir transactions. End-use controls and reporting requirements would not substantiallydelay sales and present a lower barrier to commerce compared to list-based controls.
Requiring companies to self-certify and self-report could deter U.S. firms from knowingly
enabling bad behavior abroad.
Action for the Department of Commerce:• Implement end-use controls and reporting requirements to prevent the use ofhigh-end U.S. AI chips in human rights violations.
o The Department of Commerce should implement end-use controls on high-end
U.S.-designed or -manufactured AI chips for use in mass surveillance applications
and institute reporting requirements on sales of such chips to China. The controls
should be targeted only at very high-end or specialized chips, such as specifichigh-performing GPUs, ASICs, or FPGAs that exceed a certain high-performancethreshold.25 Commerce would, by necessity, update this threshold as chips
continue to improve.
o Any firm that sells such chips to China should have to certify that the chips will not
be used for any designated human rights abuses. Firms that sell such chips should
also be required to provide quarterly reports to BIS listing all chip sales, in whatquantity, and to which company. This will facilitate U.S. government tracking ofchips that are most likely to facilitate abusive uses of AI and deter companies fromselling chips to businesses that they know are engaging in such behavior.26
Protecting the U.S. Research Environment
The United States needs comprehensive and resourced interagency measures to counter
adversarial threats to its research environment, especially from China. Efforts must be
supported by technically versed intelligence collection, analysis, and dissemination on
threats in the Science & Technology (S&T) space. Promising steps have been initiatedthrough the National Counterintelligence Task Force and the Office of Science andTechnology Policy.27 However, it is imperative to holistically improve the way the government
postures itself and equips the research community—in academia and the private sector—
to counter threats and uphold the integrity of open research.

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Recommendation

Recommendation: Build Capacity to Protect the Integrity of the U.S. Research Environment
Actions for Congress:• Pass a modified version of the Academic Research Protection Act.28
o Congress should pass the Academic Research Protection Act (ARPA) with amodification that would mandate and execute standardization of grant processes
across federal research-funding agencies.29■■

The ARPA would establish a National Commission on Research Protection;
establish an open-source intelligence clearinghouse relating to foreign
threats to academia overseen by the Director of National Intelligence; improveguidance from the Departments of State and Commerce on export control
responsibilities; and develop a Federal Bureau of Investigation (FBI) outreachstrategy to promote information sharing on threats to the academic community.
The proposed modification would mandate development and implementationof a uniform application process and database across all Executive agencies
that award R&D grants. This would enable effective oversight by grantawarding agencies, allow for automated auditing, and support investigative
efforts by federal law enforcement.

• Establish a government-sponsored independent entity focused on research
integrity.
o Congress should authorize the sponsorship of a university-affiliated research
center (UARC) to act as a center of excellence on research integrity and provide
information and advice on research security.
o The entity should bridge the gap between the government and academic andprivate-sector research institutions and lower the barriers for research organizations
to independently conduct compliance and informed risk assessments.
o The UARC mandate should be to:
■

■

■

■

■

■

p
501

Maintain open-source materials to serve university vetting of international
engagement and risk management, including databases and risk-assessment
tools;
Provide tailored guidance to research organizations for decision support;
Conduct comprehensive studies and regular reports on the state of foreign
influence on U.S. research;
Undertake independent investigations on research integrity;
Develop education materials and tools for U.S. research institutions to build
annual training and compliance initiatives; andManage dialogue with stakeholder communities and provide a venue for
information sharing.

TECHNOLOGY PROTECTION

Action for the Director of National Intelligence:• Strengthen channels for information sharing with the research community.
o In concert with the open-source intelligence clearinghouse relating to foreign
threats to academia directed by the ARPA legislation, the Director of National
Intelligence should support increased information and intelligence sharing withdesignated personnel at research organizations to share actionable information on
specific threats. This would provide organizations the ability to swiftly take steps tomitigate risks.
Recommendation: Coordinate Research Protection Efforts Internationally with Allies andPartners

Recommendation

The United States should build a coalition of like-minded nations committed to the principleof open fundamental research and the associated values of research integrity—sidelining
nations and organizations that do not abide by the values that provide the foundation forinternational innovation and science cooperation.30
Action for the Office of Science and Technology Policy:• Foster international dialogue around research protection and integrity.
o The Office of Science and Technology Policy, through the National Science andTechnology Council, should work in coordination with Department of State’s
Office of Science and Technology Cooperation and Office of the Science andTechnology Adviser to foster discussions with like-minded allies and partners
focused on mitigating detrimental academic collaboration with China’s People’s
Liberation Army (PLA)-affiliated and other high-risk entities. This should involve theestablishment of an annual meeting of relevant education, science, and industry
ministers to deepen research collaboration and coordinate on issues related tointellectual property and research security.
Action for the Department of Justice:• Strengthen information-sharing venues.
o The Department of Justice (DOJ) and FBI, in coordination with Intelligence
Community partners, should strengthen channels for information sharing on threats
and best practices on research protection and coordinate multilateral responses toenforce research security.
Action for the Department of State:• Reinforce global norms around a commitment to open fundamental research.
o Through international dialogues on research security and associated diplomacy, theDepartment of State should reinforce global norms around commitment to openp502

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fundamental research,31 as described in the United States in the National Security
Decision Directive (NSDD)-189, the National Policy on the Transfer of Scientific,
Technical and Engineering Information.32
Recommendation

Recommendation: Bolster Cybersecurity Support to Research Institutions
Protection of research data and intellectual property from cyber-enabled theft is perhaps
the most important and actionable layer of security for the U.S. R&D environment. This isparticularly true for AI, when theft of training data or trained models essentially provides
malicious actors access to a final product. Federal investments in priority emerging
technology research areas such as AI should be accompanied by a requirement andsupport for institutions—whether academic or private sector—to implement cybersecurity
measures that adequately guard research data from cyber-enabled theft.
Actions for U.S. grant-making agencies:• Incentivize cybersecurity standards and best practices for grant-receiving research
institutions.
o U.S. grant-making agencies should provide incentives to research institutions
to ensure that necessary practices, based on the existing NIST cybersecurity
framework,33 as well as governance processes are in place to protect sensitive
research data.
■■■

Reporting structures and information flows of research institutions should be
structured to raise cybersecurity as a critical issue for senior management andfacilitate internal checks and audits. This includes senior leadership awarenessof cyber threats, risk assessments, and active preventive measures.
U.S. grant-making agencies should make available incentives for research
institutions that demonstrate adherence to cybersecurity standards and bestpractices.
Universities, research institutions, and other recipients of federal researchfunding should be required to periodically demonstrate that they are adhering
to cybersecurity best practices. For government-owned and -sponsoredlaboratories, adherence to best practices, such as requiring critical data to be
encrypted at rest and in transit, should be mandated and audited on a routine
basis.

• Support increased information sharing.
o Research institutions receiving federal research dollars that do not alreadyparticipate should be encouraged to join the Research and Education Networks
Information and Sharing Analysis Center (REN-ISAC)34 or an alternate ISAC, throughwhich they can share information on threats and mitigation, benefit from automatedthreat-sharing tools, and have access to peer-assessment services to strengthen
security postures.
o Similarly, research institutions should be made aware and encouraged to takeadvantage of the cybersecurity services offered by the Department of Homeland
Security’s Cybersecurity and Infrastructure Security Agency (CISA), to include
automated indicator sharing35 and enhanced cybersecurity services.36
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Action for the Federal Bureau of Investigation:• Share real-time, actionable threat information with research institutions.
o The FBI Cybersecurity Division should work closely with and share timely,anonymized threat information with REN-ISAC and research institutions to help
them take active measures to counter cyber attacks and mitigate vulnerabilities.
Action for the Department of Homeland Security:• Support research cybersecurity information sharing similar to that of criticalinfrastructure.
o The Department of Homeland Security, CISA, and National Cybersecurity andCommunications Integration Center37 should support the level of information
sharing with research institutions as they do with critical infrastructure and the
Financial Services ISAC.38
Action for the Office of Science and Technology Policy:• Support secure data storage.
o OSTP should broker commercial cloud credits39 for universities to establish
an ability to support secure data storage for research groups and laboratories
conducting work known to be of high interest to foreign adversaries. This would
provide an ability for universities to protect their sensitive research in a manner that
does not require a significant capital investment.
Recommendation: Counter Foreign Talent-Recruitment Programs

Recommendation

China uses foreign talent-recruitment programs to achieve a “high ground” of AI experts.40
Rather than pursue legitimate competition for scientific talent through attractive job offers,
China’s talent-recruitment plans are designed in a manner that contradicts U.S. norms ofresearch integrity, violates rules around disclosure, and creates vectors for technology
transfer.41 The FBI and Intelligence Community assess that “participants are often
incentivized to transfer to China the research they conduct in the United States, as wellas other proprietary information to which they can gain access.”42 There is an urgent needto reinforce standards around disclosure of conflicts of interest and commitment and tocreate mechanisms that enable a heightened level of transparency and accountability.43
This applies to researchers’ individual transparency and institutional accountability, as
well as to the government in identifying problematic affiliations and enforcing standards.
Currently, U.S. grant-making agencies lack common processes, coordination, andcompliance mechanisms to enable this level of transparency and effective oversight.44

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Action for the Office of Science and Technology Policy:• Standardize grant application and recording processes.
o The Office of Science and Technology Policy (OSTP), in coordination with the
Office of Management and Budget, should provide advice and coordination to the
Executive Branch to make uniform the grant application and recording processes
across Federal agencies that fund external research.
o OSTP should advise and coordinate with agencies to ensure agencies embrace agovernment-wide standard for grant proposal documentation, requiring machinereadable formats that facilitate automation to identify fraud.45 This would enableeffective oversight by grant-awarding agencies, allow for automated auditing, andsupport investigative efforts by federal law enforcement.
Actions for Congress:• Mandate and resource compliance operations.
o Congress should require and resource U.S. grant-making agencies to maintaincompliance operations that can enforce standardized disclosure and accountability
measures. Through periodic vetting and monitoring, grant-making agencies canprovide a layer of accountability to enforce disclosure and protection policies.46• Amend the Foreign Agent Registration Act.
o Congress should amend the Foreign Agent Registration Act (FARA)47 to require
any individual or entity involved in the recruitment of U.S. nationals for a foreign
talent program48 to register as a foreign agent. This requires Congress to add a new
category of activity to the legislation.
Actions for Department of Justice:• Update filing regulations to support an amended FARA.
o Should Congress amend FARA legislation as proposed above, DOJ, in its
implementing regulations, should identify specific information required fromindividuals involved in recruitment for foreign talent programs to ensure that the
U.S. government has adequate visibility into foreign countries’ talent recruitment
activities in the United States.
o DOJ regulations should include methods for individuals and organizations to appeal
a determination that they are subject to registration under this FARA expansion.
• Publicly identify U.S.-based entities and foreign government proxies that serve as
recruitment networks, platforms, or brokers.
o To help raise awareness among researchers and research institutions, and reinforce
transparency, Federal law enforcement and other relevant agencies should identify
entities involved in recruitment activities for foreign talent programs and require
their registration through the FARA (if amended).
o This effort must be accompanied by an associated appeal process for organizations
to contest the need to register from identification.
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Recommendation: Limit Collaboration with PLA-Affiliated Persons and Entities

Recommendation

PLA-affiliated universities and research labs send personnel abroad, with the overarching
aim to obtain knowledge that can directly feed defense research and development
priorities. Visiting scholars or students from PLA institutions often downplay their ties to themilitary or deliberately obscure affiliation by using alternate, external names for their homeinstitutions that do not mention military or defense mandates.49
The government should take actions through designation of institutions of concern andheightened visa vetting to assist universities in making risk assessments around researchcollaborations—becoming an effective partner in protecting research integrity.
Action for the Director of National Intelligence:• Create an open-source database of organizations that have a history of improper
technology transfer, intellectual property theft, or cyber espionage.50
o The Director of National Intelligence, in coordination with law enforcement
partners, should create a queryable database of academic institutions and otherorganizations that have a history of improper technology transfer, intellectual
property theft, or cyber espionage. This resource should serve the researchcommunity and inform risk assessments of research organizations when enteringcollaborative arrangements. It would represent an expansive, open-source view of
research institutions of concern, countering efforts to obscure military affiliations
through adoption of innocuous institutional aliases.
o This must be accompanied by an associated appeal process for organizations tocontest their inclusion in the database.
Action for the President:• Limit entrance of researchers with military and intelligence affiliations fromcountries of concern.
o The President should issue an order to the Secretary of State and Secretary ofHomeland Security to implement a requirement for special review of visas foradvanced-degree students and researchers with ties to research institutions
affiliated with foreign military and intelligence organizations of designated countriesof concern.51■

This should be paired with penalties that ban entry to any visa applicants foundto have intentionally obscured institutional affiliations.

Action for the Department of State:• Resource special review measures.
o Consular officers should be provided with adequate training, reference resources,
analytical support, and time to conduct the special review.

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Blueprint for Action: Chapter 14 - Endnotes
A draft text of such an Executive Order is included in an Annex to this Blueprint for Action.
This Executive Order also includes directives pertaining to most other export control–related
recommendations in this Blueprint for Action.

1

Additional details for this recommendation are also contained within the draft Executive Order
included as an Annex to this Blueprint for Action.

2

Additional details for this recommendation are also contained within the draft Executive Order
included as an Annex to this Blueprint for Action.

3

The ETTAC contains roughly 20 leading technical experts from prominent U.S. technology anddefense firms, universities, and think tanks. However, it has been underutilized by Commerce; ETTAC
did not hold a single meeting between June 2018 and May 2020. Emerging Technology Advisory
Committee, Bureau of Industry and Security (last accessed Jan. 2, 2021), https://tac.bis.doc.gov/
index.php/ettac-home.

4

Additional details for this recommendation are also contained within the draft Executive Order
included as an Annex to this Blueprint for Action.

5

The Wassenaar Arrangement, a multilateral body with 42 participating states, is the primaryinternational forum responsible for aligning policies on dual-use export controls. However, because it
operates by consensus and includes Russia, is slow to react to new technologies and developments,
and is non-binding, the Wassenaar Agreement must not be the exclusive forum in which the UnitedStates and allies negotiate export control provisions on dual-use technologies. About Us, The
Wassenaar Arrangement (last accessed Jan. 2, 2021), https://www.wassenaar.org/about-us/; Second
Quarter Recommendations, NSCAI at 68-69 (2020), https://www.nscai.gov/previous-reports/.

6

The Chapter 15 Blueprint for Action and associated Annex reinforce this recommendation andillustrate how these efforts should fit into a broader technology diplomacy strategy.

7

See Chris Darby, et al., Mitigating Economic Impacts of the COVID-19 Pandemic and Preserving U.S.
Strategic Competitiveness in Artificial Intelligence, NSCAI at 14-15 (May 19, 2020), https://www.nscai.
gov/white-papers/covid-19-white-papers/; Second Quarter Recommendations, NSCAI at 69, 75-77(July 2020), https://www.nscai.gov/previous-reports/.

8

9

50 U.S.C. § 4817(a)(1)(A).

10
50 U.S.C. § 4565(a)(4)(B)(iii)(II); 85 Fed. Reg. 3112, Provisions Pertaining to Certain Investmentsin the United States by Foreign Persons, U.S. Department of Treasury: Office of Investment Security(Jan. 17, 2020) https://www.federalregister.gov/documents/2020/01/17/2020-00188/provisionspertaining-to-certain-investments-in-the-united-states-by-foreign-persons.
11
New Controls on Emerging Technologies Released, While U.S. Commerce Department Comes
Under Fire for Delay, Gibson Dunn (Oct. 27, 2020), https://www.gibsondunn.com/new-controls-onemerging-technologies-released-while-us-commerce-department-comes-under-fire-for-delay/; Letterfrom U.S. Senators Tom Cotton and Charles E. Schumer to Secretary Wilbur Ross, Department ofCommerce (Nov. 18, 2019), https://www.cotton.senate.gov/imo/media/doc/191118_Cotton_Schumer_
ECRA%20Letter%20to%20Sec.%20Ross%20copy.pdf.
12Additional implementation details for this recommendation are also contained within the draft
Executive Order included as an Annex to this chapter.

State Sponsors of Terrorism, U.S. Department of State (last accessed Jan. 2, 2021), https://www.
state.gov/state-sponsors-of-terrorism/.

13

14
As discussed in the following recommendation, due to the Department of Commerce’s delay inidentifying export controls on “emerging and foundational technologies,” as required under the Export
Control Reform Act of 2018 (ECRA), CFIUS’ mandatory filing requirements have largely not expanded
to emerging technology industries.

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For instance, for early-stage technology venture investments, particularly those that do not
yet produce specific products, export controls have historically been ineffective, but investment
screening would still have value. See Michael Brown & Pavneet Singh, China’s Technology Transfer
Strategy, Defense Innovation Unit Experimental at 24 (Jan. 2018), https://admin.govexec.com/media/
diux_chinatechnologytransferstudy_jan_ 2018_(1).pdf.

15

CFIUS regulations released in January 2020 created an exception for non-controlling technology,
infrastructure, and data (TID) investments for investors tied to “excepted foreign states,” with
Australia, Canada, and the United Kingdom forming the initial list. The regulations require thatexcepted foreign states implement their own process to analyze foreign investments for national
security risks and to facilitate coordination with the United States on investment screening by
February 2022. However, Treasury has yet to publish the criteria CFIUS will use when determining
whether additional countries can qualify as “excepted foreign states” in the future. See 31 C.F.R.
800.218 (2020), https://home.treasury.gov/system/files/206/Part-800-Final-Rule-Jan-17-2020.pdf.

16

17
The detailed reasons why high-end SME and photolithography equipment in particular represents
the best target for such controls are described in Chapter 14 of this report.
18
John Verwey, The Health and Competitiveness of the U.S. Semiconductor Manufacturing Equipment
Industry, SSRN at 5, 8 (July 1, 2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3413951.
19
See Chapter 13 of this report and its associated Blueprint for Action for additional details on
recommendations to support the U.S. microelectronics industry, to include U.S. development of SME.
20
Additional details for this recommendation are also contained within the draft Executive Order
included as an Appendix to this chapter.
21
EUV lithography equipment is the only type of lithography equipment capable of mass
manufacturing chips at the 5nm node or potentially below. ArF immersion lithography equipment is
the only other type of tool capable of mass producing chips at the 28nm node or below, with moresophisticated ArF immersion equipment capable of nodes under 16nm. See Saif Khan, Securing
Semiconductor Supply Chains, Georgetown Center for Security and Emerging Technologies at 20(Jan. 2021), https://cset.georgetown.edu/research/securing-semiconductor-supply-chains/.
22
In 2019, the United States put significant pressure on the Netherlands to block a sale of EUV
lithography equipment from Dutch firm ASML to Chinese firm SMIC. The contract expired before
the equipment was delivered, although the Netherlands has not stated whether or not it will approve
future sales. See Alexandra Alper, et al., Trump Administration Pressed Dutch Hard to Cancel China
Chip-Equipment Sale: Sources, Reuters (Jan. 6, 2020), https://www.reuters.com/article/us-asmlholding-usa-china-insight/trump-administration-pressed-dutch-hard-to-cancel-china-chip-equipmentsale-sources-idUSKBN1Z50HN.

Increasing the competitiveness of the cutting-edge U.S. microelectronics fabrication industry would
create new market opportunities for SME firms, which could offset any potential losses resulting
from decreased access to the Chinese market due to export controls. This is particularly important
for allied governments that may be hesitant to impose export controls on equipment which will hurt
key domestic companies without simultaneously providing them access to new markets or growing
existing markets.

23

Saif Khan, Securing Semiconductor Supply Chains, Georgetown Center for Security and Emerging
Technologies at 20 (Jan. 2021), https://cset.georgetown.edu/research/securing-semiconductorsupply-chains/.

24

25
GPUs are graphics processing units, ASICs are application-specific integrated circuits, and FPGAs
are field-programmable gate arrays.
26
The Chapter 15 Blueprint for Action reinforces this recommendation and illustrates how these efforts
should fit into a broader technology diplomacy strategy.

Specifically, the Joint Committee on Research Environments within the National Science andTechnology Council. See NSTC, The White House (last accessed Jan. 1, 2021), https://www.
whitehouse.gov/ostp/nstc/.

27

H.R. 8346, Academic Research Protection Act, 116th Cong. (2020), https://www.congress.gov/
bill/116th-congress/house-bill/8346.

28

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Blueprint for Action: Chapter 14 - Endnotes
This could mirror a provision for development of a uniform grant application process acrossresearch-funding agencies proposed in S. 3997, Safeguarding American Innovation Act, 116th Cong.
(2020), https://www.congress.gov/bill/116th-congress/senate-bill/3997/text.

2930
Notably, two-thirds of overseas professional associations that transfer technology to China arelocated outside the United States. See Ryan Fedasiuk & Emily Weinstein, Overseas Professionals
and Technology Transfer to China, Center for Security and Emerging Technology at 2 (July 21, 2020),
https://cset.georgetown.edu/research/overseas-professionals-and-technology-transfer-to-china/.
One-third of Thousand Talents awardees are located outside the United States, mainly in the U.K.,
Germany, and Singapore. See Ryan Fedasiuk & Jacob Feldgoise, The Youth Thousand Talents
Plan and China’s Military, Center for Security and Emerging Technology at 4 (Aug. 2020), https://cset.georgetown.edu/research/the-youth-thousand-talents-plan-and-chinas-military/. Two-thirdsof awardees for some of China’s largest scholarship programs are outside the United States. See
Andrew Imbrie & Ryan Fedasiuk, Untangling the Web: Why the US Needs Allies to Defend Against
Chinese Technology Transfer, Brookings Institution at 3 (April 2020), https://www.brookings.edu/
research/untangling-the-web-why-the-us-needs-allies-to-defend-against-chinese-technologytransfer/. Leaders in Canada, the Netherlands, U.K., Japan, and India have in recent years publicly
raised concerns around security risks related to research collaborations with China. Remco Zwetsloot,
China’s Approach to Tech Talent Competition: Policies, Results, and the Developing Global Response,
Center for Security and Emerging Technology at 8 (April 2020), https://cset.georgetown.edu/research/chinas-approach-to-tech-talent-competition-policies-results-and-the-developing-global-response/.

This could build on a concept currently under consideration by the National Science Foundation
to establish and formalize an international code of conduct around shared principles in research
integrity and then fund collaborative research in accordance with said principles.

31

32
The directive defines fundamental research as: “‘Fundamental research’ means basic andapplied research in science and engineering, the results of which ordinarily are published andshared broadly within the scientific community, as distinguished from proprietary research and fromindustrial development, design, production, and product utilization, the results of which ordinarily
are restricted for proprietary or national security reasons.” The key provision of NSDD-189 remains
today: “It is the policy of this Administration that, to the maximum extent possible, the products offundamental research remain unrestricted. It is also the policy of this Administration that, where thenational security requires control, the mechanism for control of information generated during federally
funded fundamental research in science, technology and engineering at colleges, universities andlaboratories is classification.” National Policy on the Transfer of Scientific, Technical and Engineering
Information, NSDD-189 (Sept. 21, 1985), https://fas.org/irp/offdocs/nsdd/nsdd-189.htm.
33

Cybersecurity Framework, NIST (last accessed Feb. 1, 2021), https://www.nist.gov/cyberframework.

34

REN-ISAC (last accessed Jan. 2, 2021), https://www.ren-isac.net/.

Automated Indicator Sharing, Cybersecurity and Infrastructure Security Agency (CISA) (lastaccessed Feb. 10, 2021), https://www.cisa.gov/automated-indicator-sharing-ais.

35

36
Enhanced Cybersecurity Services (ECS), Cybersecurity and Infrastructure Security Agency (lastaccessed Feb. 10, 2021), https://www.cisa.gov/enhanced-cybersecurity-services-ecs.
37

Cyber Incident Response, CISA (Oct. 27, 2020), https://www.cisa.gov/cyber-incident-response.

Information Sharing and Awareness, CISA (Dec. 8, 2020), https://www.cisa.gov/information-sharingand-awareness.

38

The National Science Foundation’s CloudBank program could be leveraged as a model. See
CloudBank, https://www.cloudbank.org/.

39

William C. Hannes & Huey-meei Chang, China’s Access to Foreign AI Technology, Center for
Security and Emerging Technology (CSET) at 9-10 (Sept. 2019), https://cset.georgetown.edu/wpcontent/uploads/CSET_China_ Access_To_Foreign_ AI_Technology.pdf.

40

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TECHNOLOGY PROTECTION

The Office of Science and Technology Policy defines foreign government talent-recruitment
programs as “an effort directly or indirectly organized, managed, or funded by a foreign government
to recruit science and technology professionals or students (regardless of citizenship or national
origin).” Enhancing the Security and Integrity of America’s Research Enterprise, Office of Scienceand Technology Policy at 18 (June 2020), https://trumpwhitehouse.archives.gov/wp-content/
uploads/2020/07/Enhancing-the-Security-and-Integrity-of-Americas-Research-Enterprise.pdf.
41

Testimony of John Brown, Assistant Director, Counterintelligence Division, Federal Bureau of
Investigation, delivered before the U.S Senate Committee on Homeland Security and Governmental
Affairs, Permanent Subcommittee on Investigations, Hearing on Securing the U.S. ResearchEnterprise from China’s Talent Recruitment Plans at 2 (Nov. 19, 2019), https://www.hsgac.senate.gov/
imo/media/doc/Brown%20Testimony.pdf. In some cases, the Chinese government appears to haverewarded scientists caught stealing technology through talent-recruitment programs, Alex Joske,
Hunting the Phoenix, Australian Strategic Policy Institute at 8 (2020), https://www.jstor.org/stable/
resrep26119.1.

42

43
A National Science Foundation-commissioned JASON study on fundamental research security
found that “disclosure of activities presents our main defense against foreign influence, especially thatinvolving rewards, deception, and coercion.” Fundamental Research Security, JASON at 31 (Dec. 6,2019), https://www.nsf.gov/news/special_reports/jasonsecurity/JSR-19-2IFundamentalResearchSecur
ity_12062019FINAL.pdf.

Threats to the U.S. Research Enterprise: China’s Talent Recruitment Plan, U.S. Senate Permanent
Subcommittee on Investigations (Nov. 2019), https://www.hsgac.senate.gov/imo/media/doc/2019-1118%20PSI%20Staff%20Report%20-%20China’s%20Talent%20Recruitment%20Plans.pdf.

44This mirrors a recommendation from the U.S. Senate Permanent Subcommittee on Investigations.
See Threats to the U.S. Research Enterprise: China’s Talent Recruitment Plan, U.S. Senate Permanent
Subcommittee on Investigations at 11 (Nov. 2019), https://www.hsgac.senate.gov/imo/media/
doc/2019-11-18%20PSI%20Staff%20Report%20-%20China’s%20Talent%20Recruitment%20Plans.pdf.

45

46
The National Institutes of Health’s recent investments in this capability could serve as a model forothers, scaled in terms of an agency’s level of funding.
47

22 U.S.C. § 611 et seq.

This will require a clear definition of a foreign talent program, distinct from standard internationally
funded research opportunities. The Office of Science and Technology Policy defines foreign
government talent recruitment programs as “an effort directly or indirectly organized, managed,
or funded by a foreign government to recruit science and technology professionals or students(regardless of citizenship or national origin).” Enhancing the Security and Integrity of America’s
Research Enterprise, Office of Science and Technology Policy at 18 (June 2020), https://
trumpwhitehouse.archives.gov/wp-content/uploads/2020/07/Enhancing-the-Security-and-Integrity-ofAmericas-Research-Enterprise.pdf.

48

Glenn Tiffert, Global Engagement: Rethinking Risk in the Research Enterprise, The Hoover
Institution at 12 (2020), https://www.hoover.org/sites/default/files/research/docs/tiffert_
globalengagement_full_0818.pdf.

49

If Congress passes the Academic Research Protection Act, this initiative could be a component of
the open-source intelligence clearinghouse on threats to academia created through the legislation.

50

51
This is recommended as an update to Presidential Proclamation 10043 that automatically suspends
F or J visas to study or conduct research for Chinese nationals affiliated with the Chinese government
military-civil fusion strategy. See Donald J. Trump, Proclamation on the Suspension of Entry as
Nonimmigrants of Certain Students and Researchers from the People’s Republic of China, The WhiteHouse (May 29, 2020), https://trumpwhitehouse.archives.gov/presidential-actions/proclamationsuspension-entry-nonimmigrants-certain-students-researchers-peoples-republic-china/. This orderwould provide for a case-by-case, risk-based review of potentially concerning applications from a
broader group of designated countries.

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BLUEPRINT FOR ACTION: CHAPTER 14

Chapter 14 Annex: Technology Protection
Draft Executive Order on Export Control on Principles Guiding U.S. Policies for Protecting
Dual-Use Technologies
By the authority vested in me as President by the Constitution and the laws of
the United States of America, and in order to promote U.S. innovation and leadership in
emerging and foundational technologies while protecting U.S. national security, it is hereby
ordered as follows:
Section 1. Policy. It is the policy of the United States that export controls andinvestment screening mechanisms must be used in targeted, clearly defined, and strategic
ways to protect U.S. national security, in pursuit of the broader policy of promoting U.S.
innovation and leadership in emerging and foundational technologies, to include dual-use
technologies such as artificial intelligence (AI).
The United States must be tailored and discrete in implementing export controls on dualuse emerging technologies such as AI. To ensure maximum effectiveness and minimize theadverse impact on U.S. industry, the U.S. Government should be guided by the following
principles:(1) Principle One: Export Controls Cannot Supplant Investment andInnovation. Technology protection policies are intended to slow U.S. competitors’ pursuitand development of key strategic technologies for national security purposes, not stopthem in their tracks. The United States must cultivate investment in these technologies
through direct federal funding or changes to the regulatory environment in order to preserve
existing U.S. advantages.
(2) Principle Two: U.S. Strategies to Promote and Protect U.S. Technology
Leadership Must Be Integrated. The U.S. strategy to protect emerging technologies,
including but not limited to AI, must be integrated with targeted efforts to promote U.S.
leadership in such technologies. When choosing to implement controls, the United Statesshould simultaneously consider policies to spur domestic research and development(R&D) in key industries to partially offset the resulting costs to U.S. firms, create alternative
global markets, or encourage new investment to strengthen the U.S. industrial position.
(3) Principle Three: Export Controls Must Be Targeted, Strategic, andCoordinated with Allies. In devising new export controls on widespread and dualuse technologies such as AI, the United States must be careful and selective in theimplementation of export controls. To ensure maximum effectiveness and minimize theadverse impact on U.S. industry, the U.S. Government should be guided by the following
three-part test:a.
Export controls must be targeted, clearly defined, and focusedon choke points where they will have a strategic impact on the national security
capabilities of competitors but smaller repercussions on U.S. industry.
p
511

TECHNOLOGY PROTECTIONb.
Export controls must have a clear strategic objective, seeking
to deter competitors from pursuing paths that endanger U.S. national security
interests, and account for the projected cost and timeframe for competitors tocreate a domestic alternative.
c.
Export controls must be coordinated with key U.S. allies which
are also capable of producing the given technology, in order to effectively restrict
the supply to adversaries and also prevent circumstances in which unilateralcontrols cut off U.S. market access but competitors are able to purchase the same
technology from other countries.
(4) Principle Four: The United States will be judicious in its use of export
controls but broaden investment screening on critical and emerging technologies.
While broad and sweeping export controls on AI and other dual-use emerging technologies
could result in significant blowback on U.S. industry, which would harm overall U.S.
strategic competitiveness, investment screening presents opportunities to take a moreproactive regulatory approach while minimizing risk to U.S. industry. Provided the UnitedStates can continue approving benign transactions expeditiously, enhancing investment
screening presents significant potential to blunt concerning transfers of technology.
Section 2. Objective. In 2018, the Congress enacted the Export Control ReformAct of 2018 (ECRA) and the Foreign Investment Risk Reduction Modernization Act of 2018
(FIRRMA) to provide the U.S. Government with additional mechanisms to control exports
and screen investments. The U.S. Government must take steps to provide the private sector
and foreign governments with clarity about the application of these laws to emerging andfoundational technologies and enhance U.S. national security in the process.
Section 3. Establishment of Interagency Task Force on Emerging andFoundational Technologies. (a) Pursuant to Section 1758 of ECRA, there is hereby
established an Interagency Task Force on Emerging and Foundational Technologies (Task
Force) to identify emerging and foundational technologies that are essential to the national
security of the United States and are not critical technologies described in clauses (i)through (v) of 50 U.S.C. 4565(a)(6)(A).
(b) The Task Force shall be chaired by the Secretary of Commerce (Chair) andconsist of senior-level officials from the following Executive departments and agencies(agencies) designated by the heads of those agencies:(i) Department of State;(ii) Department of the Treasury;(iii) Department of Defense;p512

BLUEPRINT FOR ACTION: CHAPTER 14(iv) Department of Energy; and(vi) such other agencies as the President, or the Chair, may designate.
(c) The Chair shall designate a senior-level official of the Department of Commerce
as the Executive Director of the Task Force, who shall be responsible for regularly
convening and presiding over the meetings of the Task Force, determining its agenda,
and guiding its work in fulfilling its functions under this Order, in coordination with the BIS
at the Department of Commerce.
Section 4. Functions of the Task Force.
(a) The Task Force shall meet regularly to identify emerging and foundational
technologies that are essential to the national security of the United States for purposesof establishing export controls and investment screening mechanisms, as appropriate,
related to those technologies.
(b) Within 120 days, the Task Force shall finalize lists of emerging and foundational
technologies pursuant to section 1758 of ECRA. The Secretary of Commerce shallthereafter issue proposed rules on emerging and foundational technologies and proceed
expeditiously to issue final rules at the conclusion of the notice and comment period.
(c) The Task Force shall review the lists of emerging and foundational technologies
and issue amendments as needed on no less than an annual basis.
Section 5. Process for Identifying Emerging and Foundational Technologies.
(a) In identifying emerging and foundational technologies pursuant to this Order, the Task
Force shall consider information from multiple sources, including:(i) publicly available information;(ii) classified information, including relevant information provided by the
Director of National Intelligence;(iii) information relating to reviews and investigations of transactions by theCommittee on Foreign Investment in the United States under 50 U.S.C. 4565; and(iv) information provided by the advisory committees established by the
Secretary to advise the Under Secretary of Commerce for Industry and Security
on controls under the Export Administration Regulations, including the Emerging
Technology Technical Advisory Committee (ETTAC).
(b) In identifying emerging and foundational technologies pursuant to this Order,p
513

TECHNOLOGY PROTECTION

the Task Force shall take into account:(i) the development of emerging and foundational technologies in foreigncountries;(ii) the effect that export controls imposed pursuant to this section may
have on the development of such technologies in the United States;(iii) the effectiveness of export controls imposed pursuant to this section
on limiting the proliferation of emerging and foundational technologies to foreigncountries; and(iv) the policy and principles reflected in section 1 of this Order.
Section 6. Improving Coordination with Expert Advisory Groups. (a) The
Secretary of Commerce shall review existing technical advisory committees (TACs) at the
Department of Commerce, including the ETTAC, to ensure that each TAC is composed ofmembers from industry and academia with deep subject-matter expertise to assess theneed for export controls for emerging and foundational technologies.
(b) The Secretary of Commerce, as Chair of the Task Force, shall ensure that the
Task Force has solicited and received feedback from the ETTAC and other relevant TACs
at the Department of Commerce on the text of any proposed or final rule on emerging or
foundational technologies, prior to issuance of such rule.
(c) The Secretary of Commerce shall ensure that senior officials at the Departmentsof State and the Treasury are granted non-voting observer access at all ETTAC meetings.
Section 7. Improving International Coordination on Export Controls on
Semiconductor Manufacturing Equipment. Within 180 days, the Secretary of State,in consultation with the Secretary of Commerce and the Secretary of Defense, shall hosta multilateral engagement with senior-level representatives of Japan, the Netherlands,
and, if deemed appropriate, other U.S. allies and partners that produce semiconductor
manufacturing equipment (SME), including EUV lithography equipment and ArF immersion
lithography equipment, listed by the Wassenaar Arrangement or identified by the Task
Force. The purpose of this meeting will be to align export licensing policies toward apresumptive denial of export licenses for exports of semiconductor manufacturing
equipment to China. The Secretary of State shall provide a report to the President within
60 days of the meeting assessing:(i) whether U.S. allies and partners are currently exporting such equipment
to China;p514

BLUEPRINT FOR ACTION: CHAPTER 14(ii) what steps each country that manufactures such equipment must taketo ensure its regulatory regime is aligned with that of the United States, and its
willingness to take those steps; and(iii) whether additional opportunities exist to strengthen international
cooperation on export controls on SME which are consistent with the policy andprinciples reflected in Section 1 of this Order.
Section 8. Engaging Technical Experts for Export Control Review. (a) The
Secretary of Commerce, in consultation with the Secretaries of the Treasury and Defense,shall establish a network within existing Federally Funded Research and Development
Centers (FFRDCs) and University Affiliated Research Centers (UARCs) to provide
technical expertise to all departments and agencies for issues relating to export controls
and investment screening related to emerging and foundational technologies. The network
shall encompass a regional distribution of FFRDCs and UARCs located in areas of theUnited States with a concentration of technology expertise in emerging and foundational
technologies.
(b) Individuals selected to participate in the network shall provide real-time
technical input to all policy discussions on export controls and review of export control
license applications, including those of the Task Force, those conducted pursuant to EO
12981 or a successor order, and any other interagency policy discussions pertaining
to export controls, as well as the investment screening processes of the Committee on
Foreign Investment in the United States (CFIUS).
Section 9. Automating Export Control and Investment Screening Reviews.
The Secretaries of Commerce and the Treasury shall task the aforementioned networkwith exploring using AI-based systems to assist in the evaluation of applications for export
control licenses and CFIUS filings and shall provide a report to the President on the useof AI-based systems for such purposes within 180 days. This report shall include anevaluation of:(i) how AI-based systems could assist existing review processes;(ii) whether incorporating such systems could enhance the accuracy andspeed of the review processes;(iii) whether relevant Departments and Agencies have sufficient quantityand quality of data to train AI-based review systems, and how existing data canbe improved;(iv) what information technology infrastructure inside relevant Departments
and Agencies needs to be improved to fully utilize such systems; andp
515

TECHNOLOGY PROTECTION(iv) an approximate timeline and cost for deploying a system or systems,
and the projected savings per year in labor-hours once deployed.
Section 10. General Provisions. (a) Nothing in this Order shall be construed to
impair or otherwise affect:(i) the authority granted by law, regulation, Executive Order, or Presidential
Directive to an executive department, agency, or the head thereof; or(ii) the functions of the Director of the Office of Management and Budget
relating to budgetary, administrative, or legislative proposals.
(b) This order shall be implemented consistent with applicable law and subject tothe availability of appropriations.
(c) This order is not intended to, and does not, create any right or benefit,
substantive or procedural, enforceable at law or in equity by any party against the UnitedStates, its departments, agencies, or entities, its officers, employees, or agents, or any
other person.

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BLUEPRINT FOR ACTION: CHAPTER 15

Chapter 15:
A Favorable International
Technology Order
Blueprint for Action

This Blueprint for Action provides detail for a comprehensive strategy to further U.S.
interests with allies and partners to shape a favorable international technology order, win
the technology competition against authoritarian states, and advance artificial intelligence(AI) innovation and adoption across the world to promote the values of free and open
societies. This Blueprint for Action also proposes reforms to reorient U.S. foreign policy
and the Department of State for great power competition in the digital age.
Recommendation

Recommendation: Develop an International Science & Technology Strategy
The International Science & Technology Strategy (ISTS) will help coordinate
emerging technology policies across the government and with our closest allies andpartners; apply the tools of foreign assistance, technical expertise and guidance,and development finance and investment; and foster collaborative R&D. The ISTS
should serve as the international component of the National Technology Strategy(NTS) and provide an organizing framework to drive U.S. foreign policy withregard to emerging technologies.1 The ISTS should center on four big initiatives:• Building an Emerging Technology Coalition (ETC);• Launching an International Digital Democracy Initiative (IDDI);• Implementing a coordinated U.S. national plan to support international efforts; and• Enhancing the United States’ position as an international digital research hub.
Action for the President:• Direct development of an International Science & Technology Strategy (ISTS) by aWhite House-led interagency task force.
o The President should direct development of the ISTS by a dedicated task force.
o The ISTS Task Force should be convened by the Technology CompetitivenessCouncil or otherwise co-chaired by the Assistant to the President for Nationalp517

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

Security Affairs and the Directors of the Office of Science and Technology Policy
and the National Economic Council.
o The ISTS Task Force should include leadership from the following agencies:o the Department of State;o the Department of the Treasury;o the Department of Commerce, including the Bureau of Industry and Security (BIS)
and the National Institute of Standards and Technology (NIST);o the Department of Energy (DOE);o the Department of Homeland Security’s Cybersecurity & Infrastructure Security
Agency (CISA);o the National Science Foundation (NSF);
o the United States Agency for International Development (USAID);o the U.S. International Development Finance Corporation (DFC);o the Export-Import Bank of the United States (EXIM);o the U.S. Trade and Development Agency (USTDA);o the Millennium Challenge Corporation (MCC); ando as appropriate, other agencies with expertise on individual topics.
o The ISTS Task Force should develop and submit to the President a formal strategy,
linked closely to the President’s National Security Strategy (NSS) and the Secretaryof State’s and USAID Administrator’s Joint Strategic Plan (JSP), building on those
documents’ technology-related goals and priorities. The ISTS should serve as theinternational component of the National Technology Strategy.2
o The ISTS should be centered around four big initiatives addressed in this Plan:■building an Emerging Technology Coalition;■

launching an International Digital Democracy Initiative;■■

implementing a comprehensive U.S. national plan to support international
digital efforts around technical standards, foreign assistance, development
finance, and export controls; andenhancing the United States’ position as an international digital research hub.

o Once approved by the President, the ISTS Task Force would be responsible foroverseeing and supporting the implementation, to include identifying resource andorganizational changes needed to implement the strategy. The ISTS Task Forceshould hold regular meetings to facilitate execution of the strategy.

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Recommendation

Recommendation: Build an Emerging Technology Coalition
As part of the ISTS, the United States, led by the White House and the Departmentof State, should lead in forming an Emerging Technology Coalition (ETC) of
countries respectful of democratic values. The ETC would be a body of likeminded allies and partners to work with each other and with help from internationaland non-governmental organizations, civil society actors, and the private sector
to develop and implement a coordinated strategy and associated policies to:
1. promote the design, development, and use of emerging technologies according todemocratic norms and values;
2. coordinate policies and investments to counter the malign use of these technologies byauthoritarian regimes; and3. provide concrete, competitive alternatives to counter the adoption of digital
infrastructure made in China.
Action for the White House and the Department of State:• Convene key allies and partners to join and establish the ETC.
o The United States should lead an ETC of like-minded nations either as part of alarger democracy summit or as a stand-alone endeavor.
o Membership should include a core group of technologically advanced democraticnations, reflecting a broad geographic distribution, that have demonstrated
shared interests in advancing responsible AI, countering malign uses of emerging
technologies, and ensuring high standards for openness, trust, and privacy in digital
infrastructure.
■■

The ETC should build on two important dialogues previously recommended
by the Commission: the U.S.-India Strategic Tech Alliance and the U.S.-EU
Strategic Dialogue for Emerging Technologies.3
The ETC should build on—­and be additive to—­promising efforts and projects
underway at the Organization for Economic Co-Operation and Development(OECD) and the Global Partnership on AI (GPAI).4 See Table 1. Key MultilateralTechnology Initiatives (located at the end of this Plan).

o The Commission further recommends that the ETC invite representatives frominternational organizations, non-governmental organizations, civil society,academia, and the private sector.
■

■

p
519

These organizations are critical to implement policies across borders,convene state and non-state actors, and promote alignment of responsible
AI and digital infrastructure development and use in accordance with shareddemocratic values.5
They should be included in the ETC, among participants in the inauguralsession, and should have observer status.

A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

Actions for the United States and Allies and Partners:• Organize efforts to synchronize policies and initiatives across seven critical areas.
o The ETC should be organized around a concrete agenda with actionable objectives
focused on the outcomes rather than processes, designed to develop and realizea shared vision of a positive technological future and contrast it against a future
dominated by authoritarian practices.
o Building on an existing framework of guiding principles, such as the OECD AI
Principles,6 members should use the inaugural meeting to endorse a concrete
agenda designed to operationalize policies and initiatives across seven critical areas:
■

■

■

■

■

■

■

Developing and operationalizing standards and norms in support of democraticvalues and the development of secure, reliable, and trusted technologies;
Promoting and facilitating coordinated and joint R&D on AI and digital
infrastructure that advances shared interests and benefits humanity;
Promoting democracy, human rights, and the rule of law through joint efforts
to counter censorship, malign information operations, human trafficking, andilliberal uses of surveillance technologies;
Exploring ways to facilitate data sharing among allies and partners throughenabling agreements, common data archival procedures, cooperative
investments in privacy-enhancing technologies, and addressing legal andregulatory barriers;
Promoting and protecting innovation, particularly through export controls,
investment screening, supply chain assurance, emerging technology
investment, trade policy, research and cyber protections, and intellectual
property alignment;
Developing AI-related talent by analyzing labor market challenges,
harmonizing skills and certification requirements, and increasing talentexchanges, joint training, and workforce development initiatives; andLaunching the International Digital Democracy Initiative to coordinate
international foreign assistance, development aid and financing, technicalguidance, and policy guidance.

o To execute an agenda across the seven critical areas, the ETC members should
consider creating implementation groups for each area.
o Proposed agendas and guidance for each critical area are included in the Emerging
Technology Coalition Annex to this Blueprint for Action.
Recommendation: Launch an International Digital Democracy Initiative

Recommendation

The Commission recommends that the United States and ETC partner states launch an
International Digital Democracy Initiative (IDDI), a coordinated effort to align partner states’
foreign assistance policies and programs to develop, promote, and fund the adoption of
AI and associated technologies that comport with democratic values and ethical norms
around openness, privacy, security, and reliability.7 The IDDI will:p520

BLUEPRINT FOR ACTION: CHAPTER 15• Coordinate partner-state approaches to adopting and governing digital technologies;• Mobilize coalition efforts to provide alternatives (through funding assistance, technology
development, and private-sector investment) to digital infrastructure and AI/machine
language (ML)-enabled technologies that are used for illiberal ends and to promotetechnologies that enhance democratic participation, human rights, and the rule of law;and• Facilitate adoption of secure, reliable, and trusted digital infrastructure, AI/ML-enabledtechnologies, and information and communications technology (ICT).8
Actions for the United States and Allies and Partners:• Coordinate national strategies that articulate involvement in IDDI.
o The United States and IDDI partners should take steps to coordinate thedevelopment of national strategies for IDDI involvement. By focusing on developing
and investing in democratically aligned digital technologies and supporting digitaldevelopment, infrastructure, and capacity-building projects, national strategies for
IDDI should further the overarching goals of the ETC. The figure below provides an
overview of the IDDI.
o The Commission recommends that IDDI partners seek to align national strategies
around common guidelines for investment strategies, critical technologies, policy
guidance, and export promotion. A public diplomacy plan and associated resources
should also be prominent within each national strategy given the importance ofpromoting a positive, unified message on the benefits and importance of IDDI.

O ver view of
ID DI S trategy.

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A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R• Conduct an assessment of the global digital development landscape.
o IDDI partner states should convene with representatives from development
agencies and international financial institutions (IFIs) to conduct an assessment ofdigital connectivity and the global digital development environment to guide IDDI
activities.9
o The Commission proposes that this assessment include:■■

A global risk evaluation of state-sponsored policies, financing and investment
tools, surveillance technologies, and other mechanisms that erode privacy andcivil and human rights. This evaluation would inform IDDI priorities.
Identification of technologies or technological features to promote through
IDDI activities, incorporating some or all of the following:• privacy protections, such as privacy-preserving ML, eyes-off ML, advanced
encryption, and secure multi-party computational models10;• protections against unwanted bias in data and inferences;• restrictions on the use of certain applications to prevent the potential
infringement on civil and human rights;• data storage and access restrictions, to prevent access from third parties,
multiple government agencies, and foreign governments;• secure, reliable AI tools and digital infrastructure;• tools and infrastructure to support “green” initiatives, including smart grids;and• tools for local populations to counter authoritarian, social-control uses ofAI.

■

Identification of best practices within IDDI members and existing initiatives thatprovide solid foundations to build upon and develop at scale. The IDDI should
capitalize on the unique capabilities and resources of individual IDDI partner
states.
• See Table 2. International Digital Development Programs (located at theend of this Plan) and the figure below.

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Models for International Digital Democracy Initiative
The following examples of efforts to develop, promote, and fund the adoption of
secure, trusted, and open digital ecosystems can serve as models for IDDI projects.
M odels forInternational
Digital D emocracy
Initiative (ID DI).

Women’s Global Development and Prosperity (W-GDP) Fund Announces $122 Million in Progress
and Partnerships, USAID (Sept. 3, 2020), https://www.usaid.gov/w-gdp/fact-sheet/aug-2020womens-global-development-and-prosperity-fund-announces-122m-progress-partnerships;
DigitalConnectivity and Cybersecurity Partnership (DCCP), USAID (Oct. 19, 2020), https://www.usaid.gov/
digital-development/digital-connectivity-cybersecurity-partnership; Press Release, U.S. Embassy
Chile, U.S. Support for Digital Transformation in Latin America and the Caribbean (Nov. 10, 2020),
https://cl.usembassy.gov/u-s-support-for-digital-transformation-in-latin-america-and-the-caribbean/;
The Three Seas Fund Makes Its First Digital Investment, Three Seas Initiative Investment Fund (Dec.
2, 2020), https://3siif.eu/news/the-three-seas-fund-makes-its-first-digital-investment; FAIR Forward Artificial Intelligence for All, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH(June2020),
https://toolkit-digitalisierung.de/app/uploads/2020/06/Factsheet-FAIR-Forward_
E050620-1.pdf; The Launch of Multi-Stakeholder Blue Dot Network, U.S. International Development
Finance Corporation (Nov. 4, 2019), https://www.dfc.gov/media/opic-press-releases/launch-multistakeholder-blue-dot-network; Blue Dot Network, U.S. Department of State (last accessed Feb. 16,2021), https://www.state.gov/blue-dot-network/; Fact Sheet, U.S. Department of State, The UnitedStates Partners with Australia and Japan to Expand Reliable and Secure Digital Connectivity in Palau(Oct. 29, 2020), https://2017-2021.state.gov/the-united-states-partners-with-australia-and-japanto-expand-reliable-and-secure-digital-connectivity-in-palau//index.html; Press Release, AustralianInfrastructure Financing Facility for the Pacific, Australia Partnering with Japan and the United States
to Finance Palau Undersea Cable (Oct. 28, 2020), https://www.aiffp.gov.au/news/australia-partneringjapan-and-united-states-finance-palau-undersea-cable#:~:text=This%20project%2C%20valued%20
at%20approximately,Japan%20and%20the%20United%20States.

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A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R• Determine investment guidelines for technology development and digitaldevelopment projects and support alignment through OECD.
o Investment decisions into the development of specific technologies and fundingof various digital development projects should be guided by the outcome of theassessment and agreed-upon digital development and AI use principles. These
include:■■■G20 Principles for Quality Infrastructure Investment 11;
Principles for Digital Development, used by USAID to guide digital foreignassistance efforts12;
Criteria for Security and Trust in Telecommunications Networks andServices, developed by the Department of State, the Center for Strategic andInternational Studies, and outside experts, and used by the Blue Dot Network13;■

OECD’s Recommendation on Digital Security of Critical Activities14;■

the forthcoming OECD Principles on Trusted Government Access to Data;■

OECD Guidelines for Multinational Enterprises15; and■UN Guiding Principles on Business and Human Rights.16

o IDDI members should also work with the OECD to standardize AI and digitaldevelopment assistance through the creation of a dedicated “Digital Development”
purpose code.
■

A dedicated purpose code, monitored by the OECD’s Development AssistanceCommittee (DAC),17 will demonstrate international resolve, facilitate
coordination, enable the OECD and other entities to monitor funding in digitaldevelopment activities, and consolidate data to inform IDDI strategic decisionmaking.

• Develop guidelines for the use of technologies within the IDDI.
o The risk assessment should lead to the development of guidelines for the use of AI/
ML-enabled applications and surveillance technologies.
■

This effort should build on several foundational documents, including theOECD AI Principles18 along with NSCAI’s Key Considerations for Responsible
Development & Fielding of Artificial Intelligence,19 which provide operational
guidance for the responsible and ethical development and use of AI inengineering practices, system performance, human-AI interaction, andaccountability and governance.

• Develop export promotion and control principles and coordinate adoption by eachpartner state.
o IDDI nations should establish priorities for export promotion and R&D activities topromote technologies that comport with shared democratic values and support free
and open societies. These priorities may expand upon OECD guidelines and new
U.S. Department of State guidelines on surveillance due diligence (see below on
Promoting Democracy through Export Controls), to incentivize companies against
transactions that could result in misuse of technology by government end users.20

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BLUEPRINT FOR ACTION: CHAPTER 15• Expand public- and private-sector investments by exploring the creation of a joint
investment fund and incentives for private investment.
o IDDI members should consider creating a joint investment fund—­with a dedicatedinvestment manager—­to support IDDI projects. Such a fund could be modeled on
the Three Seas Initiative Investment Fund (3SIIF). See Table 2. International Digital
Development Programs (located at the end of this Plan).
o As public-sector investment is unlikely to achieve the scale necessary to realize
IDDI goals and safeguard IDDI partner states’ collective security,21 IDDI partners
should seek to catalyze at least $20 billion in private-sector investment.
o IDDI members could explore incentives for private-sector investment in criticalemerging technologies, particularly in the Indo-Pacific, Latin America, and otherregions with strong growth potential. Policies to explore include tax incentives
and subsidies, communication of IDDI priorities to the private sector, highlighting
private-sector investments and practices that advance IDDI goals, and increased
taxes on profits made from strategic competitors’ publicly traded companies.22• Execute a coordinated strategic messaging and awareness campaign.
o The success of the IDDI will depend not only on coordinated investment andassistance activities, but also on the ability of IDDI members to effectively
and strategically communicate the objectives to world leaders, internationalorganizations, and the public.

Recommendation

Recommendation: Develop and Implement a Comprehensive U.S. National Plan to Support
International Technology Efforts
The ISTS should include an integrated government-wide plan for coordinating the toolsof U.S. foreign policy to advance the ETC, the IDDI, and stand-alone projects. This planshould leverage technical expertise, foreign assistance, development financing andinvestment, policy guidance, and export controls in support of three core goals:
1. Shaping international technical standards on AI and related technologies;
2. Implementing a coordinated U.S. policy for the IDDI; and3. Promoting transparency and accountability through export controls.
Component 1: Shape International Technical Standards
The United States and its allies should lead the way on international technical standardization
for AI. U.S. government-led dialogue with U.S. industry, as well as democratic allies, canhelp overcome information asymmetries and clarify objectives for technical standards on
AI that foster economic growth, protect consumers, and safeguard democratic values.
Partnership and information-sharing between the U.S. government, industry, and academia
is critical to ensure protection of national security concerns involving standards and theneutrality of international standards-setting bodies.23

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Action for the President:• Issue an Executive Order to support international technical standardization.
o As detailed in NSCAI’s Interim Report and Third Quarter Recommendations, thePresident should issue an executive order24 that would:
■

■

■

■

■

■

establish an interagency coordination task force for sharing threat informationand identifying U.S. national security interests related to AI technicalstandards, and related standards such as international data science standards,
to be led by NIST with membership from the Departments of State, Defense,
Energy, Commerce, and Homeland Security, the Office of the Director of
National Intelligence, and USAID;
direct the interagency task force to improve partnership and collaboration withindustry and academia;
direct the interagency task force to consult with relevant congressional
committees and develop a work plan with congressional appropriators on
the necessary resources and full-time equivalents necessary to support U.S.
leadership in international technical standardization;
direct federal agencies to resource and support focused research, test, andevaluation and regular and active participation by the U.S. Government ininternational standards-setting activities;
require the Director of NIST and the Standards Coordinator to encourage theprivate sector to create a Standardization Center to improve sharing of bestpractices and other information relevant to standards development, as well as
support focused research coordination; andestablish a federal advisory committee with experts from the private sector andacademia to provide strategic guidance to the interagency coordination task
force on international technical standards.

Action for the Department of Commerce:• Coordinate technical standards-development activities government-wide through
NIST leadership of the interagency task force.
o The development of international standards for AI and emerging technology
should be incorporated into the overarching ISTS. Within the U.S. government, thisprocess must continue to be led by NIST with active participation of agencies in thecoordination task force described above.
o The Commission has proposed a comprehensive plan for NIST and other U.S.
departments and agencies to ensure that the development of international
technical standards receives greater attention and resourcing to ensure that U.S.
national security interests, including the promotion of technologies that comportwith democratic values, are advanced in standards-development organizations.25
o NIST and other agencies should consider the Commission’s Key Considerations for
Responsible Development and Fielding of AI in assessing positions on technicalstandards.26

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BLUEPRINT FOR ACTION: CHAPTER 15• Convene a federal advisory committee to inform strategy on international
standards.
o As noted above, the proposed Executive Order would create a federal advisorycommittee to provide the interagency task force with expert guidance to inform U.S.
government strategy on international technical standards.
o Members of the advisory committee should be drawn from the private sector andacademia and should be selected by the interagency task force for their expertise inemerging technologies, geopolitical analysis, global economic trends, and similarfields.
o The Commission envisions that this advisory committee, by focusing on strategic
geopolitical issues around international technical standards, would serve a function
not currently fulfilled by other advisory groups and the industry organizations thatcoordinate U.S. positions before international standards bodies.27
o The advisory committee should have a forward-looking mandate to contributeto U.S. government strategy on a range of emerging technologies—including
technologies involved in genomics, digital currency, biopharma production, andothers.
o NIST and the Department of State should ensure that members receive appropriate
clearances to facilitate exchanges of classified information necessary to thedevelopment of U.S. strategy.
Action for the Departments of Commerce and State:• NIST, with assistance from the Department of State, should coordinate technicalstandards-development activities internationally.
o In addition, NIST, working closely with the Department of State—­ideally, in thecontext of the ETC and the IDDI—­must prioritize engagement with democraticnations to align positions on standards critical to mutual security and defense
and ensure those positions are reflected in deliberations of technical standardsdevelopment organizations.
o The Department of State’s Regional Technology Officers can serve as conduits forthis alignment (see below on “Reorient U.S. Foreign Policy and the Department ofState”).
Actions for Congress:• Provide appropriate funding to NIST and other U.S. departments and agencies tosupport international technical standardization efforts.
o As the Commission has recommended,28 Congress should provide funds sufficient
to support at least six full-time equivalent personnel at NIST and at least one fulltime equivalent each at the Departments of State, Defense, Homeland Security,and Energy; the Office of the Director of National Intelligence; USAID; and otheragencies as may be appropriate. These personnel will support NIST’s AI Standards
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necessary for technical standardization, such as participating in standardsdevelopment organizations.
• Provide appropriate funding, and grant-issuing authority, for the Department ofState to ensure international leadership in developing technical standards.
o As the Commission has recommended,29 the Department of State must be properly
resourced to fully engage in international forums, unions, and organizations focusedon developing standards for AI, associated technologies, and data. Congressshould provide a minimum of $5 million to support these endeavors, particularly therecruitment and funding of U.S. academic scholars and researchers to participate
in these international forums. This action may require the creation of a new foreignassistance fund and grant-issuing authority to a Department office.
• Establish a grant program to enable small- and medium-sized U.S. AI companies toparticipate in international standardization efforts.
o As the Commission has recommended,30 Congress should authorize a grant
program for small- and medium-sized U.S. AI companies to cover the high costsof engaging in international standardization efforts, including conducting relevant
research, developing requisite skills and expertise, preparing standards proposals,
and attending technical standards-setting meetings. Their input enables greater
technological innovation, helps prevent potential high “switching costs” that may
impede their growth, and facilitates solution development for standards that impede
exporting by these small businesses.
o The Commission proposes that Congress appropriate an initial amount of $1 million
annually to fund grants issued by the Small Business Administration, in coordination
with NIST.
Component 2: Implement a Coordinated U.S. National Policy for the IDDI
A national policy for U.S. digital development efforts and involvement in IDDI will provide
high-level strategic vision and coordination necessary to:• Advance the interests of the United States and its allies and partners in the development
and global adoption of AI/ML-enabled technologies and secure, trusted, and open
digital ecosystems that promote values critical to free and open societies;• Elevate—­across U.S. departments and agencies—­the prioritization of digitaldevelopment necessary to advance U.S. interests and IDDI goals and reorient U.S.
development efforts for a digital age; and• Strengthen U.S. foreign policy through significant appropriations for digitaldevelopment, increased resourcing and staffing, and expanded authorities for federal
departments and agencies, particularly the Department of State, USAID, and DFC.

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Actions for the ISTS Task Force:• Develop, as part of the ISTS, a U.S. national strategy for promoting digitaltechnologies and supporting digital development, infrastructure, and capacitybuilding.
o The ISTS should include a comprehensive and integrated approach to the foreignassistance and development financing tools of the U.S. government. This will
enable coordinated U.S. participation in the broader IDDI effort and provide
a roadmap to more effectively using U.S. government resources to support
digital infrastructure development and democratic adoption of AI and emerging
technology.
o The strategy should also detail a strategic messaging and public awarenesscampaign to expose violations of international standards and democratic norms byauthoritarian states.
o The figure below identifies critical U.S. stakeholders and their proposed role in thegovernment-wide effort. The Commission recommends development of agencyspecific plans to implement the strategy.

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U.S. National Plan to Support International Technology Efforts
U.S . National
Plan to Suppor t
International
Technology Ef for ts.

NIST is non-regulatory agency of the Department of Commerce and BIS is a Bureau in the Departmentof Commerce.

p530BLUEPRINT FOR ACTION: CHAPTER 15• Conduct an assessment of existing programs across the U.S. government andassociated funding, staffing, and authorities of ISTS Task Force entities.
o The ISTS Task Force should conduct an early assessment to guide development of
this portion of the ISTS. The assessment should include:■■

An evaluation of current and recent interagency programs to identify bestpractices and priority countries as well as data governance frameworks andmultilateral engagements on which more comprehensive efforts can be built.31
See Table 3. U.S. Digital Development Programs (located at the end of thisPlan).
An evaluation of authorities, appropriations, and personnel necessary toachieve the objectives of the national strategy.32

o The ISTS Task Force should also consider addressing immediate needs for expertsin emerging technology issues through innovative public-private fellowship rotation
programs and intra-government details.
■

Personnel from CISA and the Defense Advanced Research Projects Agency(DARPA), for example, can help on immediate needs at the Department ofState and USAID.

Action for USAID:• Prioritize implementation of the Digital Strategy and support urgent resourcing
and organizational needs.
o The USAID Digital Strategy33 is an ambitious and necessary five-year plan fordevelopment and humanitarian assistance focused on promoting secure, trusted,and open digital ecosystems and the responsible use of AI technologies.
■

Implementation has lagged due to insufficient funding, inadequate staffing,
and bureaucratic challenges. Currently, the Digital Strategy is administered by
the Technology Division within the Innovation, Technology and Research Hubin the Bureau for Democracy, Development and Innovation (DDI).

o The Commission recommends that the USAID Administrator continue efforts totransform the development paradigm by infusing a digital foundation across USAID
portfolios.34 To this end, the Administrator should prioritize the Digital Strategy by (1)advocating for congressional appropriations to fund Digital Strategy programs (seeinfra), (2) augmenting development staff with experts in AI, 5G and connectivity,and cybersecurity, both at headquarters and in forward-deployed missions,(3) converting the Technology Division into a formal Center within DDI, and (4)prioritizing the inclusion of technology and digital across all development efforts.
o Immediate staff augmentation can be accomplished by enhancing existing
implementing partnerships with the private and non-profit sectors, through direct
hires, fellowship programs for researchers, and details from other federal agencies.
o Longer-term staffing needs would benefit from creating a foreign service backstop
from the recommended Center focused on digital expertise to strengthen USAID’s
ability to identify needs, assess risks, and execute on programmatic activities arounddigital development.

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Action for DFC:
• Expand formal relationships with international partners and private foundations to
expand the scope of DFC investments and connectivity projects through blended
financing arrangements.
o DFC’s Roadmap for Impact is a five-year effort to catalyze $75 billion—­$25 billion
by DFC, $50 billion from the private sector—­and provide technical expertise andsupport to optimize development impact.35 The Roadmap for Impact proposes to“elevate innovation and technology across at least 50% of the DFC portfolio” anddevote $5 billion for digital infrastructure projects and increasing internet access.36
o Current authorities limit DFC’s ability to invest in higher-risk transactions, which
presents challenges for scaling digital infrastructure projects, particularly indeveloping countries.
■■

DFC investments are scored under the Federal Credit Reform Act, and DFC
has limited budget authority for subsidy for equity financing ($150 million) anddebt financing and technical assistance ($30 million).37
DFC cannot provide concessionary lending, unlike China38 and peer
agencies, such as the Japan Bank for International Cooperation and European
Investment Bank, as well as the World Bank.39

o DFC should deepen its relationships with existing and new international partners to
expand the scope of its financing and equity investments in the digital development
space.40
o Similarly, DFC should expand partnerships with a broader range of nongovernmental entities to leverage its own appropriations through blended financing
arrangements that enable higher-risk investments.41
o This may include creating a digital technology fund42 that invests in developing
secure, trusted digital infrastructure, AI/ML-enabled technologies, and ICT withtechnical features that comport with democratic values and ethical norms around
openness, privacy, security, and reliability.
Actions for Congress:• Create an allocated Emerging Technology Fund for foreign operations and related
programs of USAID and the Department of State.
o The underfunding of U.S. digital foreign assistance and financing programs is
exacerbated by competition with other funding priorities and lack of a flexible
allocated budget.
o Congress should authorize an allocated budget account, the Emerging Technology
Fund, to facilitate holistic planning of digital foreign assistance, digital development
projects, emerging technology programs, and other ISTS activities.
o The Commission proposes that the allocated account include the requests for
additional, targeted appropriations for USAID and the Department of State.
o Existing digital-related programs could also be consolidated into the Emerging
Technology Fund.

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BLUEPRINT FOR ACTION: CHAPTER 15• Appropriate $200 million annually to implement the USAID Digital Strategy.
o The Commission recommends Congress appropriate a minimum of $200
million annually to support implementation of the USAID Digital Strategy by theTechnology Division within DDI, with required funding likely multiples higher. The
funds should support programmatic activities as well as critical hiring needs.
o This amount builds on USAID’s FY 2021 request for $82 million,43 which includes
support for the Digital Ecosystem Fund,44 staff augmentation, and programmatic
activities.
• Appropriate $300 million annually for the Department of State’s emergingtechnology programs.
o The Commission recommends Congress appropriate a minimum of $300 million
annually to support the Department of State’s emerging technology programs andadministrative needs and to build what is currently a small cross-Department groupof officials with expertise in emerging technology issues.
■■

These funds should include the immediate request for supplemental
appropriations, described later in this Blueprint for Action, for $70 million
to address urgent diplomatic efforts, programs, and foreign operations in AI,
emerging technologies, and data.
Additional funding would support foreign assistance activities aroundemerging tech and digital infrastructure, to include planning, assessments,
and provision of assistance. Funds would support targeted, digital programs inseveral areas, including Department of State programs involving the rule of law(INL), democracy and human rights (DRL), security cooperation (AVC, PM, ISN),
and technical assistance (EB, STAS, others).

• Provide DFC with sufficient appropriations to strengthen development finance as atool for achieving national objectives.
o To improve the ability of the U.S. government to leverage the tools of development
finance and equity investments to further the ISTS mission, Congress should
provide DFC with $1 billion in flexible, programmatic funding to support digitaldevelopment projects.45• Increase DFC’s capacity for blended development financing through interagency
partnerships.
o Congress has restricted the appropriations available for USAID, MCC, and theDepartment of State to partner with DFC in blended transactions.46 USAID and theDepartment of State are limited to transferring $50 million overall—­spread acrossall projects, not limited to digital.47
o As DFC’s role in digital development investments increases, the need for funds
from the Department of State, USAID, and MCC will also increase, requiring anequivalent increase in funding to support USAID, State, and MCC digital and AIrelated efforts that may be tabled to enable a transfer of funds to DFC.48
o The Commission proposes that Congress appropriate a total of $200 million to the
Department of State, USAID, and MCC to be used for DFC investment programs.
• Appropriate funds to support critical personnel needs at DFC.

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o Congress should appropriate funds sufficient for DFC to increase its forwarddeployed personnel, located in regions in which DFC invests.
■

Currently, 98% of DFC staff is based in Washington, D.C. This puts DFC ata disadvantage vis-à-vis foreign development finance institutions (DFIs). By
comparison, DFC estimates that peer DFIs have roughly four times the numberof staff and base them predominantly in low- and lower-middle-income
countries.49

Component 3: Promote Transparency and Accountability Through Export Controls
ISTS objectives will be furthered by the U.S. government’s ability to harness the power of
the U.S. private sector. A critical tool for achieving this involves incentivizing the export oftechnologies that align with democratic values.
Action for the Departments of Commerce and State:• Develop end-user licensing policies and export controls as part of the ISTS.
o The Department of Commerce, through the BIS, should use targeted end-use
controls and human rights due-diligence reporting requirements to preventand deter U.S. firms from enabling problematic government end uses of AI andassociated technologies.50■■

BIS should build on its 2020 request for public comments on ways tostrengthen controls and monitoring of advanced surveillance systems—thisarea could be explored to prevent the use of compute-intensive technologies
for human rights abuses while furthering the promotion of democratic-aligned
technology. Regulations issued in October 2020 provide BIS with discretion todeny export licenses for products that could be used to violate or abuse humanrights.51
Coordinated with the ISTS Task Force, these stronger export control rules canpromote the ethical and responsible use of AI among U.S. firms, set standards
for global industry, and counter abuses of human and civil rights.

o The Department of State, through the Bureaus of Democracy, Human Rights andLabor (DRL), International Security and Nonproliferation (ISN), and Political-Military
Affairs (PM), should expand upon its recently issued framework to guide businesses
in assessing risks of human rights abuses when exporting surveillance equipment,52
while bolstering the promotion of democratic values.
o In coordination with the Department of Commerce, the Department of Stateshould expand data collection and analysis of human rights abuses associated withemerging technologies and authoritarian digital practices.53p534

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Recommendation

Recommendation: Enhance the United States’ Position as an International Emerging
Technology Research Hub
The third component of the ISTS is to enhance the role of the United States as aninternational emerging technology research hub. The goals are to:• Facilitate U.S. government contributions to collaborative initiatives and technicalstandards, such as Global Partnership on AI (GPAI)54 and digital projects of the OECD;• Strengthen the talent of the United States, allies, and partners by investing in people
through workforce development, mentorship, and exchange programs facilitatedthrough the recommended Multilateral AI Research Institute (MAIRI);• Foster collaborative research relationships and pool research resources for thedevelopment of technologies (particularly in civilian applications) that comport withdemocratic values and address gaps in commercial R&D, including joint research inprivacy-enhancing technologies; and• Enable the U.S. and allies to overcome current regulatory challenges currently
inhibiting collaboration, particularly in Europe, such as data-sharing restrictions andliability agreements.

C omponents ofInternational Digital
Research H U B.

Component #1: Support International Digital and AI R&DInternational efforts, like the GPAI and the OECD’s AI and digital initiatives, are criticalforums for facilitating alignment among like-minded countries on advancing the responsible
and human-centric development and use of AI. Research undertaken by the National AI
Research Institutes—run by the NSF and other U.S. agencies—and by other programs
across Federal departments and agencies, is an incredible resource that should support
these key international efforts and advance AI and digital goals of the U.S. and like-minded
partners.

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Actions for the Department of State, OSTP, and NSF:
• Formalize a “center of expertise” relationship with the Global Partnership on
Artificial Intelligence (GPAI).
o NSF should evaluate candidates to serve as a U.S. center of expertise for GPAI. NSF
should submit a recommendation to the Director of OSTP to guide negotiations withGPAI.
■

NSF should consider candidates from among its AI-related awardees,
including the National AI Research Institutes or by establishing a coordination
hub of all Institutes. NSF should also propose methods for leveraging other
U.S. science and research agencies, such as the Department of Energy and
NIST, to support the center of expertise.

o In coordination with the Department of State and OSTP, NSF should negotiate amemorandum of understanding between NSF and GPAI to formalize the center’s
support of GPAI working groups.55• Increase support to the OECD’s AI and digital efforts.
o The U.S. government should expand its collaboration with the OECD’s AI initiatives,
including those of the Directorate for Science, Technology and Innovation and the
AI Policy Observatory.56 The Commission proposes an expanded relationship in
three ways:o First, NSF should explore methods to support OECD’s “Going Digital” program57 topromote data sharing among partner nations.
■

Input should include pilots on cross-border data flow measurement,
taxonomies to compare countries’ data initiatives, or data governance policies.

o Second, the Department of State, OSTP, and NSF should look for opportunities toalign with allies and partners through the OECD on data guidelines, in particular
by promoting value-based best practices for collecting (e.g., with consent andcontributor controls), documenting (e.g., to support responsible use and quality),
using data in R&D (e.g., with transparency), and then making data used in publishedresearch available to the broader research community (e.g., for reproducibility).
o Third, OSTP should work with the OECD to formalize a “network of researchnodes” to coordinate AI and digital-related efforts and R&D centers worldwide.
Policymakers and researchers would greatly benefit from a global information
platform that enables easier understanding of the various AI and related initiatives
and ongoing research efforts.
Action for Congress:• Provide administrative funding to support U.S. research contributions to GPAI.
o The centers of expertise that support GPAI also provide administrative andsecretariat-like assistance (e.g., planning of GPAI plenaries). Congress should
therefore provide additional resourcing to NSF to support the center’s development,
administrative staff, and resourcing to leverage research from NSF’s AI portfolio,
including the National AI Research Institutes, and from other U.S. departments andagencies as needed.
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o The Commission recommends a minimum of $3 million over a three-year period.
Component #2: Establish the Multilateral AI Research Institute (MAIRI)
The Multilateral AI Research Institute (MAIRI) will provide a model for equitable, multilateralresearch, facilitate AI R&D that builds on like-minded countries’ strengths, and develop thenext-generation global AI workforce. With a physical center located in the United Stateswith a virtual presence, MAIRI will enable collaborative research among key allies andpartners and contribute to a broader effort—­reflected in the Emerging Technology Coalition
and IDDI—­to preserve free and open societies, win the global technology competition,and foster AI innovation in a manner that comports with democratic values. Ultimately, tofurther these objectives, MAIRI should seek to facilitate a federated network of research
institutes across the globe and with national labs and university hubs.
Actions for the NSF:
• Establish MAIRI in the U.S. and support involvement of U.S. researchers in MAIRI.
o NSF should establish MAIRI, modeled on the Banff International Research Station.58
MAIRI should have a physical center in the United States, as well as a virtualpresence. NSF should provide MAIRI with all staff necessary to ensure its success.
■Although NSF does not require further authorities to establish MAIRI,
legislation could facilitate this process (see actions for Congress).

o MAIRI should be designed with sufficient flexibility to enable involvement byresearchers from industry, academia, and research institutions and philanthropies
on a project-by-project basis; other U.S. departments and agencies, like theDepartment of Energy, may be critical for leveraging the entire U.S. R&D ecosystem.
o NSF programs through the Office of International Science and Engineering (OISE)
can support MAIRI by facilitating involvement of U.S. researchers.
■■

AccelNet59 can fund the travel, virtual networking and other activities
necessary to support research projects between research networks.
MULTIPLIER60 may support subject-matter experts’ travel to identify
collaboration opportunities with founding members or with countries that areconsidering joining MAIRI.

• Identify key allies and partners to be MAIRI founding members.
o NSF, in close coordination with the Department of State, should identify andnegotiate involvement of founding members.
o The Commission recommends that founding members include Australia, Canada,
France, Germany, Italy, Japan, New Zealand, South Korea, and the United Kingdom.
■

p
537

These countries have existing agreements and collaborative relationships with
the United States that could be more readily leveraged to develop the center.
They also have extensive research capabilities and share values and interests
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o Expansion to include additional allies and partners should be prioritized; forexample:■■European Union involvement should be a priority; however, the EU’s inclusionin MAIRI will depend on the ability to overcome disagreements between the
EU and United States over governing law, liability, funding, data sharing, andintellectual property.
Involvement by India should also be prioritized as MAIRI develops, building on
the Commission’s recommended U.S.-India Strategic Tech Alliance.

• Develop research integrity principles with MAIRI’s founding members.
o Founding members would agree to a jointly determined Principles for Multilateral
AI Research, which would be founded on the importance of research integrity.
Principles may include the need for transparency, particularly in disclosing funding
and international connections; the necessity for open data and data sharing; thedevelopment of risk-benefit frameworks; and the use of merit-based competition
reviews of research proposals.
o Members would also receive training on security risks and agree to use trusted
infrastructure as part of founding principles (see recommended appropriations inactions for Congress).
o The agreement will also detail the terms for handling intellectual property, sharing
data, governing law and liability, and funding.
• Develop a concrete research agenda with MAIRI’s founding members.
o Once founding members have agreed to the Principles, they will determine focus
areas and initiatives. Countries will fund the involvement of their researchers injoint projects. Joint research projects will occur through virtual spaces as well as atpartner entities like research institutions and universities that receive funding fromMAIRI. The facilities of other participating departments and agencies may also be
used.61o Research Priorities: Projects should be chosen to leverage members’ comparativeadvantages, enabling participants to learn from partner researchers. Examples of
R&D priorities are provided in the Emerging Technology Coalition Annex to thisBlueprint for Action. Priorities should include:■■■

Building shared, secure compute resources (including high-performancecomputing [HPC], cloud, and quantum computing),62 including joint
benchmarking projects and data-sharing, pooling, and storing initiatives
founded on commonly agreed upon principles that ensure trust, privacy, andsecurity.
Privacy-preserving AI/ML technologies, including technologies like federated
learning and on-device prediction that enable remote execution, encrypted
computation through multi-party computation and homomorphic encryption,and differential privacy.
Developing smart-city technologies, aligned with democratic values,that promote sustainability as well as norms that should guide standards
development at bodies like the ITU and technical standards bodies.

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BLUEPRINT FOR ACTION: CHAPTER 15• Coordinate with MAIRI founding members on funding, international agreements,
and governance structures.
o Although the United States should fund the initial startup costs, including
acquisition of MAIRI’s physical center, staff, and virtual research/networking
infrastructure (see recommendations to Congress below), each member should
thereafter provide proportionate financial contributions to MAIRI’s R&D and to theparticipation of their researchers in MAIRI-sponsored workshops and conferences63
modeled on the approach used by the Banff International Research Station.64
o For ongoing operations, MAIRI should explore the potential to develop an
endowment, modelled on the three US-Israeli binational funds. This approach
would facilitate the use of philanthropic donations to support MAIRI.
o Umbrella international AI/S&T agreements—­negotiated with NSF, MAIRI members,and U.S. agencies—­will facilitate cooperation among allies and partners beyond
MAIRI.
o Once established, MAIRI may support GPAI and other international efforts. MAIRI
should also pursue research agreements with other centers of excellence andresearch centers focused on AI R&D to create a federated network of research
institutes throughout the globe.
o MAIRI members should also determine how they will determine expanding MAIRI’s
membership, particularly to the European Union and India.
Action for Other U.S. Departments and Agencies:• Support the establishment of MAIRI and its R&D.
o NSF will be the U.S. anchor partner for MAIRI. Its success requires leveraging theentire U.S. R&D ecosystem and government research entities.65 The Departments ofEnergy and State as well as NIST, in particular, should be critical partners.
o The Department of Energy should leverage its national labs, history of working
with industry, immense technical capabilities, experience on applied research, andexpertise in HPC and quantum computing.
o The Department of State should provide foreign policy expertise and diplomacy,
including by assigning a dedicated Foreign Service Officer to support the creation of
MAIRI as well as identification of beneficial projects.
o Other federal entities, including the National Institutes of Health, the National
Oceanic and Atmospheric Administration (NOAA), and the Department of Healthand Human Services, will be critical for technical expertise and collaboration on
targeted research projects.
Actions for Congress:• Pass legislation to formally authorize MAIRI.
o Although it’s not required for MAIRI’s establishment, Congress should passlegislation that formally authorizes the creation of MAIRI and clarifies the authoritiesp539

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of other executive agencies to award funding to MAIRI. This will serve as a signal of
the importance of international AI collaboration and ensure that NSF and partner
agencies have sufficiently robust authorities to achieve its objectives.
o Legislation should also specifically authorize and direct NSF, in coordination with
the Department of State, to create a trusted learning cloud and associated compute
capacity to facilitate international collaborative research.
■

The trusted learning cloud would enable access to needed resources,
compute, and data for shared innovation and development of data-sharing
standards that could be a model for a larger international data-sharing
framework.

• Support the establishment of MAIRI through appropriate funding to NSF and othercritical agencies.
o The Commission recommends Congress appropriate a minimum of $60,750,000
for a five-year period, which will be supplemented by contributions frominternational partners.
o The proposed appropriations are as follows:■■■$10 million per year for five years to NSF and other critical agencies (such as
the Departments of Energy and State) for research initiatives.
$2 million per year for five years to NSF for establishing and maintaining thephysical center located in the United States, its associated infrastructure, andadministrative operations.
$150,000 per year for five years to NSF to support U.S. researchers’ travel andassociated expenses to partake in MAIRI’s workshops, conferences, and otherevents at the physical center.

o The Commission recommends Congress appropriate $11.25 million per year for
research initiatives dedicated to creating a trusted learning cloud and associatedcompute capacity to facilitate international collaborative research.
• Create an endowment for MAIRI to support ongoing funding.
o MAIRI may wish to develop an endowment fund similar to the U.S.-Israeli binationalfoundations. If pursued, Congress should authorize this endowment fund andsupport an initial U.S. investment. Additional appropriations would be required tosupport a MAIRI endowment fund secretariat.
Component #3: Expand Talent Exchanges
The United States must attract talent to collaborative research endeavors at both the
National AI Research Institutes and MAIRI. Sustained, strong collaboration between
MAIRI partners is critical to ensure that responsible, secure, human-centric AI prevails
over authoritarian AI. Shoulder-to-shoulder research and talent exchanges are invaluable,
enabling researchers to build relationships, learn from each other, exchange ideas, andspark future collaborations.

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Action for the Department of State:• Leverage O and J visa programs to attract skilled researchers to support MAIRI andinternational talent exchange programs.
o The Department of State, in coordination with the Department of Homeland
Security, should leverage the O and J visa programs to facilitate foreign researchers
to travel to the United States to work collaboratively with researchers from theUnited States and other nations.66 There are no statutory caps on the number ofvisas issued under these programs.67

Recommendation

Recommendation: Reorient U.S. Foreign Policy and the Department of State for GreatPower Competition in the Digital Age
In the near term, it is imperative to establish a Department of State focal point for emerging
technology policy and expertise and resourcing through steps the Commission proposes
below. In the longer term, the United States must fundamentally reorganize the structure,
focus, and culture of the Department of State to advance American interests at theintersection of democracy, technology, security, commerce, and human rights.68 Withouthigh-level support in the Department, technology competition is unlikely to become a core
aspect of U.S. foreign policy.
Action for the President:• Disseminate a Presidential letter of instruction to Chiefs of Mission that articulates
emerging technology as inseparable from U.S. core geopolitical interests.
o The instruction should direct each Chief of Mission to develop an emerging
technology plan as part of its mission strategy submitted to the Secretary of State.
Actions for the Department of State:• The Secretary of State should direct the Deputy Secretary of State for Management
and Resources (D/MR) to lead on reorienting and reorganizing the Department fortechnology diplomacy.
o The D/MR position has in the past exercised leadership to oversee significant
organizational and resourcing priorities across the Department of State.
■

Past officials in the D/MR position have spearheaded U.S. diplomatic priorities
around regional policy (such as the U.S.-Pakistan Strategic Dialogue), foreignassistance, civilian response, and international economic issues.

o D/MR should provide direction around immediate and long-term planning tocoordinate disparate offices and bureaus within the Department of State, develop
technological expertise at all levels of the Foreign and Civil Service, and ensurethat policy direction is aligned with management, personnel, and resource actions
needed to achieve reorientation with urgency and sustainability.
o D/MR should also provide leadership for executing the ISTS.
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A FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R• Generate a comprehensive proposal for immediate funding needs with a request toCongress for supplemental appropriations.
o The Department of State should prepare and submit to Congress within 60 daysa request for immediate funding needs to address personnel shortages andprogrammatic efforts to further U.S. diplomacy around emerging technology. The
Department should seek funding through supplemental appropriations to avoid lagsin the budget cycle.
• Expedite building out a dedicated bureau for emerging technology diplomacy.
o The Department of State should expedite and prioritize efforts to staff, resource, andbuild out a bureau for emerging technology diplomacy.
o The Bureau of Cyberspace Security and Emerging Technologies (CSET Bureau),
formally approved in January 2021,69 is intended to focus on security challenges
associated with cyberspace and emerging technologies.70 The Commission
proposes that the CSET Bureau be established with a broad aperture to address
diplomatic efforts across the security, economic, human rights, and regional
dimensions of foreign policy. It should serve as a clearinghouse to assess strategic,
budgetary, and personnel priorities on emerging technology policy across the
Department. The Bureau should have responsibilities for managing high-level
dialogues with allies and partners to further progress and cooperation, coordinating
policy, standards, and digital development assistance with U.S. agencies, andpromoting AI and emerging technology advocacy within the Department.
o The Department should assess where the CSET Bureau should be placed to best
achieve those objectives, but must ensure that its creation is not further delayed.
o The Bureau should be led by a high-profile Assistant Secretary or Ambassador-atLarge. If the Department appoints an Assistant Secretary to head the Bureau andlead coordination across the Department, it should consider creating a separate
Ambassador-at-Large position to lead diplomacy with foreign counterparts on
cybersecurity and emerging technology.
• Develop a comprehensive plan to reorganize technology diplomacy under a newUnder Secretary.
o The Department of State should develop a comprehensive proposal to establish
an Under Secretary for Science, Research and Technology (Q). State/Q would
bring together the elements for a robust, coordinated approach to science andtechnology diplomacy in the context of great power competition—with a focus on
emerging technology.71■

State/Q would also work with the Director for Foreign Assistance to manage a
new allocated account for digital democracy and emerging technologies andlead implementation of the ISTS across the U.S. government.

o The plan should also consider establishing Deputy Assistant Secretaries for Scienceand Technology in each regional bureau. These positions would provide a criticallink between technology officers and senior leadership.
■

Currently the Department lacks a core of senior, career officials with deep andbroad technology policy expertise. The positions would provide a career pathto the senior level for officers focused on technology policy and would enablesenior-level advocacy for reforms needed to effectively manage technology
policy.
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o Given the urgent need to enhance the Department’s technology diplomacy
capacity and the likely long-term nature of the process of establishing a new Under
Secretary, efforts to implement this recommendation should proceed in parallelwith the Commission’s other organizational recommendations.
• Establish a diplomatic presence in major U.S. and foreign technology hubs.
o The Department of State should enhance its presence in major foreign and U.S.
technology hubs, supported by establishing a cadre of dedicated technology
officers at U.S. missions to strengthen diplomatic advocacy, improve technology
scouting, and inform policy and foreign assistance choices.
o The Department should accelerate plans to establish 12 Regional Technology
Officer positions around the world72 and further describe how these officers will
enhance U.S. technology competitiveness with partners such as the Foreign
Commercial Service, USAID, and DFC. These officers should also scout technology
initiatives that can enhance our diplomatic and development capabilities.
o The Department should re-establish a permanent presence in Silicon Valley,
which it initiated in 2014, and established dedicated positions in 2015-2016. These
positions and State’s presence were discontinued when an OMB hiring freeze was
implemented in January 2017.
o In addition, the Diplomat in Residence program—with presence in 16 regionsat universities across America73—should be repurposed beyond recruitment toinclude public diplomacy, technology scouting, and engagement with foreign
government and commercial entities active across America. Domestic insight is avaluable input into foreign policy and will increase public confidence in and support
for America’s international technology leadership.
• Incorporate AI and emerging technology training modules into Foreign Service
institute (FSI) courses.
o The Department of State should incorporate mandatory AI and emerging
technology–related modules into key FSI training courses, including the
Ambassadorial Seminar, the Deputy Chiefs of Mission course, Political andEconomic Tradecraft courses, and A-100 orientation training classes. FSI should
also develop a stand-alone course on emerging technologies and foreign policy.
o The Department should partner with academic and private-sector organizations toaccess the leading edge of technology education while also building a more robust
technology fellows program for exchanges with industry.
Actions for Congress:• Expedite necessary reorganization of the Department of State by passing
legislation to create an Under Secretary for Science, Research and Technology (Q).
o Congress should act to create the State/Q position and consolidate disparate
S&T efforts in the Department in a single division. There is urgent need for suchreorganization, and Congress can empower the Department of State by introducing
and passing legislation to expedite the reorientation.
• Appropriate funds to support immediate augmentation of the U.S. diplomatic
corps.
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o Congress should provide robust funding for hiring and training of needed personnel
to enable the Department of State’s reorientation and support the Department’s
international efforts to promote U.S. values and standards in AI, data, and associatedemerging technologies.
o The Commission recommends that Congress provide at least $8 million in
supplemental appropriations for immediate hiring of staff to address emerging
technology needs across the Department’s offices and bureaus, to establish a
diplomatic presence in major U.S. and foreign technology hubs, and to develop FSI
courses.
o See the Funding Table Appendix to this report for a detailed breakdown of the
proposed appropriations.
• Appropriate funds to support the CSET Bureau.
o The Commission recommends a minimum of $20 million to establish the CSET
Bureau.
o See the Funding Table Appendix to this report for a detailed breakdown of the
proposed appropriations.
• Appropriate funds to support critical diplomatic efforts, programs, and foreign
operations in AI, emerging technologies, and data.
o Further funding is needed to enable the Department of State to advanceresponsible AI aligned with U.S. and like-minded values.
o While details of funding needs should reflect input from the Department of State,
the Commission recommends, at a minimum, that Congress issue a supplemental
appropriation for no less than $37 million, as a subset of the proposed $300 million
described earlier in this Blueprint for Action, for the following urgent needs:
■

■

■

■

■

■

■

■

Public diplomacy messaging and engagement to support democratic values
and raise awareness of U.S. leadership in AI innovation as well as the risks ofunwanted technology transfer and authoritarian digital practices;
AI exchange programs to promote U.S. values and fund participation by
developing countries participation in multilateral AI activities;
Programs to showcase American innovation and through which to promote theethical use of AI, including the American Spaces, TechCamp, MakerSpaces,
and U.S. Speakers programs74;
Partnership development and advancement of scientific norms through the
U.S. Science Envoys and Embassy Science Fellows programs;
Diplomatic operations and programs around international AI cooperation,
including support for initiatives of the ETC;
Promotion of human rights and fundamental freedoms in the AI context;
Maintaining U.S. lead in the use of AI for military applications throughcooperation with allies and partners;
Ensure political and policy congruence with allies and partners on the use of
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■

■

■

■

■

■

Ensure continued interoperability among the U.S. and its allies and partners;
Training and capacity-building for foreign governments on emerging
technologies to support responsible innovation;
Reporting to counter malign influence in AI ecosystems;
Empower global AI-focused S&T entrepreneurship through the U.S. Global
Innovation through Science and Technology (GIST) Initiative75; andPublic diplomacy initiatives on international AI standards as well as tracking
and reporting on public opinion related to AI.
See the Funding Table Appendix to this report for a detailed breakdown of the
proposed appropriations.

Multilateral AI and Emerging Technology Initiatives

Table 1. Key Multilateral Technology Initiatives
Initiatives

Critical Areas

Objectives

Council ofEurope/CAHAI• Standards and Norms• Support the Ad Hoc Committee on Artificial Intelligence (CAHAI)
efforts around the development and use of AI aligned with humanrights, rule of law, and democracy (goal of international legislative
framework on AI similar to the Budapest Convention on Cybercrime)• Promote Human Rights
and Democracy• Membership: 47 countries from Europe and six Observer States(Canada, US, Israel, Japan, Mexico, Holy See)
D10 Initiative• Standards and Norms• Promote Human Rights
and Democracy• Foster international cooperation to provide 5G alternatives to ZTE and
Huawei; shift critical supply chains out of China, and protect national
security• Support nascent effort as it builds on the promising coalition andrefines its goals, structure, and timeline; explore potential for the UK
and U.S. to jointly announce further developments• Explore expansion into other emerging technologies critical tonational security• Additional consideration: Divergent views on 5G and absence of key
nations in 5G effort may limit efficacy• Membership: Australia, Canada, France, Germany, India, Italy, Japan,
South Korea, the UK (founding member), and the U.S.

OECD Digitaland AI efforts(OECD AI
Principles,OECD.AI,ONE AI,“Going Digital”
Project)• Standards and Norms• Data Sharing• IDDI• Joint R&D on AI and
Digital Infrastructure• Continue articulating support of OECD’s international efforts toadvance responsible AI and promote implementation of OECD AI
Principles• Advance shared interests through AI Policy Observatory (OECD.AI)
and the Network of Experts on AI (ONE AI), particularly in working
groups on classifying AI systems, implementing values-based
principles, and guiding national AI strategies• Facilitate coordination of U.S. experts and representatives engaging
in ONE AI and associated working groups• Advance U.S. interests in “Going Digital” initiatives that promote data
sharing and harmonizing on IP and regulation• Membership: 37 member countries, OECD AI Principles adopted by
42 countriesp545

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Initiatives

Critical Areas

Objectives

Freedom
OnlineCoalition(FOC)/ T-FAIR• Standards and Norms• Engage to further efforts on AI and human rights and against digital
authoritarianism, with a focus on content moderation and facialrecognition

GPAI• Standards and Norms• Influence direction, scope, and goals; support expanded membership• Data Sharing• Formalize a U.S.-based center of expertise to provide technicalsupport for working groups• IDDI• Support Task Force on AI and Human Rights (T-FAIR)• Membership: 32 country coalition with members from Africa to Asia,
Europe, the Americas, and the Middle East

• Joint R&D on AI and
Digital Infrastructure• IDDI• Engage in Multi-stakeholder Experts Group Plenary• Membership: Australia, Canada, France, Germany, India, Italy, Japan,
Mexico, New Zealand, Singapore, Slovenia, South Korea, Spain, theUnited Kingdom, the United States, and the European Union with theOECD and UNESCO as a Permanent Observer the GPAI Council andSteering Committee
IPAC• Promote Human Rights
and Democracy• Standards and Norms• Promote and Protect
Innovation• IDDI• Defense and Security*

Security Alliances and Partnerships• Advance shared interests of democratic nations through working
groups: 1) Responsible AI (including Ad Hoc AI and Pandemic
Response Subgroup), 2) Data governance, 3) Future of work, 4)Commercialization and Innovation and facilitate coordination of U.S.
experts and representatives engaging in working groups and steeringcommittee

Five Eyes/
TTCP AI
Strategic
Challenge• Defense and Security

JAIC AI
Partnershipfor Defense• Defense and Security

NATO• Defense and Security• Membership: IPAC Co-chairs span the political spectrum and comefrom 19 countries across North America, Europe, Asia, and Africa• Develop methods to address AI application and interoperability,
including a possible test bed for application in other situations andwith other coalitions (e.g., NATO)• Membership: Australia, Canada, New Zealand, the United States, andthe United Kingdom• Data Sharing• Standards and Norms• Data Sharing

National
Technology
and Industry
Base (NTIB)• Influence IPAC to become a vehicle for promoting AI-related goalsof democratic nations, including the spread of democratic technology
alternatives; alignment on emerging technologies that pose threats
to national security; alignment on fair trade practices for digitalcommerce; countering IP theft; alignment on export controls andinvestment screening; and overall legislative alignment• Strengthen role as key multilateral forum for security coordination
around AI, including advancing shared interests and best practices on
AI ethics, collaborative frameworks, and strategic messaging• Current membership: United States plus twelve partner nations—­
Australia, Canada, Denmark, Estonia, Finland, France, Israel, Japan,
Norway, South Korea, Sweden, and the United Kingdom• Promote interoperability, human capital development, implementationof strategic objectives• Standards and Norms• Membership: 30 countries from Europe and North America• Defense and Security• Leverage NTIB to strengthen the industrial capabilities of the U.S.
and allies and address supply chain concerns; explore expansion of
mandate to advance AI-related interests around defense and security
issues• Promote and Protect
Innovation• Membership: Australia, Canada, the United Kingdom, and the UnitedStates*In addition to mapping multilateral efforts on AI and associated technologies to the seven critical areas
recommended as priorities for the Emerging Technology Coalition, and detailed in the Annex: Emerging
Technology Coalition Blueprint, this table includes multilateral efforts that are important for defense and security,
particularly as articulated in Chapter 3: AI-Enabled Warfare.

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Initiatives

Critical Areas

Objectives

The Quad• Defense and Security• Build on the Quad framework to deepen AI cooperation and negotiate
formal AI cooperation agreements in the Indo-Pacific• Membership: Australia, India, Japan, the United States

Wassenaar
Arrangement• Defense and Security• Promote and Protect
Innovation• Promote Human Rights
and Democracy

G7• Standards and Norms• Data Sharing

Intergovernmental Forums• Promote and Protect
Innovation• Advance multilateral coordination on export controls on conventionaland dual-use technologies• Membership: 42 participating states—Australia, Argentina, Canada,
India, Japan, Mexico, New Zealand, Russia, South Africa, South
Korea, Turkey, Ukraine, the United Kingdom, the United States, andall EU members other than Cyprus• Support French-led effort to use G7 to promote responsible AI
development and coordinate on efforts to counter disinformation andother dangerous online content• Additional consideration: Success of D10 as a coalition to addresscommon AI-related issues may limit G7 efficacy in the area, although
it continues to serve as a key forum to address important geopolitical
topics• Membership: Canada, France, Germany, Italy, Japan, the United
Kingdom, and the United States

G20• Joint R&D on AI and
Digital Infrastructure• Standards and Norms• Data Sharing• Advance efforts to enable international digital economy, develop
global solution to tax challenges from digitization of the economy, andutilize technology in infrastructure and smart city efforts• Ensure countries do not successfully promote authoritarian technology,
particularly on topic of smart cities• Membership: Governments of 19 countries and the EU; includes
China, Russia

IP5• Promote and Protect
Innovation• Continue to engage in IP5’s New Emerging Technologies AI Task
Force to advance global legal certainty and protections of AI-related
IP, enhance efficiencies in office operations through AI adoption, andstrengthen communication with industry• Membership: European Patent Office and national patent offices fromJapan, South Korea, China, and the United States

3GPP• Standards and Norms• Promote and Protect
Innovation• Advance standards to enable innovation, protect national andeconomic security in telecommunications• Defend the integrity of technical standardization

International Standards• Additional considerations: Chinese companies participating as voting
members have more than doubled in the past few years to 110 inJanuary 2020, more than twice the 53 US voting members• Membership: Seven telecommunications standards development
organizations from Japan, the United States, China, Europe, andSouth Korea
IEEE• Standards and Norms• Data Sharing• Engage, particularly on standards within the P7000 series on ethicallyaligned design series (e.g., P7001 - Transparency of autonomous
systems and P7003 - Algorithmic Bias) and Ethics Certification
Program for Autonomous and Intelligent Systems (ECPAIS)• Membership: IEEE is a technical professional organization that has
419,000 members from more than 160 countries

ISO/IEC• Standards and Norms• Data Sharing• Advance standards to enable innovation; protect national andeconomic security• Maintain consensus approach to standards development; counter
adversarial or politicization efforts• Ensure U.S. domestic policy and resourcing enables full U.S.
engagement, particularly in ISO/IEC JTC 1/SC 42 on AI• Membership: 31 participating member countries and 16 observing
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Initiatives

Critical Areas

Objectives

ITU-T• Standards and Norms• Continue to engage on international technical standards and defend
the integrity of international technical standards• Data Sharing• Engage in AI for Global Good Summit, which has strong involvementfrom India, China, and Japan• Membership: 193 countries are members of ITU-T

UNESCO• Standards and Norms• Engage with the Ad Hoc Expert Group’s initiative to create a global
standard-setting instrument on ethics and AI (Recommendation on
Ethics of AI)• Support continued development of the AI Decision Makers’ Essential
Toolkit to help decision makers, particularly in Africa, address practicalquestions for AI and UNESCO’s AI capacity building programs forstakeholders in the judicial system

International Organizations• Membership: 193 member countries
UN CCW
GGE on LAWs

• Standards and Norms• Advance shared interests of democratic nations regarding lethal
autonomous weapon systems

UNSG HighLevel Panelon Digital
Cooperation• Standards and Norms• Promote Human Rights
and Democracy• Engage as part of UN engagement; however, it is unlikely the Highlevel Panel on Digital Cooperation (HLPDC) will be a main vehicle foradvancing U.S. AI interests

WIPO AIand IP
Conversations• Promote and Protect
Innovation• Continue to engage in WIPO’s “Conversations” on AI and IP Policy
and Administration; includes data protection and sharing standards• Data Sharing• Membership: 193 member countries, with participants in theconversations on IP and AI receiving participants from over 130
countries

WTO• Promote and Protect
Innovation• Continue to engage in e-commerce and trade efforts outflowing
from the 2019 Joint Statement on Electronic Commerce and the“Osaka Track,” which promotes international rule-making to promote
e-commerce and addresses data concerns• Membership: Over 80 countries have participated in discussions• Data Sharing• Membership: Panel has participants from over 15 countries• Membership: Although there are 164 member countries in WTO, 78
members signed the Joint Statement on Electronic Commerce; 24
countries signed the Osaka Declaration on the Digital Economyp548

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Table 2: International Digital Development Programs

Multilateral Initiatives

Trilateral Infrastructure Partnership (TIP). Established in November 2018 by the US, Japan, and Australia, the TIP supports
infrastructure projects for “an Indo-Pacific region that is free, open, inclusive, prosperous, and secure.” TIP coordinates
separate financing and investment efforts of Australian Infrastructure Financing Facility for the Pacific, Japan’s Bank forInternational Cooperation (JBIC), Japan’s Nippon Export and Investment Insurance, and U.S. government stakeholders(DFC, USAID, Department of the Interior Compact Funding, and the U.S. government’s Transaction Advisory Fund). In
October 2020, TIP facilitated a $30 million project to construct underseas fiber optic cable to Palau to ensure secure digitalconnectivity.
Blue Dot Network (BDN). Launched in November 2019 by the US, Japan, and Australia, BDN convenes “governments, theprivate sector, and civil society to promote high-quality, trusted standards for global infrastructure development in an open
and inclusive framework.” It leverages “commonly accepted principles and standards to promote market-driven, transparent,
and financially sustainable infrastructure development” in the Indo-Pacific region and globally. It is led by the U.S. DFC,
Australia’s Department of Foreign Affairs and Trade, and the JBIC.
World Bank - Digital Development Partnership (DDP). Established in 2016 to support the implementation of the UN’s
Sustainable Development Goals, the DDP has over 50 active client countries with an emphasis on delivering data andindicators, digital economy enabling environment, cybersecurity, accessible internet, digital government, and mainstreaming
digital services, solutions, and platforms.
Three Seas Initiative Investment Fund (3SIIF). The 3SIIF is a partnership of 12 countries focused on promoting economic
growth, security, and a more cohesive and stronger Europe through infrastructure development in the energy, transportation,and digital sectors. The Three Seas Initiative Investment Fund (3SIIF) (currently over $900 million, including a $300 million
DFC initial investment) supports digital infrastructure projects to meet regional compute, storage, and connectivity needs.

Initiatives Led by
European Partners

EU Economic and Investment Plan for the Western Balkans. Announced in October 2020, this plan supports the region’s
digital and green transitions while boosting economic development, regional cooperation, and EU integration. Throughinvestment of 9 billion euros and the Western Balkans Guarantee Facility, which seeks to mobilize 20 billion euros inpublic and private investment, it aims to bolster digitalization across ten investment flagships. Projects include broadband
infrastructure in six Western Balkan partners, establishing trustworthy data centers and cloud infrastructures, expanding theBalkan Digital highway, and digital education programs.
EU-Asia Connectivity Strategy. In 2018, the European Parliament, the European Council, the European Economic andSocial Committee, the Committee of the Regions and the European Investment Bank issued a joint communication toestablish building blocks on an “EU Strategy on Connecting Europe and Asia” through interoperable transport, energy anddigital networks. Efforts are currently underway to strengthen EU-Asia connectivity that is sustainable, comprehensive andadherent to the international rules-based order.
FAIR Forward - Artificial Intelligence for All. The German Development Cooperation initiative seeks to create a more
“open, inclusive and sustainable approach to AI,” particularly in Africa and Asia. In partnership with Ghana, Rwanda, South
Africa, Uganda, and India, FAIR Forward seeks to: 1) strengthen local technical AI know-how, 2) remove entry barriers to
AI through access to training data and AI technologies, and 3) develop policy frameworks for ethical AI, data protection,and privacy. Projects include an African AI start-up accelerator, a Pan-African digitalization initiative, and the Open for Good
Alliance to improve localized AI training data.

Initiatives Led by
Asian Partners

International Digital Cooperation - ICT Standardization (InDiCo). Launched in 2018 by the European Commission,
InDiCo is a three-year project to promote ICT standards alignment and interoperability with key partner countries, including
Japan, South Korea, China, India, Brazil, and the United States. It coordinates technical standardization meetings andorganizes technical and political workshops on ICT standards.

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Quality Infrastructure Initiative. Japan has championed efforts to promote “quality infrastructure” investments, including
through financing of approximately $200 billion. Japan’s efforts have led to the development of principles for infrastructure
investments and is expanding into digital connectivity.
Global Cooperation and Training Framework (GCTF). GCTF, led by Taiwan and in partnership with the United States andJapan, serves as a platform for Taiwan to share its expertise with partners around the world. Recently, GCTF held a virtual
webinar with Latin American and Caribbean governments on digitization, particularly on ways to leverage data and AI to help
governments respond to COVID-19.

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Fact Sheet, U.S. Department of State, The United States Partners with Australia and Japan to Expand
Reliable and Secure Digital Connectivity in Palau (Oct. 29, 2020), https://2017-2021.state.gov/theunited-states-partners-with-australia-and-japan-to-expand-reliable-and-secure-digital-connectivityin-palau//index.html; Fact Sheet, U.S. Department of State, 2020 Indo-Pacific Business Forum
Promotes Free and Open Indo-Pacific (Oct. 29, 2020), https://2017-2021.state.gov/2020-indo-pacificbusiness-forum-promotes-free-and-open-indo-pacific/index.html; The Launch of Multi-Stakeholder
Blue Dot Network, U.S. International Development Finance Corporation (Nov. 4, 2019), https://www.
dfc.gov/media/opic-press-releases/launch-multi-stakeholder-blue-dot-network; Blue Dot Network,
U.S. Department of State (last accessed Feb. 16, 2021), https://www.state.gov/blue-dot-network/; Blue
Dot Network: Frequently Asked Questions, U.S. Department of State (last accessed Feb. 16, 2021),
https://www.state.gov/blue-dot-network-frequently-asked-questions/; Digital Development Partnership(DDP), The World Bank (last accessed Feb. 16, 2021), https://www.worldbank.org/en/programs/digitaldevelopment-partnership; Digital Development Partnership Annual Report: Responding to the COVID-19
Crisis, The World Bank (Oct. 26, 2020), https://www.worldbank.org/en/news/feature/2020/10/26/
digital-development-partnership-annual-report-responding-to-the-covid-19-crisis; Objectives, Three
Seas Initiative (last accessed Feb. 16, 2021), https://3seas.eu/about/objectives; Three Seas Story,Three Seas Initiative (last accessed Feb. 16, 2021), https://3seas.eu/about/threeseasstory; The Three
Seas Fund Makes Its First Digital Investment, Three Seas Initiative Investment Fund (Dec. 2, 2020),
https://3siif.eu/news/the-three-seas-fund-makes-its-first-digital-investment; The Fund, Three Seas
Initiative Investment Fund (last accessed Feb. 16, 2021), https://3siif.eu/the-fund/; Press Release, U.S.
International Development Finance Corporation, DFC Approves Over $2.1 Billion in New Investments
for Global Development (Dec.10, 2020), https://www.dfc.gov/media/press-releases/dfc-approvesover-21-billion-new-investments-global-development; Press Release, European Commission, Western
Balkans: An Economic and Investment Plan to Support The Economic Recovery and Convergence(Oct. 6, 2020), https://ec.europa.eu/commission/presscorner/detail/en/ip_ 20_1811; Questions andAnswers: Economic and Investment Plan for the Western Balkans, European Commission (Oct. 6,2020), https://ec.europa.eu/commission/presscorner/detail/en/qanda_ 20_1819; Connecting Europe
and Asia - Building Blocks for an EU Strategy, European Commission (Sept. 19, 2018), https://eeas.
europa.eu/sites/eeas/files/joint_communication_-_connecting_europe_and_asia_-_building_blocks_
for_an_eu_strategy_ 2018-09-19.pdf; Connecting Europe & Asia: The EU Strategy, European External
Action Service (Sept. 26, 2019), https://eeas.europa.eu/headquarters/headquarters-homepage/50699/
node/50699_en; FAIR Forward - Artificial Intelligence for All, Deutsche Gesellschaft für Internationale
Zusammenarbeit (GIZ) GmbH (Jun. 2020), https://toolkit-digitalisierung.de/app/uploads/2020/06/
Factsheet-FAIR-Forward_E050620-1.pdf; Goals, Open for Good (last accessed Feb. 16, 2021), https://www.openforgood.info/; About the Project, The InDiCo Project (last accessed Feb. 16, 2021), https://www.indico-ictstandards.eu/about-the-project; The ‘Expanded Partnership for Quality Infrastructure’
Initiative Directed Toward the G7 Ise-Shima Summit Meeting Announced, Japanese Ministry of Economy,
Trade and Industry (May 23, 2020), https://www.meti.go.jp/english/press/2016/0523_01.html; Tobias
Harris, ‘Quality Infrastructure’: Japan’s Robust Challenge to China’s Belt and Road, War on the Rocks(Apr. 9, 2020), https://warontherocks.com/2019/04/quality-infrastructure-japans-robust-challenge-tochinas-belt-and-road/; Andreea Brînză, Japan’s Belt and Road Balancing Act, The Diplomat (Nov. 8,2018), https://thediplomat.com/2018/11/japans-belt-and-road-balancing-act/; Global Cooperation andTraining Framework (GCTF) Programs, American Institute in Taiwan (last accessed Dec. 28, 2020),
https://www.ait.org.tw/our-relationship/global-cooperation-and-training-framework-programs-gctf/;
Fact Sheet, U.S. Department of State, U.S. Support for Digital Transformations in Latin America and the
Caribbean (Nov. 10, 2020), https://2017-2021.state.gov/u-s-support-for-digital-transformation-in-latinamerica-and-the-caribbean//index.html.

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Table 3: U.S. Digital Development Programs

Interagency Initiatives

Infrastructure Transaction and Assistance Network (ITAN) and Transaction Advisory Fund (TAF). ITAN and TAF are
multi-agency, United States Government efforts to strengthen capacity-building programs, provide expert transaction advisory
services, and coordinate U.S. assistance support for sustainable, transparent, high-quality digital and non-digital infrastructurein the Indo-Pacific region.
US-ASEAN Smart Cities Partnership (USASCP). Launched in 2018, USASCP aims to promote U.S. private sector
engagement in smart, sustainable city solutions, sharing best practice and technical collaboration, and strengthening digital
economy and cybersecurity. It draws on capabilities of the Departments of State, Commerce, and Transportation as well as
USAID, USTDA, and the NSF. Limited funding ($10 million initial investment in 2018) has limited its impact.
Digital Connectivity and Cybersecurity Partnership (DCCP). DCCP draws on tools of 10 U.S. agencies to promote
expanded connectivity and an open, interoperable, secure, and reliable internet. In the Indo-Pacific, $50 million has supported
15 countries through capacity building to improve digital connectivity; strengthen the private sector’s digital capacity; and assist
in the design, development, and implementation of ICT policies and regulations. In the Western Hemisphere, $10 million will
support similar initiatives.

Department or Agency-Specific Initiatives

Bilateral Memorandums of Understanding and Investments. United States Government entities are undertaking various
bilateral projects to advance a democratic digital ecosystem. For example, EXIM and the Brazilian Ministry of Economy signed
a MOU to deploy up to $1 billion in financing to support U.S. exports to Brazil in the 5G space. DFC is financing a major
telecommunications project in Ecuador and Peru, which will deploy at least 500 telecom towers and expand access to 4G
mobile broadband and high-speed internet in rural areas.

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USAID’s Digital Strategy. USAID has a far-reaching plan for digital capacity building and foreign assistance, especially aroundstrengthening open, secure, and inclusive digital ecosystems; providing country assessments to inform digital development;
and supporting the responsible use of digital technologies. For example, USAID will support the African Union’s drafting of a
dedicated protocol on digital trade and e-Commerce within the African Continental Free Trade Area. However, global demandsignificantly exceeds available funding for the digital programs. In FY2019, for example, USAID provided only $1.7 million inforeign assistance under the Digital Ecosystem Fund, one component of the strategy.
DFC’s Roadmap for Impact. The Roadmap proposes catalyzing $75 billion—$25 billion in DFC funds plus $50 billion fromthe private sectors—over five years (2020-2025) to support development projects in low- and lower middle-income countries.
DFC’s investment focus on “Technology and Infrastructure” includes support for open, interoperable, reliable and secure digital
infrastructure and internet access. The DFC five-year plan aspires to commit $5 billion in technology and critical infrastructure
investments through 10 major infrastructure projects.
EXIM’s Program on China and Transformational Exports (PCTE). Established by Congress in December 2019, PCTE
directs EXIM to reserve no less than 20% of its total financing authority ($27 billion out of $135 billion) “to directly neutralize
export subsidies for competing goods and services financed by official export credit, tied aid, or blended financing provided
by China or by other covered countries” and “to advance the comparative leadership of the U.S. with respect to China, or
support United States innovation, employment, and technological standards, through direct exports” in specific industries. AI
and associated technologies are an explicit focus.
USTDA’s Global Infrastructure Resilience Initiative. The Initiative supports work with emerging markets to plan, sustain,
and finance infrastructure to combat external threats. Projects include ICT, remote health technology, emergency response
technology, and grid infrastructure technology. USTDA also collaborates with USAID and other agencies to build digital
infrastructure and provide technical assistance as part of the DCCP.

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Advancing Sustainable Infrastructure in the Indo-Pacific Region, USAID (last accessed Feb. 16,2021),
https://www.usaid.gov/sites/default/files/documents/1861/USAID_ITAN_Fact_Sheet_080719.
pdf; Fact Sheet, U.S. Department of State, U.S.-ASEAN Smart Cities Partnership (USASCEP): Sharing
Expertise Between Cities to Benefit the People of ASEAN (Feb. 12, 2021), https://www.state.gov/us-asean-smart-cities-partnership-usascp-sharing-expertise-between-cities-to-benefit-the-people-ofasean/; U.S.-ASEAN Smart Cities Partnership, USASCP (last accessed Feb. 16, 2021), https://www.
usascp.org/home-page; Digital Connectivity and Cybersecurity Partnership (DCCP), USAID (Oct. 19,
2020),
https://www.usaid.gov/digital-development/digital-connectivity-cybersecurity-partnership;
Fact Sheet, U.S. Embassy and Consulate in the Republic of Korea, 2020 Indo-Pacific Business Forum
Promote Free and Open Indo-Pacific (Oct. 29, 2020), https://kr.usembassy.gov/102920-2020-indopacific-business-forum-promotes-free-and-open-indo-pacific/; Press Release, U.S. Embassy Chile,
U.S. Support for Digital Transformation in Latin America and the Caribbean (Nov. 10, 2020), https://cl.usembassy.gov/u-s-support-for-digital-transformation-in-latin-america-and-the-caribbean/;Fact
Sheet, U.S. Department of State, U.S. Support for Digital Transformations in Latin America and the
Caribbean (Nov. 10, 2020), https://2017-2021.state.gov/u-s-support-for-digital-transformation-in-latinamerica-and-the-caribbean//index.html; USAID has published resources, such as Reflecting the Past,
Shaping the Future: Making AI Work for International Development and Managing Machine Learning
Projects in International Development: A Practical Guide, which are reflective of the Digital Strategy
and outline democratic principles in the deployment of those technologies. See Reflecting the Past,
Shaping the Future: Making AI Work for International Development, USAID (May 21, 2019), https://www.
usaid.gov/digital-development/machine-learning/AI-ML-in-development; Managing Machine Learning
Projects in International Development: A Practical Guide, USAID (Jan. 12, 2021), https://www.usaid.gov/
digital-development/managing-machine-learning-projects; Digital Strategy 2020-2024, USAID, (Jun.
2020), https://www.usaid.gov/usaid-digital-strategy; Digital Ecosystem Fund: 2020 Activities, USAID(Dec. 22, 2020), https://www.usaid.gov/digital-development/DEF2020; Roadmap for Impact, DFC (lastaccessed Feb. 16, 2021), https://www.dfc.gov/roadmap-for-impact; Fact Sheet, Export-Import Bankof the United States, Overview: Program on China and Transformational Exports (last accessed Feb.
16,2021),
https://www.exim.gov/who-we-serve/external-engagement/china-and-transformationalexports-program/fact-sheet#:~:text=EXIM%20is%20actively%20working%20to,or%20by%20other%20
covered%20countries; Fact Sheet, Export-Import Bank of the United States, Program on China andTransformational Exports: Supporting Artificial Intelligence (last accessed Feb. 16, 2021), https://www.
exim.gov/who-we-serve/external-engagement/supporting-us-artificial-intel-exports; Press Release,
Export-Import Bank of the United States, EXIM Board Unanimously Approves Historic Policy to Support
U.S. Exporters Competing with the People’s Republic of China (Dec. 18, 2020), https://www.exim.
gov/news/exim-board-unanimously-approves-historic-policy-support-exporters-competing-peoplesrepublic; Global Infrastructure Resilience Initiative, U.S. Trade and Development Agency (last accessed
Feb. 16, 2020), https://ustda.gov/call-for-initial-proposals/; 2021 Indo-Pacific Strategy Fact Sheet,
U.S. Trade and Development Agency (last accessed Feb. 16, 2020), https://ustda.gov/wp-content/
uploads/2021-Indo-Pacific-Strategy-Fact-Sheet.pdf; Press Release, U.S. Trade and Development
Agency, USTDA Announces 2021 Digital Strategy for the Indo-Pacific (Oct. 27, 2020), https://ustda.gov/
ustda-announces-2021-digital-strategy-for-the-indo-pacific/.

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Blueprint for Action: Chapter 15 - Endnotes
1

For more information on the National Technology Strategy, see Chapter 9 of this report.

2

See Chapter 9 of this report for more details.

3
See Interim Report and Third Quarter Recommendations, NSCAI at 214-218 (Oct. 2020), https://www.nscai.gov/previous-reports/.

The Commission supports these efforts and further encourages the U.S. government to engage
proactively through a “mosaic approach” to ensure the Emerging Technology Coalition is additive andnot duplicative. See Interim Report and Third Quarter Recommendations, NSCAI at 185 (Oct. 2020),
https://www.nscai.gov/previous-reports/.

4

Key organizations for membership include the OECD, the Council of Europe (through the Ad Hoc
Committee on AI), the Freedom Online Coalition, GPAI, and the North Atlantic Treaty Organization.
The United States and other core partner states should consider including international organizations
as observers (e.g., the World Bank, the International Monetary Fund, the World Intellectual Property
Organization, the World Health Organization, the World Trade Organization, and United Nations
Educational, Scientific, and Cultural Organization).

5

Recommendation of the Council on Artificial Intelligence, OECD (May 21, 2019), https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.

6

The IDDI should mobilize financial resources and technical expertise as the DFI Alliance, apartnership between the U.S. International Development Finance Corporation and development
finance institutions (DFIs) of 15 other OECD countries, ushered in response to COVID-19. See
Development Finance Institutions Join Forces to Respond to COVID-19 in Developing Countries, U.S.
International Development Finance Corporation (April 6, 2020), https://www.dfc.gov/media/pressreleases/development-finance-institutions-join-forces-respond-covid-19-developing.

7

The Global Infrastructure Hub has forecasted global telecommunications infrastructure investment
need at $8.9 trillion over the next approximately 20 years, with current trends falling short of theneed by $1 trillion. Forecasting Infrastructure Investment Needs and Gaps, Global Infrastructure
Hub (2020), https://outlook.gihub.org/. The Alliance for Affordable Internet estimates it will cost$428 billion and up to 10 years to achieve universal connectivity to quality broadband internet. See
Maiko Nakagaki, $428 Billion Investment Needed to Connect All of Humanity to the Internet by 2030,
Alliance for Affordable Internet (Sept. 17, 2020), https://a4ai.org/428-billion-investment-needed-toconnect-all-of-humanity-to-the-internet-by-2030/.

8This could include bilateral development finance institutions (DFIs) in OECD member countries andmultilateral DFIs, which are the private-sector arms of multi-state IFIs. See Development FinanceInstitutions and Private Sector Development, OECD (last accessed Jan. 27, 2021), https://www.oecd.
org/development/development-finance-institutions-private-sector-development.htm.

910
See the Chapter 7 Blueprint for Action and the Annex of this report containing the abridged version
of NSCAI’s Key Considerations for Responsible Development & Fielding of AI. For additional details
on the Commission’s recommendation to employ technologies and operational policies that align withprivacy preservation, see the section on “Aligning Systems and Uses with American Values and theRule of Law” in Key Considerations for Responsible Development & Fielding of Artificial Intelligence:
Extended Version, NSCAI (2021) (on file with the Commission).
11
G20 Principles for Quality Infrastructure Investment (last accessed Jan. 4, 2021), https://www.mof.
go.jp/english/international_policy/convention/g20/annex6_1.pdf.
12“The Digital Principles were first created in consultation with organizations such as The Billand Melinda Gates Foundation, the Swedish International Development Agency (SIDA), the UN’s
Children’s Fund (UNICEF), UN Development Program (UNDP), the World Bank, and the U.S.
Agency for International Development (USAID), and the World Health Organization (WHO).” See
Frequently Asked Questions, Principles for Digital Development (last accessed Jan. 3, 2021), https://digitalprinciples.org/about/.

Criteria for Security and Trust in Telecommunications Networks and Services, CSIS Working Groupon Trust and Security in 5G Networks (May 2020), https://csis-website-prod.s3.amazonaws.com/s3fspublic/publication/200511_Lewis_5G_v3.pdf.
13

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Recommendation of the Council on Digital Security of Critical Activities, OECD (Oct. 12, 2019),
https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0456.

14

15OECD Guidelines for Multinational Enterprises, OECD (2011), http://www.oecd.org/daf/inv/
mne/48004323.pdf.

Guiding Principles on Business and Human Rights: Implementing the United Nation “Protect,
Respect and Remedy” Framework, United Nations Human Rights Office of the High Commissioner(2011), https://www.ohchr.org/Documents/Publications/GuidingPrinciplesBusinessHR_EN.pdf. To
mark the 10th anniversary of the adoption of the UN Guiding Principles on Business & Human Rights(UNGPs), the UN will review existing gaps and develop a roadmap for the next decade. See UN
Guiding Principles: The Next Decade, Business & Human Rights Resource Centre (last accessed Feb.
8, 2021), https://www.business-humanrights.org/en/big-issues/un-guiding-principles-on-businesshuman-rights/un-guiding-principles-the-next-decade/.

16

17
Development Finance Standards, OECD (last accessed Jan. 27, 2020), http://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/; DAC and CRS Code Lists,
OECD (last accessed Dec. 28, 2020), http://www.oecd.org/dac/financing-sustainable-development/
development-finance-standards/dacandcrscodelists.htm.
18
Recommendation of the Council on Artificial Intelligence, OECD (May 21, 2019), https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.
19
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details, see the Key Considerations for
Responsible Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on filewith the Commission).
20
See Chapter 14 of this report for additional details and recommendations pertaining to end-use andend-user export controls.
21
For example, Hillhouse Capital, an Asia-focused private equity firm known for its early investments
in Tencent and Baidu, grew from a “boutique hedge fund into a $60 billion behemoth that’s madeprescient bets on stocks, private equity and venture capital.” Hillhouse currently seeks to raise “whatwould be Asia’s largest U.S. dollar-denominated fund targeting $13 billion.” See Michael McDonald& Lulu Yilun Chen, Hillhouse Reloads After Building $60 Billion Asia Juggernaut, Bloomberg (April
28, 2020), https://www.bloomberg.com/news/articles/2020-04-27/yale-s-2-4-billion-profit-machinehillhouse-ready-to-reload; Kane Wu & Julie Zhu, Exclusive: Hillhouse Targets Over $3 Billion for New
Yuan-Denominated Fund: Sources, Reuters (Sept. 18, 2020), https://www.reuters.com/article/ushillhouse-fundraising-exclusive/exclusive-hillhouse-targets-over-3-billion-for-new-yuan-denominatedfund-sources-idUSKBN2690LK.
22
See Chapters 11, 13, 14, and 16 of this report, along with their associated Blueprints for Action,
for recommendations to strengthen public-private partnerships and private-sector investments in the
United States.

Interim Report and Third Quarter Recommendations, NSCAI at 205 (Oct. 2020), https://www.nscai.
gov/previous-reports/.

23

This executive order would build upon Executive Order 13859. Donald J. Trump, Executive Order on
Maintaining American Leadership in Artificial Intelligence, The White House (Feb. 11, 2019), https://
trumpwhitehouse.archives.gov/presidential-actions/executive-order-maintaining-american-leadershipartificial-intelligence/; see also Interim Report and Third Quarter Recommendations, NSCAI at 207-12(Oct. 2020), https://www.nscai.gov/previous-reports/.

24

Interim Report and Third Quarter Recommendations, NSCAI at 206 (Oct. 2020), https://www.nscai.
gov/previous-reports/.

25

26
See the Chapter 15 Blueprint for Action Annex. For additional recommendations on how NIST can
support qualified confidence in AI models and predicted outcomes, see Chapter 7 of this report andits associated Blueprint for Action.

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This includes the American National Standards Institute, the primary industry organization
advocating for U.S. companies before international standards bodies, and the International
Digital Economy and Telecommunication Advisory Committee (IDET). See Interim Report andThird Quarter Recommendations, NSCAI at 208-209 (Oct. 2020); About IDET, U.S. Departmentof State (last accessed Feb. 11, 2021), https://www.state.gov/international-digital-economy-andtelecommunication-advisory-committee/about-idet/.

27

Interim Report and Third Quarter Recommendations, NSCAI at 206 (Oct. 2020), https://www.nscai.
gov/previous-reports/.

28

29

Id.

30

Id.

The Task Force should also evaluate efforts to promote targeted development priorities. For
example, Power Africa is a “U.S. Government-led partnership, coordinated by USAID, that brings
together the collective resources of over 150 public- and private-sector partners to double access toelectricity in sub-Saharan Africa.” See Power Africa, USAID (Nov. 30, 2020). https://www.usaid.gov/
sites/default/files/documents/power-africa-fact-sheet-11-2020.pdf.

3132This evaluation should include a review of the Defense Production Act (DPA) as a tool for DFC andpotentially other agencies to promote the U.S. industrial base, as was done as part of the response to
COVID-19. See Defense Production Act (DPA), U.S. International Development Finance Corporation(last accessed Jan. 4, 2021), https://www.dfc.gov/dpa.

The Digital Strategy includes several complementary efforts relating to connectivity, cybersecurity,
digital finance, inclusion, and other areas, such as the Digital Connectivity and Cybersecurity
Partnership, Digital Finance, Digital Inclusion, Geospatial Technology and Analytics, Development
Informatics, and Digital Agriculture, among others. Critical components of the Digital Strategy also
include catalytic funding provided to missions (Digital Ecosystem Fund) and conducting Digital
Ecosystem Country Assessments (DECAs). See USAID Digital Strategy 2020-2024, USAID (June2020), https://www.usaid.gov/usaid-digital-strategy. USAID has also published resources which
are reflective of the Digital Strategy and outline democratic principles in the deployment of thosetechnologies. See, e.g., Amy Paul, et al., Reflecting the Past, Shaping the Future: Making AI Workfor International Development, USAID (May 2, 2019), https://www.usaid.gov/sites/default/files/
documents/15396/AI-ML-in-Development.pdf; Artificial Intelligence in Global Health: Defining a
Collective Path Forward, USAID (2020), https://www.usaid.gov/sites/default/files/documents/1864/AIin-Global-Health_webFinal_508.pdf.

33

34
For example, USAID may want to explore the potential for all its programs to include a minimum
threshold of digital programming (e.g., 10% of programmatic efforts include an element of digitaldevelopment) as a key element of supporting nations on their journey to self-reliance.

Roadmap for Impact, DFC (last accessed Nov. 25, 2020), https://www.dfc.gov/roadmap-for-impact[hereinafter DFC Roadmap for Impact].

35

36

DFC Roadmap for Impact at 6.

DFC Roadmap for Impact at 57; Congressional Budget Justification: Fiscal Year 2021, DFC (lastaccessed Nov. 25, 2020), https://www.dfc.gov/sites/default/files/media/documents/FY2021_DFC_
CBJ-Final-04222020.pdf [hereinafter DFC FY2021 Budget].

37

The China Development Bank and China Exim Bank provide concessional loans, including, forexample, a 40-year concessionary loan to Indonesia to fund its U.S. $5.29 B high-speed railway. The
loan provided a 10-year grace period, no guarantees by Indonesia, and local content guarantees. See
China’s Belt and Road Initiative in the Global Trade, Investment, and Finance Landscape, OECD at 18(2018), https://www.oecd.org/finance/Chinas-Belt-and-Road-Initiative-in-the-global-trade-investmentand-finance-landscape.pdf.

38

39
Policy Update, Donor Tracker (July 23, 2020), https://donortracker.org/policy-updates/europeaninvestment-bank-provide-us84-million-concessional-loan-senegal-support; Japanese Concessional
ODA Loans, United Nations (last accessed Feb. 2, 2021), https://www.un.org/ldcportal/japaneseconcessional-oda-loans/; Understanding China’s Belt and Road Infrastructure Projects in Africa,
Brookings Institution (Sept. 2019), https://www.brookings.edu/wp-content/uploads/2019/09/
FP_ 20190930_china_bri_dollar.pdf.

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DFC has formal relationships with international partners such as Japan and Australia (Japan Bankfor International Cooperation, Nippon Export Investment Insurance, Australia Infrastructure Financing
Facility), the African Development Bank, and the Inter-American Development Bank. See Testimony
by Adam S. Boehler, CEO, U.S. International Development Finance Corporation before the House
Appropriations Subcommittee on State and Foreign Operations, and Related Programs (March 4,2020), https://www.dfc.gov/testimony-DFC-HAP-03042020.

40

Blended finance, according to the OECD, “is the strategic use of development finance for themobilisation of additional finance towards sustainable development in developing countries.” Blended
concessional finance includes the “use of relatively small amounts of concessional donor funds tomitigate specific investment risks and help rebalance risk-reward profiles of pioneering investmentsthat are unable to proceed on strictly commercial terms.” See Blended Finance, OECD (last accessed
Dec. 28, 2020), https://www.oecd.org/dac/financing-sustainable-development/blended-financeprinciples/; Blended Concessional Finance, International Finance Corporation of the World Bank
Group (last accessed Dec. 28, 2020), https://www.ifc.org/wps/wcm/connect/topics_ext_content/ifc_
external_corporate_site/bf; see also Blended Finance, Convergence (last accessed Dec. 28, 2020),
https://www.convergence.finance/blended-finance.

41

This investment fund could be modelled after the $100 million Women’s World Banking AssetManagement Fund, which received $25 million from DFC and $600,000 from USAID. Additionally,
DFC put out a call for proposals for fund managers investing in 5G-related companies operated in
DFC-eligible emerging market countries in order to invest in open and secure ICT. See First-of-ItsKind U.S. Government Blended Finance Fund to Empower Women in Developing Markets, DFC (June4, 2019), https://www.dfc.gov/media/opic-press-releases/first-its-kind-us-government-blendedfinance-fund-empower-women; Information and Communication Technology Call for Proposals, DFC(March 2020), https://www.dfc.gov/sites/default/files/media/documents/IFD_5G_CFP_032020.pdf.

4243
The $82 million request was part of the larger $640.1 million requested to support DDI’s efforts.
See Congressional Budget Justification: Foreign Operations: Appendix 2: FY 2021, U.S. Departmentof State at 223 (2020), https://www.state.gov/wp-content/uploads/2021/01/FY21-CBJ-Appendix-2FINAL-508-Version.pdf.

The Digital Ecosystem Fund “equips the Agency’s Operating Units with catalytic financing to design
and implement activities that foster open, inclusive, and secure digital ecosystems. The DEF supports
two types of interventions: 1) Emergent opportunities to harness or shape the digital ecosystem in any
sector; 2) Strategic initiatives to strengthen or improve the digital ecosystem.” See Digital Ecosystem
Fund: 2020 Activities, USAID (Dec. 22, 2020), https://www.usaid.gov/digital-development/DEF2020.

44

45

DFC’s FY 2021 budget request sought $700 million in such funds. See DFC FY2021 Budget at 1.

46
The U.S. government announced a first-of-its-kind blended finance fund in June 2019. USAID
provided $600,000 in funding and technical assistance and DFC’s predecessor invested $25 million
to support private capital investments in the $100 million Women’s World Banking Asset Management
Fund. See First-of-Its-Kind U.S. Government Blended Finance Fund to Empower Women in Developing
Markets, DFC (June 4, 2019), https://www.dfc.gov/media/opic-press-releases/first-its-kind-usgovernment-blended-finance-fund-empower-women; Vince Chadwick, USAID, OPIC Team Up on
Women’s Finance in ‘Preview’ of New DFI Era, Devex (June 5, 2019), https://www.devex.com/news/
usaid-opic-team-up-on-women-s-finance-in-preview-of-new-dfi-era-95050.
47

See, e.g., DFC Roadmap for Impact at 57.

48

DFC FY2021 Budget; DFC Roadmap for Impact.

49

DFC Roadmap for Impact at 56.

50
See Chapter 14 of this report for recommendations regarding specific end-use controls on high-end
AI chips.
51
85 Fed. Reg. 43532, Advanced Surveillance Systems and Other Items of Human Rights Concern,
U.S. Department of Commerce: Bureau of Industry and Security (July 17, 2020), https://www.
federalregister.gov/documents/2020/07/17/2020-15416/advanced-surveillance-systems-and-otheritems-of-human-rights-concern; 85 Fed. Reg. 63007, Amendment to Licensing Policy for Items
Controlled for Crime Control Reasons, U.S. Department of Commerce: Bureau of Industry and Security(Oct. 6, 2020), https://www.federalregister.gov/documents/2020/10/06/2020-21815/amendment-tolicensing-policy-for-items-controlled-for-crime-control-reasons.

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Blueprint for Action: Chapter 15 - Endnotes
Guidance on Implementing the UN Guiding Principles for Transactions Linked to Foreign
Government End-Users for Products or Services with Surveillance Capabilities, U.S. Department ofState (last accessed Jan. 4, 2021), https://www.state.gov/wp-content/uploads/2020/10/DRL-IndustryGuidance-Project-FINAL-1-pager-508-1.pdf.

52

53
Kara Frederick, Democracy by Design: An Affirmative Response to the Illiberal Use of Technology
for 2021, Center for a New American Security (Dec. 15, 2020), https://www.cnas.org/publications/
reports/democracy-by-design; Dahlia Peterson, Designing Alternatives to China’s Repressive
Surveillance State, Center for Security and Emerging Technology (Oct. 2020), https://cset.georgetown.
edu/wp-content/uploads/CSET-Designing-Alternatives-to-Chinas-Surveillance-State.pdf.

GPAI was launched in June 2020 to advance “responsible and human-centric” AI consistent withhuman rights, fundamental freedoms, democratic values, innovation, and economic growth. Current
members include Australia, Brazil, Canada, the European Union, France, Germany, India, Italy,
Japan, Mexico, the Netherlands, New Zealand, Poland, Singapore, Slovenia, South Korea, Spain, theUnited Kingdom, and the United States, with the OECD and UNESCO as Permanent Observers. See
UNESCO Joins Global Partnership on Artificial Intelligence as Observer, UNESCO (Dec. 10, 2020),
https://en.unesco.org/news/unesco-joins-global-partnership-artificial-intelligence-observer.

54

GPAI’s five working groups (responsible AI, data governance, innovation and commercialization,
the future of work, and pandemic response) are supported by research undertaken by two centres ofexpertise: the Paris-based National Institute for Research in Digital Science and Technology (INRIA)
and the Montreal-based International Centre of Expertise in Montreal for the Advancement of Artificial
Intelligence (ICEMAI). See The Global Partnership on Artificial Intelligence Officially Launched,
Montreal International (June 15, 2020), https://www.montrealinternational.com/en/news/the-globalpartnership-on-artificial-intelligence-officially-launched/; Launch of the Global Partnership on Artificial
Intelligence by 15 Founding Members, French Ministry for Europe and Foreign Affairs (June 15, 2020),
https://www.diplomatie.gouv.fr/en/french-foreign-policy/digital-diplomacy/news/article/launch-of-theglobal-partnership-on-artificial-intelligence-by-15-founding.

55

The OECD’s work through the Directorate for Science, Technology and Innovation and the AI Policy
Observatory is supported by partnerships with governments and research entities, like the German AI
Observatory’s support of the OECD’s effort on AI’s impact on the labor market. See Work, Innovation,
Productivity and Skills Programme: Overview, OECD.AI (last accessed Feb. 1, 2021), https://oecd.ai/
work-innovation-productivity-skills.

56

57

Going Digital, OECD (last accessed Feb. 2, 2021), http://www.oecd.org/going-digital/project/.

58

Banff International Research Station (last accessed Jan. 4, 2021), https://www.birs.ca/.

59
AccelNet accelerates network-to-network collaborations by funding the connection (travel,virtual networking, workshops) between international research networks. NSF only funds the
U.S. portion and expects international partners to fund their part of the collaboration. In addition
to funding connections between existing networks, AccelNet will fund efforts to create andfoster nascent networks. See Accelerating Research Through International Network-to-Network
Collaborations (AccelNet), NSF (Sept. 21, 2020), https://www.nsf.gov/funding/pgm_summ.jsp?pims_
id=505584&org=OISE&from=home.

MULTIPLIER sends subject-matter experts to international areas of interest to assess capabilities
and gather information for potential NSF joint projects. This new program has already beenpromising—NSF sent a multidisciplinary team to the Czech Republic and was astounded at theircapabilities. NSF is now exploring bilateral collaboration. See NSF MULTIPLIER: MULTIPlying Impact
Leveraging International Expertise in Research Missions, NSF (last accessed Dec. 28, 2020), https://www.nsf.gov/od/oise/multiplier.jsp; NSCAI staff discussions with NSF staff (Nov. 14, 2020).

60

61
For example, the Department of Energy’s national laboratories may be used to sponsor researchwith the recommended dedicated funding.

The shared research resource can help prevent bottlenecks due to limited compute resources.
This effort may also be, if appropriate, part of an expansion of the National AI Research Resource
delineated in Chapter 9 of this report.

62

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63
NSCAI recommends each member dedicate funding to support research efforts. However, MAIRI
will also serve as a location for research to gather for dialogues, workshops, and mentorships.
Based on similar international research institutes, MAIRI members should consider providing theequivalent of $100K-$250K per year to cover the travel, accommodations, and per diem of around 80
researchers to MAIRI to facilitate communications and interactions between researchers.

In 2017, the government of Alberta (Canada), Canadian Natural Science and Engineering ResearchCouncil, U.S. National Science Foundation, and Mexico’s Consejo Nacional de Cinco y Tecnologia
invested $12.5 million over the next five years. See Research Station Gets $12.5M to Bring Scientists
and Mathematicians to Banff, CBC News (Feb. 10, 2017), https://www.cbc.ca/news/canada/calgary/banff-international-research-station-math-science-funding-1.3977703. For the 2012-2017 period, BIRS
received $10.3 million, of which $3.68 million was from NSF. For the 2006-2011 period, BIRS received$9.3 million, $3.1 million of which was from NSF. See Organization: Banff International Research
Station, Research Money (last accessed Jan. 4, 2021), https://researchmoneyinc.com/organization/banff-international-research-station/.

64

65
The National AI Research Institutes provide an important example of the power of leveraging R&D
cooperation across the U.S. interagency. The new NSF and DOE centers for quantum information
science are also a powerful example of the benefits of dedicated resourcing and prioritization across
two U.S. government research entities. See Andrea Peterson, NSF and DOE Support ResearchPriorities with Spate of New Center Awards, American Institute of Physics (Sept. 16, 2020), https://www.aip.org/fyi/2020/nsf-and-doe-support-research-priorities-spate-new-center-awards.
66

For more information, see the Chapter 10 Blueprint for Action.

67
J-1 visas are used by academic employers like universities and research institutions to sponsor
foreign-born academics, interns, trainees, and researchers to work for several months to five years in
the United States. J-1 visa holders are not allowed to renew their visas and must wait one to two years
before they can apply for a different immigration status. O-1 visas are for individuals who can provide
extensive evidence that they have “extraordinary ability in the sciences, arts, education, business, orathletics.” These visas last for up to three years with indefinite renewals but have been used minimallyto attract experts in S&T due to restrictive policy guidance. For a discussion of immigration and visa
programs to attract scientists and researchers to the United States, see generally Zachary Arnold,
et al., Immigration Policy and the U.S. AI Sector, CSET (Sept. 2019), https://cset.georgetown.edu/
research/immigration-policy-and-the-u-s-ai-sector/.
68

See also Chapter 1 of this report on Malign Information Operations.

69
Secretary Pompeo Approves New Cyberspace Security and Emerging Technologies Bureau, U.S.
Department of State (Jan. 7, 2021), https://2017-2021.state.gov/secretary-pompeo-approves-newcyberspace-security-and-emerging-technologies-bureau//index.html.
70
For additional details regarding the function and need for the CSET bureau, see Second Quarter
Recommendations, NSCAI at 88-89 (July 2020), https://www.nscai.gov/previous-reports/.

These elements should include key, technology-related functions of the proposed CSET Bureau; the
Bureau of Oceans, Environment and Science (OES); the Office of the Science and Technology Adviser
to the Secretary (STAS); the Coordinator for Cyber Issues (S/CCI); the Bureau for Economic andBusiness Affairs (EB); the Bureau for Democracy, Human Rights, and Labor (DRL); and the Center for
Analytics.

71

Key Topics, Office of the Science and Technology Adviser at the U.S. Department of State (lastaccessed Dec. 15, 2020), https://www.state.gov/key-topics-office-of-the-science-and-technologyadvisor/.

72

Diplomats in Residence, U.S. Department of State (last accessed Feb. 2, 2021), https://careers.
state.gov/connect/dir/.

73

See, e.g., Managing American Spaces, U.S. Department of State (last accessed Feb. 1, 2021),
https://americanspaces.state.gov/; TechCamp, U.S. Department of State (last accessed Feb. 1, 2021),
https://techcamp.america.gov/; Program Description, World Learning (last accessed Feb. 1, 2021),
https://www.worldlearning.org/program/u-s-speaker-program/.
74

75

See About GIST, GIST (last accessed Feb. 1, 2021), https://www.gistnetwork.org/about.

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Chapter 15: A Favorable International Technology Order
Annex: Emerging Technology Coalition
This Annex provides a framework and overarching agenda for global cooperation
on artificial intelligence (AI) and emerging technologies. It includes guidance on concrete,
operational projects; applications; and implementation mechanisms for collaborative AI
work across seven critical areas. Collaborative work in these areas will serve to further AI
consistent with democratic values and strengthen the ties that connect the United Stateswith its allies and partners. This Annex is intended to provide guidance to the Emerging
Technology Coalition (ETC) and may assist officials in prioritizing bilateral and multilateralcollaborative efforts outside the context, to include engagement with multilateral initiatives
Graphic
12.1:theSeven
Critical Areas
for International
Cooperation
acrossAI landscape,as reflectedin the Key
Multilateral Technology Initiatives Table of
the Chapter 15 Blueprint for Action.

Seven Critical Areas for International Cooperation

Seven Critical Areas for International Cooperation

STANDARDS& NORMS
Advance norms
that upholddemocratic values
Support secure,reliable, trusted
technologies atinternational technicalstandards bodies

FACILITATEDATA SHARING
Address legal andregulatory barriers
Explore agreements toenable data sharing
and free flow of data

JOINT R&D ON
AI AND DIGITALINFRASTRUCTURE
Promote collaborative
R&D that advances
shared interests andbenefits humanityp559

DEVELOP
AI-RELATED TALENT
Analyze labor market
challenges; harmonize
skills and certification
requirements; andincrease talentexchanges, joint
training, and workforce
development initiatives

PROMOTE DEMOCRACY,HUMAN RIGHTS, AND
THE RULE OF LAW
Pursue joint efforts tocounter censorship, maligninformation operations,human trafficking, andilliberal uses ofsurveillance technologies

PROMOTE & PROTECTINNOVATION
Export controls
Investment screening
Supply chain assurance
Emerging tech investment
Trade policy
Intellectual property
Research and cyberprotections

LAUNCH INTERNATIONALDIGITAL DEMOCRACY
INITIATIVE
Coordinate international
foreign assistance, policy
and technical guidance,and development aid
and financing

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Critical Area #1 – Developing and Operationalizing Standards and Norms• Objectives:o Advance common, democratic norms and values to govern and guide responsible
artificial intelligence (AI) and the research, development, and application of
emerging technologies globally.1
o Promote international AI norms and standards that uphold democratic values,
building on guiding documents such as the Organization for Economic CoOperation and Development (OECD) AI Principles and efforts to operationalize
principles, as reflected in the Commission’s Key Considerations for Responsible
Development & Fielding of Artificial Intelligence.2
o Coordinate positions taken by partner states’ governmental delegations and,
where appropriate, national (non-government) standards institutes accredited tointernational technical standards organizations to support development of secure,reliable, and trusted technologies and to ensure ethical and technical integrity,
endorse standards that comport with democratic values, and maintain the neutralityof these organizations.3• Priority areas for coordination. The ETC should seek to align with allies and partners
and prioritize efforts in development of international technical standards in the following
priority areas:o Safety and reliability;o Privacy-enhancing technologies, including privacy-preserving machine learning(PPML), allied cryptographic code, and other privacy-enhancing technologies;o Data sharing, labelling, and related documentation for data, models, and systems;o Assessing system performance and characterizing blind spots per shared values(including fairness, interpretability, reliability, and secure use of AI technologies aspart of integrated systems);
o Robustness to ensure models are resilient to adversarial examples and model
inversion, while red-teaming with allies on competitors’ attempts to undermine AIenabled systems;o Trust in human-machine teaming and development of common standards andbenchmarks to assess risks in settings of varying complexity and uncertainty;
o Traceability, focused on audit trail requirements per mission needs for high-stakes
AI systems including safety-critical applications; ando Interoperability, including benchmarks that assess reliability of produced model
explanations.
• Mechanisms for Coordination.
o In addition to coordinating efforts through the ETC, the U.S. government, led by the
Department of State and in coordination with the National Institute of Standards
and Technology (NIST) and its AI Standards Coordinator, should engage withdemocratic nations to align positions on standards that are critical to mutual security
and defense.

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The Department of State, as the Commission has recommended, is in theprocess of placing regional technology officers in major foreign technology
hubs.4 This development will facilitate diplomatic efforts toward coordinating
positions with allies and partners.5
As the Commission recommended in its Third Quarter Recommendations5and elsewhere in this Report, NIST and other agencies should consider theCommission’s Key Considerations for Responsible Development and Fieldingof AI. The Key Considerations include operational guidance on standards
critical to responsible AI and national security, including for technicalstandards on testing and evaluation, verification and validation (TEVV).7

o Coordination on technical standards should include the work of international
standards organizations as well as coordinated work on operationalizing AI
norms and principles. The Global Partnership on AI’s (GPAI) Data GovernanceWorking Group can provide particularly salient best practices for engineering andimplementing data-sharing, pooling, and collecting initiatives.8 The OECD is also a
critical forum for technical standards and guidelines, particularly in data sharing andresponsible AI.9
Critical Area #2 – Joint Research and Development on AI and Digital Infrastructure• Objectives:o Identify areas of shared interest conducive to collaborative R&D—such as privacyenhancing technologies, small data approaches to AI, next-generation materials,
prototyping, and high-performance computing (HPC)—for which there are existing
gaps and identify ways to share resources to pursue R&D in those areas.
o Develop mechanisms to facilitate fundamental and applied R&D projects thatinvolve collaboration among nations, industry partners, and researchers.
■

Projects may also include secure cloud frameworks, sharing best practices
on TEVV, innovative funding models, international test beds to develop
pre-commercial technologies, and leveraging the Commission’s proposed
Multilateral AI Research Institute for coordination (see the Chapter 15 Blueprint
for Action).

o Pursue collaborative, coordinated efforts to develop and deploy AI applications tobenefit humanity at large in areas of global concern such as those embodied in UN
Sustainable Development Goals.
• Priority areas for collaborative R&D—advancing AI technology.
o Development of privacy-preserving technology, such as homomorphic encryption
and differential privacy techniques,10 to facilitate cross-border AI applications, data
sharing, and cooperative efforts.11
o Continuous development and adaptation of TEVV systems to strengthen thedevelopment of trustworthy, robust AI is critical to advancing the interests ofdemocratic nations and to understanding how AI systems perform in multi-agent/
adversarial contexts.12 Collaboration in this area will contribute to understanding
differences among allies on policy, metrics, standards, and requirements while
creating stronger connections for all users in a full-cycle approach.

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o Development of AI for modeling, simulation, and design to provide researchers witha larger scope of AI-ready data sets.13
o Development of one- and few-shot learning algorithms14—algorithms that relyon less data—to facilitate future joint R&D and data sharing and improve contextspecific interoperability.15
o Development of robust allied AI to reduce vulnerabilities of allied AI systems andtraining data to adversarial attacks.16
o Achieving context-specific interoperability of AI systems necessary for crossborder AI applications, with a focus on how systems integrate particular AI/ML
components.17 The potential for AI to increase speed of operations will require
allies and partners to stress-test decision-making procedures and communications
protocols to ensure interoperability. Interoperability of AI systems is already an
issue at the forefront of defense cooperation and will only grow in importance astechnology matures.
o Development of AI to secure and improve resiliency of supply chains to protect AIcomponent supply chains while promoting domestic and allied innovation and to
apply AI to improve auditing, mapping, and securing supply chains while ensuringresilience to shocks. Given the inherently cross-border nature of supply chains andtheir critical role in the international economy around AI and advanced technology,
this is a natural area for the United States to work collaboratively with like-mindednations.
o Additional critical AI research areas including novel machine learning (ML)directions, complex multi-agent scenarios, advanced scene understanding,AI system risk assessment, enhanced human-AI interaction and teaming, andautonomous AI systems.18• Priority areas for collaborative R&D—­AI to benefit humanity. The potential for AI
to assist the global community in improving the human condition is immense. Priority
areas for international collaboration should include the following:o Environment and climate. Recognizing the growing view that environmental
degradation and climate change represent imperatives for national andinternational security, the international community must work collaboratively todevelop AI-based solutions to address common climate, environmental, and energy
challenges.
■

Collaborative initiatives such as the following serve as models for futureinternational efforts:• The Partnership between Cross Section Evaluation Working Group andOECD’s Nuclear Energy Agency’s (NEA) Working Party on International
Nuclear Data Evaluation Co-operation on International Criticality Safety
Benchmark Evaluation Project19;• GEOTHERMICA, a collaboration among 12 European countries and theUnited States to fund AI-specific research on geothermal R&D20; and• The International Partnership for Hydrogen & Fuel Cells in the Economy(IPHE),21 an intergovernmental partnership to facilitate and acceleratetransition to clean and efficient energy with the support of AI and ML
research.

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o Health, including pandemic detection and response. The COVID-19 crisis has madeclear the need for global collaboration and the potential for AI-enabled solutions.
■■

Smart disease monitoring. The Commission has recommended global
cooperation on smart disease monitoring.22 Such a global initiative, forexample, could seek to combine existing data on zoological spills with open
source health-related data to create shared, predictive, global disease
monitoring models (see Chapter 16 of this report and its associated Blueprint
for Action).23
Pandemic preparedness, vaccine development, and syndromic surveillance.
Efforts in this space24 include:• Development and coordination on international norms and standards togovern use and sharing of international health data, protecting privacy
while ensuring timely accessibility of data;• Development of privacy standards for genomic data sets;• Increased international cooperation in the COVID-19 High Performance
Computing Consortium (potentially through GPAI); and• Facilitation of international cooperation with DARPA’s work on creating theinfrastructure and protocols for data sharing and collaboration at the pointof experimentation for drug discovery.

■■

Initiatives to enable long-term quality of life. Collaboration with allies andpartners can facilitate the Commission-recommended focus on harnessing AI
to help the elderly live independently longer, assist in managing health anddaily tasks, and improve the quality of life, particularly through the applicationof AI to biomedicine.25
The National Nanotechnology Initiative’s U.S.-EU Communities of Research,26
along with various national-level efforts by partner nations,27 should serve asmodels for larger-scale international collaboration.

o Food security. The United States emphasizes agriculture-led growth, resilience,nutrition, and water security, sanitation, and hygiene in its foreign assistanceprograms. Enhancing the security of water and food of partner nations is needed todisrupt the vicious cycle of poverty, hunger, and conflict.28■■

Agricultural sectors are increasing the use of data-driven technologies such as
robotics, satellites, GPS, and drones. Significant data sets are being generated
about crop growth, soil characteristics, and weather conditions.29
AI and ML-based algorithms can amplify the data sets and hardware to improvereal-time monitoring and analysis of agricultural and distribution processes.
This can improve efforts to assess needs, enhance productivity and security,and build local capacities and productivity while minimizing environmental
impact.

o Disaster relief. AI-enabled technologies are being used to address a range ofdisaster scenarios, and further work on an international basis should be explored.
The World Economic Forum is among those groups calling for greater international
collaboration in order to realize the benefits of AI to specifically include the area of
disaster relief.30

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■

■

■

Climate- and weather-related disasters like hurricanes, wildfires, and flooding
are on the rise, and AI is already being applied to mitigate the effects bylocating survivors using unmanned aerial vehicles, removing debris after adisaster, deploying robots to communicate with victims, employing edge
technology to obtain the most up-to-date data, running predictive simulations,and leveraging social media reporting.
The DoD is partnering with DoE and Microsoft to develop “deep-learning
artificial intelligence algorithms to provide near-real-time data to improvethe decision-making of first responders engaged in natural disasters andhumanitarian assistance efforts,”31 and countries like Singapore are working
with the Joint Artificial Intelligence Center (JAIC) on this particular National
Mission Initiative.32
The United States can work with its allies and partners to effectively predict,model, prepare for, and respond to disasters, as the Commission recommends
in Chapter 11 of this report.33

o Civilian space cooperation. The United States and other space agencies employ AI
to tackle a range of space missions—including for visualization of space objects andsituational awareness, tracking space debris for satellite collision avoidance, roving
the lunar surface, deep-space exploration with autonomous systems, and detectionof asteroids that could threaten Earth.34■■■

For safe satellite navigation around space debris, the European Space Agency(ESA) and the UK Space Agency both have AI initiatives underway, whichsuggests potential opportunities for closer U.S. collaboration.35 ESA has
already established a partnership with Stanford.36
India is also building its space program and has deployed an AI-powered Moonrover.37
Russia and China appear to be developing technological solutions to thespace collision problem, which could present an area for mutually beneficial
cooperation. AI-enabled robotic assistants are also being developed for theInternational Space Station.38• Methods to implement collaborative R&D. The ETC should also explore vehicles
to enable R&D collaboration among government partners and non-governmentalorganizations.
o Collaboration must include not only government-to-government efforts, but also
methods to partner with researchers at academic research centers and in the private
sector.
o Existing science and technology (S&T) agreements between governments may
provide the legal foundation for cooperation, but details will depend on thearrangement at issue.
o The ETC should prioritize approaches that would facilitate the pooling of resources,
reduce redundancies, and support development and socialization of best practices.
o In addition, the ETC should examine challenges to cross-border, collaborative
R&D—­such as those around data privacy and data sharing between the UnitedStates and European Union—­and explore solutions to overcome those challenges.

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BLUEPRINT FOR ACTION: CHAPTER 15• Potential methods to implement and further collaborative R&D include:o Establishment of the Multilateral AI Research Institute (MAIRI). Proposed by theCommission in this report, MAIRI will serve as a center for multilateral research
to coordinate joint efforts to develop technologies and align norms that advanceresponsible, human-centric, and privacy-preserving AI/ML that better societies.
o Prioritization of R&D work of the Global Partnership on AI (GPAI). The ETC should
leverage existing frameworks wherever possible, and GPAI, supported currently
by Canada’s International Center of Expertise in Montréal for the Advancement of
Artificial Intelligence (ICEMAI) and France’s National Institute for Research in Digital
Science and Technology (INRIA),39 is among the most promising multilateral, multistakeholder initiatives to pursue collaborative R&D and advance AI technology forcommon causes. The Commission has proposed a greater role for U.S. researchers
through a U.S.-based Center of Expertise, leveraging the NSF National AI ResearchInstitutes.40
o Creation of a joint emerging-tech investment consortium. Modeled on In-Q-Tel,the consortium would spur investment by the United States and foreign partners
in early-stage companies to further development of AI technology that advances
and/or protects democratic values. The effort would benefit the United States andits allies and partners through a cross-border platform to engage with startups andentrepreneurs in the AI and emerging-tech space.
■

Within the U.S. government, this effort should draw on State Department’s
Regional Technology Officers, the Foreign Commercial Service, and
USAID missions to identify R&D and prototypes to advance U.S. diplomatic,
development, and commercial interests.

o Launching multilateral innovation prize competitions. Modeled on Defense
Advanced Research Projects Agency (DARPA) Challenges and XPRIZE Foundationcompetitions, international innovation prize competitions sponsored by two or
more governments would incentivize R&D in fundamental AI or around specificapplications necessary for national security and help to pool resources and talent
with allies and industry.41
o Fostering allied research at U.S. national labs. The ETC should consider
recommendations for increasing research by allies (potentially a subset of the ETC
membership) at U.S. national labs on sensitive topics. Although there are limitations
on U.S. national labs to allow foreign researchers, domestically housed research
efforts would limit concerns around cross-border data-sharing and cybersecurity
and could prove fruitful in R&D necessary for defense and security applications.
o Development of an R&D matching platform and a global horizon-scanningcapability. The platform would connect researchers and their projects with funders
and partners (governments, philanthropists, venture capitalists, companies,
research institutions), providing the U.S. Government with increased visibility into
research trends. A horizon-scanning capability of global R&D would complement
these efforts and draw on open-source data to give policymakers greater
understanding of relevant discoveries and key trends in the field.42
o Development of an international test bed for TEVV. An international test bed
for TEVV could be modeled on the National AI Research Institutes43 or theCommission-recommended creation of a NIST-sponsored third-party testing
center,44 but with a cross-border focus, as well as on the AI4EU project.45

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o Improved collaboration between centers of excellence, research institutes, andindustry consortia through additional coordination by partner governments.
This concept would leverage existing and soon-to-be-established centers likethe MAIRI, European Union Centres of Excellence, European AI-related Digital
Innovation Hubs, the U.S. National AI Research Institutes program, the General
Services Administration’s AI Center of Excellence (in partnership with the
Departments of Agriculture and Health and Human Services as well as the JAIC),
the Alan Turing Institute in the U.K., and in Canada the Montreal Institute forLearning Algorithms (Mila),46 the Alberta Machine Intelligence Institute (Amii),47 andthe Vector Institute for Artificial Intelligence.48 On the U.S. side, this could involve
building on industry and academic efforts like the Stanford Institute for HumanCentered Artificial Intelligence.49
o Fostering of binational R&D foundations. ETC members may consider developing
targeted, binational R&D efforts modeled on the unique binational foundations thatfacilitate U.S.-Israel and U.S.-India R&D on cutting edge issues.50 These can serve
as models for other allies and partners to convene international researchers.
Critical Area #3 – Promoting Democracy, Human Rights, and the Rule of Law• Objectives:o Collaborative, coordinated efforts to counter anti-democratic uses of AI andemerging technologies through coordinated policy, regulatory alignment (such as
end-user export restrictions), and technology deployment.
o Potential priorities include countering censorship, countering malign information
operations, and promoting democratic models of surveillance technology, although
the ETC should explore a range of potential applications.
o Furthering these normative priorities will build on implementation methods
addressed in other Critical Areas, especially #2 (joint R&D), #5 (protecting andpromoting innovation), and #7 (the International Digital Democracy Initiative).
• Countering censorship and authoritarian uses of technology
o The ETC should explore efforts to use AI and associated technologies to further
internet freedom and counter censorship across the world. This work should bedesigned to implement the principles adopted in November 2020 by the Freedom
Online Coalition, a partnership of 32 governments aligned around promoting
human rights and individual freedom.51
o The United States should leverage the Open Technology Fund, created by the
FY 2021 NDAA, to support this effort, as well as related efforts by the Bureau of
Democracy, Human Rights, and Labor (DRL) at the Department of State.52
o The ETC should coordinate efforts in this space with the Council of Europe’s Ad Hoc
Committee on AI, established in November 2019 to focus on development, design,
and application of AI in areas of human rights, democracy, and the rule of law.53
o To promote private-sector conduct that comports with shared democratic values,
the ETC should develop a proposal for end-user controls that would disincentivize
private companies from exporting AI and associated technologies that may be used
to suppress and violate human and civil rights.54

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BLUEPRINT FOR ACTION: CHAPTER 15• Countering Malign Information Operations
o Malign information operations present a growing international challenge that iscompounded by the use of AI/ML technologies.
o This ETC should examine coordinated efforts (outside of the intelligence space)to counter disinformation and other information operations. Joint efforts include
detecting, moderating, identifying, and classifying malign information, developing
standards and best practices, and training experts.
o The Commission recommends creation of an International Task Force to Counterand Compete Against Disinformation.55 An International Task Force to Counter andCompete Against Disinformation (ITF-CCAD) could be established as a joint project
between the United States and multiple countries, as well as the EU and NATO, tofurther joint efforts to enable content moderation and detection of disinformation,
develop standards for identifying and classifying misinformation and disinformation(to include deepfake detection), and share best practices and lessons learned
with allies. The private sector, academia, and civil society organizations would be
important partners in this effort.
o The ITF-CCAD should draw best practices from, and should work in coordination
with, the Global Internet Forum to Combat Terrorism,56 along with efforts of the
Department of State’s Global Engagement Center’s (GEC) Technology Engagement
Team (TET); the Federal Bureau of Investigation’s Foreign Influence Task Force(FITF); the European External Action Services’ Strategic Communication Task Force;the EU “Team Europe” initiative; and the NATO/StratCom Center of Excellence. IFTCCAD should additionally prioritize stress-testing rapid-response mechanisms andlook to fund open-source research.
o It should explore generating best practices for non-tech solutions, such as media
literacy, free press,57 and civic engagement initiatives, drawing on notable workby the Center for Strategic and International Studies’ Defending Democratic
Institutions project and the German Marshall Fund’s Alliance for Securing
Democracy.
• Surveillance technology that comports with democratic values.
o The ETC should dedicate a multifaceted effort to promoting surveillancetechnology that supports democratic values.58 In particular, the effort should focus
on (a) promoting technology that delivers a degree of protection for individual
privacy and for civil rights and civil liberties and limits the use of data collected
or combined in ways that enable re-identification, and (b) countering the global
deployment of surveillance technology used to undermine democratic values andindividual rights.
o Doing so will require coordinated R&D, messaging, and development assistancestrategies to support democratic alternatives to technology manufactured in
China.59
o Fostering the R&D necessary to provide alternatives will require public-private
coordination or partnerships at an international level (see Critical Area #2 forpotential mechanisms).
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Potential stakeholders for such a project include NSF, the National AI ResearchInstitutes, DARPA, NIST, various EU Centres of Excellence, research institutions(such as the Johns Hopkins University Applied Physics Laboratory, theA FAV O R A B L E I N T E R N AT I O N A L T E C H N O L O G Y O R D E R

Massachusetts Institute of Technology Computer Science & AI Laboratory, andthe Stanford Institute for Human-Centered AI), GPAI, and non-governmentalorganizations such as OpenMined.
Critical Area #4 – Exploring Ways to Facilitate Data Sharing• Objectives:o Address legal and regulatory barriers to international collaborative work; explore
bilateral and multilateral, general and specific approaches to enable data sharing,
pooling, and storing consistent with privacy, security, and other fundamental values,
including the viability of a Data Free Flow with Trust Agreement.
• Methods to implement coordinated approaches to data sharing.
o Development of shared data environments. Development of pooled data storage
centers, computational environments, and cloud and edge computing facilities
to pool data from different sources for free use by credentialed researchers. An
approach like this would prove particularly beneficial to improve data sharing
among members to the Five Eyes alliance.
o Agreement on foundational data documentation, labelling, archiving, and data
organization frameworks at international organizations. Data agreements among
members of alliances (such as NATO) or other international organizations would
facilitate support to collaborative R&D endeavors; for example, ongoing efforts at
the Organisation of Economic Co-operation and Development (OECD) AI Policy
Observatory and Global Partnership on Artificial Intelligence (GPAI).
o Agreements to share specific data sets with specified foreign partners. Narrowerin scope than the above two approaches, an agreement of this kind would allow
researchers from different countries to access the same data sets for their respective
projects. For example, in the context of COVID-19 and health care,60 countries
would need to address data labelling, data storage, data anonymization, data
security, and other issues on a joint basis or through a pilot project.
■■This type of effort could also include joint projects with allies to anonymize61
high-impact data sets for specific research or initiatives, such as National
Institutes of Health data sets and data sets maintained, for various purposes, by
DOE, the U.S. Agency for International Development (USAID), the U.S. Foodand Drug Administration, DARPA, IARPA, and the Department of State’s Center
for Analytics.
Diplomatic effort is needed to resolve divergent views over what constitutes
anonymized data, consent, and matters of public interest.

o Ad hoc data sharing arrangements on bilateral or multilateral bases. The ETC
should explore the willingness of strategic allies and partners to engage in targeted,non-treaty data-sharing arrangements. “Innovation sandbox” arrangements may
be designed to facilitate specific challenges across all domains—security, health,disinformation, environmental resilience, and so on.
o A multilateral data-sharing agreement founded on trust. The ETC should leadan effort to create a formal, potentially treaty-based approach to data sharing,
pooling, and storing with like-minded governments modeled on the data free flow
with trust (DFFT) concept introduced by Japan at the June 2019 G20 Summit.
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DFFT would permit the free flow of data between authorized parties upon meeting
specific standards, including intellectual property (IP), privacy, and cybersecurity
protections.62 The European Commission endorsed the DFFT concept in December
2020.63■■

A general DFFT would require significant consideration of data protection,
IP protection, privacy shield, and trade issues, both for the United Statesdomestically and for foreign partners.
A specific DFFT, on the other hand, focused on the free flow of data forparticular purposes—such as facilitating pandemic response efforts—would
have a greater chance of success and could be a model for targeted datasharing arrangements in other areas of shared interest.

o Development of a secure AI research resource infrastructure. A secure, cloud-based
infrastructure would provide researchers from partnered and allied countries accessto compute resources, diverse data sets, and controlled environments to enable
testing, for example of privacy-preserving ML techniques. Participating like-minded
governments would agree to and comply with common technical standards andnorms64 and risk-based frameworks that ensure privacy, security, reliability, respect
for the rule of law, and other appropriate parameters.
■

Such an infrastructure could be developed bilaterally or multilaterally andcould be a priority effort of the Commission’s proposed MAIRI. Research andacademic institutions could support the MAIRI effort with appropriate technicaland implementation assistance, while GPAI’s Data Governance Working Group
could support the development and utilization of engineering best practices.

Critical Area #5 – Promoting and Protecting Innovation• Objectives:o Develop an allied strategy to align and develop regulatory and legal regimes
in areas critical to fostering domestic and international innovation. These areas
include export controls, investment screening, supply chain assurance, emerging
technology investment, trade policy, IP, technology transfer, and researchprotection.
o Achieving such a strategy will require an integrated approach among allies andpartners, leveraging our full technology toolkit, upgrading capabilities and, where
necessary, developing new ones to counter threats. These efforts will require acoordinated strategic coordination plan to raise allied public awareness on issues
such as technology-transfer risks.
• Export Controls and Investment Screeningo The ETC should explore coordinated approaches to export controls and investment
screening. Cooperation in these areas is critical to ensure that like-minded nations
have the authority to unilaterally institute export controls and block predatory
investments that present risks to national and international security.
o The Commission recommends in Chapter 14 of this report that the United States
engage with allies and partners on legal reforms to (a) implement a coordinatedapproach to AI-related export controls and (b) enhance investment screening
procedures and enforcement.65
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o The Commission has also recommended as part of Chapter 15’s Blueprint for
Action that the United States should engage with allies and partners to align policy
guidance on exports as part of the International Digital Democracy Initiative (IDDI) to
promote technologies that comport with shared values and support free and open
societies.66
o As detailed in Chapter 14 of this report, export control priorities should include
targeted, high-end semiconductor manufacturing equipment (SME) components
needed to produce chips at the 16nm node and below. Additionally, states should
explore implementing targeted end-use and end-user controls on specific highend, AI-specialized chips to prevent their use in human rights violations.67
o Consideration should be given to appropriate economic incentives to supportalignment on export control and investment screening.
o The ETC should also pursue robust collaboration on foreign S&T and investment
flow monitoring—to include open-source intelligence—to utilize early warning
indicators related to strategic acquisition risks. Further, ETC partners must share
best practices to monitor smaller transactions that attempt to skirt existing controls.
• Supply Chain Assuranceo Leadership of the United States and its allies and partners in emerging technologies
is dependent on components sourced from strategic competitors or regions withsignificant geopolitical risk.
o The semiconductor manufacturing industry is a prime example of an industry thatis critical to U.S. and allied security, but which is heavily concentrated in specificgeographic regions and is therefore susceptible to supply chain shocks, particularlyin the event of a crisis.68
o The ETC should conduct a supply chain assessment and make recommendations
on integrated, multilateral approaches to coordinating critical technology
components to enhance international security while reducing collective
dependence on strategic competitors.69
o The ETC should also develop a strategic plan to fund key choke point technologies
and next-generation materials, approaches, and prototyping capabilities at
discovery, manufacturing, and applied scales.70• Emerging Technology Investmentso Likewise, investments in emerging technologies require coordinated action. 5G
presents a test case for the challenges of international and multilateral coordination.
The United States and partners have cooperated on developing alternatives toChinese 5G infrastructure multilaterally and bilaterally.
o The Commission offered recommendations regarding steps to promote domestic
development of 5G technology in its First Quarter Recommendations and urged
the United States to continue to work closely with key allies and partners on bothconstructive 5G technical solutions, and to ensure that global 5G networks aresafe and secure.71 Chapter 16 of this report details steps to promote domestic
development of biotechnology, 5G, quantum computing, autonomy and robotics,
advanced manufacturing, and energy systems, while Chapter 13 of this reportdetails steps to cultivate domestic innovation in microelectronics research andmanufacturing.72 The United States may engage key allies and partners on thesetechnologies.
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o The ETC can serve as a forum to explore these issues in a coordinated manner.
• Trade Policy
o Trade policy is a key lever for the United States and foreign governments to promote
an innovation environment. The ETC should consider coordinated approaches totrade policy to further innovation and strengthen national and international security.
• Intellectual Propertyo IP rights and regimes are critical to innovation in AI and emerging technologies.
The ETC should explore coordinated approaches to IP that could inform a mutual
agenda with the World Intellectual Property Organization’s (WIPO) Conversation on
AI and Intellectual Property, IP5,73 and forums with broader mandates.
o Coordination on assistance to nations in developing strong and aligned IP regimes.
The ETC can assist the United States and partners in prioritizing assistance tonations in improving their IP regimes to help facilitate innovation while deterring
IP theft. A more focused approach, through IP5 and WIPO, may prove more
impactful in scope and could help to harmonize efforts to shore up IP with respect toidentifiable international challenges.
■■

The United States should engage with key allies and partners to alignon critical aspects of IP, including patent eligibility for AI and associatedtechnologies, countering China’s narrative on winning the innovationcompetition, IP contractual ecosystem impediments to international
collaboration, IP protections for data, and the over-declaration of “standard
essential” patents and other efforts to efforts by countries to exploit standardssetting and licensing processes.74
These are among a set of 10 critical IP considerations that the Commission
proposes to guide U.S. efforts to reform IP policies and establish new IP regimes
for AI and critical emerging technologies in order to protect and promotenational security, innovation, and technology competitiveness.75

o Coordinated efforts to stop IP theft and counter cyber espionage. IP theft remains
a global concern. With a goal of protecting the economic viability of AI innovationand emerging technologies, the ETC should identify methods to strengthen theinternational framework for addressing the export of counterfeit goods, theft of IP
technology, forced technology transfers of foreign innovation, and cyber espionage.
• Research and Cyber Protectionso Promoting multilateral responses to research integrity and security. As theCommission has proposed, the United States should coordinate action with allies
and partners in developing multilateral responses to challenges to research
integrity and security posed by PLA-affiliated individuals and entities and topromote a commitment to open fundamental research.76■

A public-private research security clearinghouse that enables sharing of opensource information, data-driven assessments, decision-support resources, andeducation and training resources could strengthen this effort.77

o Promoting multilateral efforts to mitigate proliferating cyber vulnerabilities anddevelop AI-enabled defenses against cyber attacks. As the Commission has
proposed, the United States must prepare for AI-enabled cyber conflict. The UnitedStates should explore coordinating and joint efforts with key allies and partners.78
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Critical Area #6 – Developing AI-Related Talent• Objectives:o Cooperative efforts to enable government, military, academic, and privatesector talent exchanges and address challenges posed by immigration and visa
restrictions; development of joint AI and digital training and workforce-development
programs.
• Methods for furthering talent development globally. The ETC should explore
methods for achieving objectives, including the following:o Creating new models for international talent exchanges. International talentexchanges are powerful tools to further AI alignment, cross-pollinate ideas, andbuild AI-related skills and capabilities. In developing new approaches to talentexchanges, the ETC should consider:
■

■

■

■

■

Military officer exchanges to improve AI deployment and interoperability,
including among NATO, JAIC, DoD, and foreign defense ministries andmilitaries;
Analogous training and exchanges needed for U.S. and allied diplomats anddevelopment experts;
Government-to-government exchanges of AI experts to assist in building
tech and ethical expertise; exchanges to benefit industry-led multilateral andmulti-stakeholder efforts like SDOs, GPAI, OECD, and influence paths taken bypartners;
Talent exchanges and secondments in industry and academia (bothinternational industry-industry or academia-academia talent exchanges, aswell as government-industry/academia); andLeveraging research centers such as the proposed MAIRI to enable crossborder collaboration and talent exchanges.

o Coordinating AI training development programs and sharing of best practices forgovernment training and broader AI education programs (including in secondary
schools and universities to include computer science teaching and curriculum
development).
■

The ETC should explore methods for non-EU partner nations to coordinate on
the “Artificial Intelligence and Analytics” in the EU’s Digital Education Plan.

Critical Area #7 – International Digital Democracy Initiative• The Chapter 15 Blueprint for Action details the Commission’s recommendations for
coordinating foreign assistance, investment, and financing through the International
Digital Democracy Initiative.

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Chapter 15: A Favorable International Technology Order
Annex: Emerging Technology Coalition - Endnotes
See Interim Report and Third Quarter Recommendations, NSCAI at 213 (Oct. 2020), https://www.
nscai.gov/previous-reports/.
1

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendations for upholding democratic values, see the section on “Aligning Systems and Uses
with American Values and the Rule of Law” in Key Considerations for Responsible Development &Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).

2

Key technical standards organizations include the International Organization for Standardization(ISO), International Electrotechnical Commission (IEC), Institute of Electrical and Electronic Engineers(IEEE), the UN International Telecommunication Union’s Telecommunication Standardization Sector(ITU-T), and the Third Generation Partnership Project (3GPP). See Interim Report and Third Quarter
Recommendations, NSCAI at 205 (Oct. 2020), https://www.nscai.gov/previous-reports/.

3

See Second Quarter Recommendations, NSCAI at 89 (July 2020), https://www.nscai.gov/previousreports/.

4

See Interim Report and Third Quarter Recommendations, NSCAI at 213 (Oct. 2020), https://www.
nscai.gov/previous-reports/. These recommendations have been reprised and built upon in the
Chapter 15 Blueprint for Action.
5

6

Id.

The Commission recommends that NIST provide a set of standards, performance metrics, and tools
for qualified confidence in AI models, data, and training environments and predicted outcomes. The
Blueprint for Action also recommends that NIST provide guidance as the science on testing acrossresponsible AI attributes evolves. See the Chapter 7 Blueprint for Action.

7

8
Jenni Tennison, An Introduction to the Global Partnership on AI’s Work on Data Governance,
OECD AI Policy Observatory (Aug. 21, 2020), https://oecd.ai/wonk/an-introduction-to-the-globalpartnership-on-ais-work-on-data-governance.

The OECD has led the international community with its work around AI norms and policy
development. The May 2019 Principles on Artificial Intelligence was the first multilateral set ofprinciples adopted by governments. Launched in February 2020, the OECD AI Policy Observatory
facilitates dialogue between its global multi-stakeholders, provides evidence-based analysis on
20+ policy areas, promotes the adoption of the AI Principles, and bolsters the advancement andmonitoring of trustworthy AI systems that benefit society. The Network of Experts on AI (ONE AI)
is an informal advisory group of multidisciplinary and multi-stakeholder experts from more than
30 countries that provides policy, technical, and business expert input to inform OECD analysis
and recommendations. The OECD has also developed Guidelines on the Protection of Privacy andTransborder Flows of Personal Data and is working toward principles for trusted government accessto data. See generally OECD AI Policy Observatory, OECD.AI (last accessed Jan. 5, 2021), https://
oecd.ai/; OECD Privacy Guidelines, OECD (last accessed Jan. 4, 2020), http://www.oecd.org/digital/
ieconomy/privacy-guidelines.htm.

910
See the Technical Glossary of AI Terms Appendix of this report for definition of homomorphic
encryption and differential privacy techniques. Collaborative research in this area could draw
from promising R&D use cases, including the DARPA Brandeis program and the IARPA HECTOR
program. See Brandeis, DARPA (last accessed Sept. 18, 2020), https://www.darpa.mil/program/brandeis; Homomorphic Encryption Computing Techniques with Overhead Reduction (HECTOR),
IARPA (last accessed Sept. 18, 2020), https://www.iarpa.gov/index.php/research-programs/hector;
see the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for future R&D needed to advance capabilities for preserving and ensuring
American values and the rule of law, see the section on “Aligning Systems and Uses with American
Values and the Rule of Law” in Key Considerations for Responsible Development & Fielding of
Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).

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11
See First Quarter Recommendations, NSCAI at 11 (March 2020), https://www.nscai.gov/previousreports/.
12
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for future R&D needed to advance capabilities for TEVV, see the section on “System
Performance” in Key Considerations for Responsible Development & Fielding of Artificial Intelligence:
Extended Version, NSCAI (2021) (on file with the Commission). TEVV R&D includes complex system
testing to improve understanding of and confidence in emergent performance of composed AI
systems and improve methods to understand, predict, and control systems-of-systems to avoid
negative outcomes resulting from system interaction. In addition, R&D in a multi-agent scenario will
advance the understanding of interacting AI systems, including the application of game theory tovaried and complex scenarios and interactions between cohorts composed of a mixture of humans
and AI technologies. See also First Quarter Recommendations, NSCAI at 11 (March 2020), https://www.nscai.gov/previous-reports/.

The Commission has previously recommended that the United States devote greater resources to AI
modeling, simulation, and design. See First Quarter Recommendations, NSCAI at 6-13 (March 2020),
https://www.nscai.gov/previous-reports/.

13

14
See the Technical Glossary of AI Terms Appendix of this report for definition of one-shot (or fewshot) learning.

See First Quarter Recommendations, NSCAI at 11 (March 2020), https://www.nscai.gov/previousreports/.

15

See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendation for future R&D needed to advance capabilities for AI security and robustness, see
the section on “Engineering Practices” in Key Considerations for Responsible Development & Fieldingof Artificial Intelligence: Extended Version, NSCAI (2021) (on file with the Commission). See also First
Quarter Recommendations, NSCAI at 11 (March 2020), https://www.nscai.gov/previous-reports/.

16

17
See the Appendix of this report containing the abridged version of NSCAI’s Key Considerations
for Responsible Development & Fielding of AI. For additional details on the Commission’s
recommendations to test machine-machine/multi-agent interaction and for international collaborationand cooperation needed to align on how to test and verify AI system reliability and performance along
shared values , see the section on “System Performance” in Key Considerations for Responsible
Development & Fielding of Artificial Intelligence: Extended Version, NSCAI (2021) (on file with theCommission).
18
See Chapter 11 of this report and its associated Blueprint for Action for detail on potential priority
areas for AI research.
19
See Working Party on Nuclear Criticality Safety (WPNCS), OECD Nuclear Energy Agency, https://www.oecd-nea.org/science/wpncs/; International Co-operation in Nuclear Data Evaluation: An
Extended Summary of the Collaborative International Evaluated Library Organisation (CIELO) Pilot
Project, NEA No. 7489, OECD Nuclear Energy Agency (2019), https://www.oecd-nea.org/science/
wpec/documents/volume40.pdf.
20
GEOTHERMICA combines financial resources and expertise on geothermal energy researchand innovation from 16 countries and their regions. It “launches joint projects that demonstrateand validate novel concepts of geothermal energy deployment within the energy system, and that
identify paths to commercial large-scale implementation.” One of the three focus areas includes“smart integration into the energy system and operations.” Some of the projects have big-data andsmart-system aspects, such as the French National Project through the GEOTHERMICA HEATSTORE
project. GEOTHERMICA partners, like the U.S. Lawrence Livermore National Laboratory, haveexpertise in ML. See About GEOTHERMICA, GEOTHERMICA (last accessed Sept. 18, 2020), http://
www.geothermica.eu/about-geothermica/; French National Project, HEATSTORE (last accessed Sept.
18, 2020), https://www.heatstore.eu/national-project-france.html; American Partners, GEOTHERMICA(last accessed Sept. 18, 2020), http://www.geothermica.eu/matchmaking/united-states/.

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Chapter 15: A Favorable International Technology Order
Annex: Emerging Technology Coalition - Endnotes
Members of the partnership include the United States as well as Australia, China, Germany, Japan,
Russia, Austria, Costa Rica, Iceland, ROK, South Africa, Brazil, India, the Netherlands, Canada,
France, Italy, and Norway. See International Partnership for Hydrogen and Fuel Cells in the Economy,
U.S. Department of Energy (last accessed Sept. 18, 2020), https://www.energy.gov/eere/fuelcells/
international-partnership-hydrogen-and-fuel-cells-economy; International Partnership for Hydrogenand Fuel Cells in the Economy, IPHE (last accessed Sept. 18, 2020), https://www.iphe.net/.
21

22
See Interim Report and Third Quarter Recommendations, NSCAI at 153 (Oct. 2020), https://www.
nscai.gov/previous-reports/.

Chapter 16 and its associated Blueprint for Action recommends that the United States pursue
global cooperation on smart disease monitoring.

23

Jason Matheny, et al., The Role of AI Technology in Pandemic Response and Preparedness:
Recommended Investments and Initiatives, NSCAI (June 25, 2020), https://www.nscai.gov/whitepapers/covid-19-white-papers/.

24

25
See Chapter 11 of this report and its associated Blueprint for Action for its recommendation for theUnited States to take on some of humanity’s biggest challenges.
26
NanoEHS CORs, US-EU Nanotechnology Communities of Research (CORs) (last accessed Sept.
18, 2020), https://us-eu.org/communities-of-research/.

See, e.g., Jeff Mason, et al., An Overview of Clinical Applications of Artificial Intelligence, Canadian
Agency for Drugs and Technologies in Health (Sept. 2018), https://www.cadth.ca/sites/default/files/
pdf/eh0070_overview_clinical_applications_of_ AI.pdf.

27

28

Feed The Future (last accessed Feb. 3, 2021), https://www.feedthefuture.gov/.

Automation and Artificial Intelligence in Agriculture: The Future of Maintaining Food Security
and Sustainable Intensification, Frontiers (last accessed Feb. 3, 2021), https://www.frontiersin.org/
research-topics/15206/automation-and-artificial-intelligence-in-agriculture-the-future-of-maintainingfood-security-and-su.

29

30
The World Economic Forum has noted 160 million people a year are at risk from natural disasters
and sees great benefit in AI from “reducing the time to assess damage to monitoring social media
to more quickly and effectively deliver aid” while “sharpen[ing] the decisions of relief workers on thefront lines.” Ashley van Heteren, et al., Natural Disasters are Increasing in Frequency and Ferocity.
Here’s How AI Can Come to the Rescue, World Economic Forum (Jan. 14, 2020), https://www.
weforum.org/agenda/2020/01/natural-disasters-resilience-relief-artificial-intelligence-ai-mckinsey/.

David Vergun, DOD Partners With Agencies to Use AI for Disaster Relief, Humanitarian Relief, DOD
News (Aug. 20, 2020), https://www.defense.gov/Explore/News/Article/Article/2319945/dod-partnerswith-agencies-to-use-ai-for-disaster-humanitarian-relief/.
31

32
Prashanth Parameswaran, What’s in the New US-Singapore Artificial Intelligence Defense
Partnership?, The Diplomat (July 1, 2019), https://thediplomat.com/2019/07/whats-in-the-new-ussingapore-artificial-intelligence-defense-partnership/.

See the discussion in Chapter 11 and its associated Blueprint for Action on using AI to tackle someof humanity’s biggest challenges.

33

34
On asteroids, see Deep Asteroid, NASA (May 27, 2016), https://open.nasa.gov/innovation-space/deep-asteroid/.

AI Challenged to Stave off Collisions in Space, European Space Agency (Oct. 9, 2019), https://www.
esa.int/Enabling_Support/Space_Engineering_Technology/AI_challenged_to_stave_off_collisions_in_space; Angelica Mari, UK Government Seeks Innovations to Tackle Space Debris, Computer
Weekly (May 28, 2020), https://www.computerweekly.com/news/252483762/UK-government-seeksinnovations-to-tackle-space-debris.

35

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36
Andrew Myers, Stanford Develops an AI Navigation System for a Future Satellite ‘Tow Truck,’
Stanford News (Feb. 1, 2019), https://news.stanford.edu/2019/02/01/stanford-spurs-ai-navigationspace-rendezvous-software/.

Leslie D’Monte, Chandrayaan-2 Pragyan Shows How AI is Helping Space Exploration, Mint (Sept.
6, 2019), https://www.livemint.com/technology/tech-news/chandrayaan-2-pragyan-shows-how-ai-ishelping-space-exploration-1567764065716.html.
37

Mike Wall, New, Emotionally Intelligent Robot CIMON 2 Heads to Space Station, Space.com (Dec. 5,2019), https://www.space.com/cimon-2-artificial-intelligence-robot-space-station.html.

38

The EU intends to establish Centres of Excellence and Digital Innovation Hubs focused on AI.
ICEMAI works with the Government of Canada’s Advisory Council on Artificial Intelligence, ForumIA Quebec, and the International Observatory on the Societal Impacts of Artificial Intelligence andDigital Technologies and is supported by the governments of Canada and Quebec with up to $15
million in funding over five years. INRIA was launched in February 2020 and has a contract with theGovernment of France to focus on “speeding up development of France’s scientific and technologicalleadership, as part of a Europe-wide approach,” including prioritizing AI and other digitaltechnologies to meet societal challenges, constructing European research and innovation spaces,
strengthening the tech industrial base, reinforcing public policies, and developing leading research
universities. See Communication Artificial Intelligence for Europe, European Commission (April 25,2018), https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligenceeurope; The Global Partnership on Artificial Intelligence Officially Launched, Montreal International(June 15, 2020), https://www.montrealinternational.com/en/news/the-global-partnership-on-artificialintelligence-officially-launched/; INRIA: For Scientific, Technological and Industrial Leadership in
Digital Technology, Government of France (Feb. 24, 2020), https://www.gouvernement.fr/en/inria-forscientific-technological-and-industrial-leadership-in-digital-technology.
39

40

See Implementation Plan to Chapter 15: A Favorable International Digital Order.

AI To Solve Global Issues, XPRIZE (last accessed Sept. 18, 2020), https://www.xprize.org/prizes/
artificial-intelligence.

41

Melissa Flagg & Paul Harris, System Re-engineering: A New Policy Framework for the American
R&D System in a Changed World, Center for Security and Emerging Technology (Sept. 2020), https://cset.georgetown.edu/research/system-re-engineering/.

42

The National AI Research Institutes is a joint government effort among the National Science
Foundation (NSF), U.S. Department of Agriculture (USDA) National Institute of Food and Agriculture(NIFA), U.S. Department of Homeland Security (DHS) Science & Technology Directorate (S&T), andthe U.S. Department of Transportation (DOT) Federal Highway Administration (FHWA). See National
Artificial Intelligence (AI) Research Institutes, NSF (last accessed Sept. 18, 2020), https://www.nsf.
gov/funding/pgm_summ.jsp?pims_id=505686.
43

See Chapter 8 of this report and its associated Blueprint for Action for its recommendation for
Congress to authorize NIST to sponsor a University Affiliated Research Center (UARC), Federally
Funded Research & Development Center (FFRDC), and/or a lab to provide independent, third-party
testing.

44

The AI4EU project was founded by the European Commission under the H2020 program toestablish the first European AI On-Demand Platform and Ecosystem. The Platform is designed tosupport the AI ecosystem and provide a forum to share AI resources from European projects. See
About the Project, AI4EU (last accessed Sept. 18, 2020), https://www.ai4eu.eu/about-project.

45

46

AI for Humanity, Mila (last accessed Sept, 18, 2020), https://mila.quebec/ia-dans-la-societe/.

47
Artificial Intelligence For Good and For All, Amii (last accessed Sept. 18, 2020), https://www.amii.
ca/.

See Pan-Canadian AI Strategy, CIFAR (last accessed Feb. 4, 2021), https://www.cifar.ca/ai; About
Us, Vector Institute for Artificial Intelligence (last accessed Sept. 18, 2020), https://vectorinstitute.ai/
about/.

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Chapter 15: A Favorable International Technology Order
Annex: Emerging Technology Coalition - Endnotes
See Stanford Institute for Human-Centered Artificial Intelligence (last accessed Sept. 18, 2020),
https://hai.stanford.edu/welcome.

49

50
The U.S. has strong research ties to Israel through the Binational Science Foundation (BSF) andthe Binational Industrial Research & Development Foundation (BIRD). See About the BSF, U.S.-Israel
Binational Science Foundation (last accessed Feb. 3, 2021), https://www.bsf.org.il/about/; What is
BIRD? U.S.-Israel Binational Industrial Research and Development (last accessed Feb. 3, 2021),
https://www.birdf.com/what-is-bird/. The Indo-U.S. Science and Technology Forum (IUSSTF) oversees
the United States-India Science & Technology Endowment Fund (USISTEF), which supports andfosters joint applied R&D. About the Fund, Indo-U.S. Science and Technology Forum (last accessed
Feb. 3, 2021), https://www.iusstf.org/usistef/us-india-science-technology.
51
See Joint Statement on Artificial Intelligence and Human Rights, Freedom Online Coalition (lastaccessed Jan. 5, 2021), https://freedomonlinecoalition.com/wp-content/uploads/2020/11/FOC-JointStatement-on-ArtificiaI-Intelligence-and-Human-Rights.pdf.

The FY 2021 NDAA created the Open Technology Fund as Section 309A of the U.S. International
Broadcasting Act of 1994. Pub. L. 116-283, sec. 1299P, William M. (Mac) Thornberry National Defense
Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021). Congress has since appropriated$20 million to the fund. See Joint Explanatory Statement, Division K– Department of State, Foreign
Operations, and Related Programs Appropriations Act, 2021 at 23 (2021), https://www.appropriations.
senate.gov/imo/media/doc/Division%20K%20-%20SFOPS%20Statement%20FY21.pdf (enacted inPub. L. 116-260, the Consolidated Appropriations Act, 2021). The Department of State’s Internet
Freedom and Business & Human Rights Section (IFBHR), within DRL, leads U.S. Government policy
and engagement to protect human rights online. See Internet Freedom: Fact Sheet, U.S. Departmentof State (Nov. 17, 2017), https://2017-2021.state.gov/internet-freedom/index.html. IFBHR works
across the U.S. Government, with democratic nations, with civil society, and with the Freedom
Online Coalition. IFBHR’s program includes funding development of censorship-defeating peer-topeer communications technologies. See Internet Freedom: Advancing and Promoting Peer-to-Peer
Communications Technologies, U.S. Department of State (Feb. 13, 2020), https://2017-2021.state.gov/
internet-freedom-advancing-and-promoting-peer-to-peer-communications-technologies/index.html.

52

Isaac Ben-Israel, et al., Towards Regulation of AI Systems: Global Perspectives on the Developmentof a Legal Framework on Artificial Intelligence (AI) Systems Based on the Council of Europe’s
Standards on Human Rights, Democracy and the Rule of Law, CAHAI Secretariat (Dec. 2020), https://rm.coe.int/prems-107320-gbr-2018-compli-cahai-couv-texte-a4-bat-web/1680a0c17a.
53

See the Chapter 14 Blueprint for Action. See also Guidance on Implementing the UN Guiding
Principles for Transactions Linked to Foreign Government End-Users for Products or Services with
Surveillance Capabilities, U.S. Department of State (last accessed Jan. 4, 2021), https://www.state.
gov/wp-content/uploads/2020/10/DRL-Industry-Guidance-Project-FINAL-1-pager-508-1.pdf.

54

55

See the Chapter 1 Blueprint for Action on malign information for further details on this proposal.

56
About, Global Internet Forum to Combat Terrorism (last accessed Jan. 5, 2021), https://www.gifct.
org/about/.

Civil society in Taiwan has responded to the threat from disinformation in a number of ways,
including demonstrating outside compromised media firms, educating senior citizens on the ways
they may be exposed to disinformation, and the establishment of robust fact-checking groups suchas the Taiwan Fact Check Center, MyGoPen, Cofacts, and Rum Toast. These groups have worked withboth government and social media platforms to not only identify and remove disinformation, but also
to forensically trace disinformation back to sources in China. See Audrey Tang on Taiwan’s Digital
Democracy, COVID-19, and Combating Disinformation, The Stimson Center (March 18, 2020), https://www.stimson.org/2020/interview-with-taiwan-digital-minister-audrey-tang/.

57

For more on democratic use of surveillance technologies, see Chapter 8 of this report, Upholding
Democratic Values: Privacy, Civil Liberties, and Civil Rights in Uses of AI for National Security.

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59Kara Frederick, The Razor’s Edge: Liberalizing the Digital Surveillance Ecosystem, CNAS (Sept.
3, 2020), https://www.cnas.org/publications/reports/the-razors-edge-liberalizing-the-digitalsurveillance-ecosystem.

See OpenMined’s Efforts for the Coronavirus Pandemic: COVID Alert App, Private Set Interaction, A
Differential Privacy Wrapper and Private Identity, OpenMined (April 1, 2020), https://blog.openmined.
org/openmineds-efforts-for-the-coronavirus-pandemic/.

60

61
The U.S. and Europe should agree on a common definition for anonymized data to include a clearer
understanding of what constitutes “consent” and “matters of public interest.”

Remarks by Angel Gurria, OECD Secretary General, delivered at the 2019 G20 Leaders’ Summit–
Digital (AI, data governance, digital trade, taxation) (June 28, 2019), https://www.oecd.org/g20/
summits/osaka/2019-g20-leaders-summit-digital-osaka-june-2019.htm.

62

63
A New EU-US Agenda for Global Change, European Commission (Dec. 2, 2020), https://ec.europa.
eu/info/sites/info/files/joint-communication-eu-us-agenda_en.pdf.

The Commission’s Key Considerations and existing international principles could be leveraged,such as the OECD Principles on AI, the OECD Guidelines on the Protection of Privacy andTransborder Flows of Personal Data, the OECD Recommendation on Digital Security of Critical
Activities, the forthcoming OECD Principles on Trusted Government Access to Data, and others.

64

65
See Chapter 14 of this report and its associated Blueprint for Action for additional details on theCommission’s recommendations regarding aligning allied export control and investment screening
regimes. Within the U.S. government, the Departments of State and Commerce, on export controls,
and the Departments of State and the Treasury, on investment screening, have already begun such
work.
66

See the Chapter 15 Blueprint for Action.

67
See Second Quarter Recommendations, NSCAI at 63-67 (July 2020), https://www.nscai.gov/
previous-reports/. In particular, the United States, the Netherlands, and Japan should coordinate
export controls on extreme ultraviolet and ArF immersion lithography tools, as doing so would limit theability of China and other competitors to develop the high-end microelectronics that are increasingly
essential for AI. For additional details on the Commission’s recommendations regarding export
controls on SME, see Chapter 14 of this report.
68
See Chapter 13 of this report for additional details and recommendations on the microelectronics
supply chain.
69

See Chapters 3 and 14 of this report.

70
See Second Quarter Recommendations, NSCAI at 48 (July 2020), https://www.nscai.gov/previousreports; Andrew Imbrie et al., Agile Alliances: How the United States and Its Allies Can Deliver a
Democratic Way of AI, Center for Security and Emerging Technology at 16-17 (Feb. 2020), https://cset.georgetown.edu/research/agile-alliances/; Andrew Imbrie, et al., The Question of Comparative
Advantage in Artificial Intelligence: Enduring Strengths and Emerging Challenges for the UnitedStates, Center for Security and Emerging Technology at 33 (Jan. 2020), https://cset.georgetown.edu/
wp-content/uploads/CSET-The-Question-of-Comparative-Advantage-in-Artificial-Intelligence-1.pdf.

First Quarter Recommendations, NSCAI at 45 (March 2020), https://www.nscai.gov/previousreports/.

71

72

See Chapters 13 and 16 of this report.

“IP5” is the name of the forum of the world’s five largest IP offices that was set up to improvethe efficiency of the examination process for patents worldwide. See About IP5 Co-Operation,
fiveIPoffices (last accessed Jan. 4, 2021), https://www.fiveipoffices.org/about.

73

74

See Chapter 12 of this report and its associated Blueprint for Action.

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Chapter 15: A Favorable International Technology Order
Annex: Emerging Technology Coalition - Endnotes75

See Chapter 12 of this report and its associated Blueprint for Action.

76

See Chapter 10 of this report.

77
This approach has been recommended by Melissa Flagg and Zachary Arnold. See Melissa Flagg& Zachary Arnold, A New Institutional Approach to Research Security in the United States Defendinga Diverse R&D Ecosystem, Center for Security and Emerging Technology (Jan. 2021), https://cset.
georgetown.edu/research/a-new-institutional-approach-to-research-security-in-the-united-states/.
78

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See Chapter 1 and its associated Blueprint for Action on preparing for AI-enabled cyber conflict.

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Chapter 16:
Associated Technologies
Blueprint for Action

Recognizing that leadership in artificial intelligence (AI) relies on leadership across a suiteof emerging technologies, the United States must prioritize the research and development(R&D), application, and adoption of not just AI, but the technologies that enable it and areenabled by it. This process should be based on a careful analysis of the national security
threats and opportunities at the intersection of AI and its associated technologies. If the
U.S. government fails to adopt a more strategic approach to protecting and promoting U.S.
advantages in these areas, it risks jeopardizing the country’s technological leadership,economic prosperity, and national security.
In accordance with its mandate to consider both AI and “associated technologies,”
the Commission identifies and proposes steps to maintain U.S. leadership across the
spectrum of technologies it believes are most critical to U.S. national competitiveness. The
Commission then offers specific recommendations on how the United States can proactively
address the novel national security threats and opportunities posed by three technologies
in particular: biotechnology, quantum computing, and 5G telecommunications.1 Finally,
the Commission expands its analysis to include recommendations on a broader set of
emerging technologies critical to U.S. national competitiveness.
Technologies Critical to U.S. National Competitiveness
The Commission has identified eight technologies and related platforms that are key to
U.S. leadership. Two of these technologies—AI and microelectronics—are addressed
elsewhere in this report. The remaining six—biotechnology, quantum computing, 5G andadvanced networking, autonomy and robotics, advanced and additive manufacturing, andenergy systems—are covered below. These recommendations build on the Commission’s
previous work by providing actions the U.S. government could take to promote overall
U.S. leadership and long-term competitiveness across the constellation of emerging
technologies.
Recommendation

Recommendation: Identify and Prioritize Technologies Central to National CompetitivenessTo date, there is no whole-of-government consensus for which emerging technologies
are most critical to long-term strategic competitiveness and whose development mustp581

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be prioritized. Several government agencies have made independent attempts to definesuch a list: the 2018 National Defense Strategy,2 the list of “critical emerging technologies”
produced by the Department of Defense in response to Section 1793 of the FY 2019
National Defense Authorization Act (NDAA),3 the Department of Commerce’s 2018 Advance
Notice of Proposed Rulemaking (ANPRM) of controls on certain emerging technologies,4
the report by the President’s Council of Advisors on Science and Technology titled
Recommendations for Strengthening American Leadership in Industries of the Future from
2020,5 and the bill introduced by Senator Chuck Schumer in the 116th Congress with sevenbipartisan co-sponsors titled the “Endless Frontier Act.”6 Additionally, the White Housepublished the National Strategy for Critical and Emerging Technologies in October 2020,
which included a list of critical and emerging technologies.7 However, this document does
not explain why each of these technologies is essential to U.S. national competitiveness,nor does it include specific implementation plans for promoting their development andprotecting U.S. advantages in each.
These lists have substantial overlap, but no two lists are the same and no single list isauthoritative. Consequently, there is no whole-of-government consensus, and certainly nonational consensus, of which technologies are critical to U.S. national competitiveness,making it more difficult for the U.S. government to marshal private-sector investment, forlegislators to prioritize funding, and for U.S. government agencies to coordinate technology
protection and promotion. There is also no list around which the White House can organize
a national technology strategy and no coordinated mechanism within the U.S. government
to support financing of these priorities when there are market failures and private-sector
financing is insufficient.
Actions for the Executive Office of the President:• Define and prioritize the key emerging technologies in which U.S. leadership isessential.
o The Executive Office of the President, in consultation with departments andagencies, should publish a single, authoritative list of technologies and sectors
which are key to overall U.S. competitiveness, along with detailed implementationplans for each to ensure long-term U.S. leadership.
■

The implementation plans should identify specific subcomponents of eachtechnology that are most important, key choke points where competitors
could be blocked with minimal impact on U.S. industry, and where additional
resources are needed. These plans should include specific steps topromote domestic industry, ensure supply chain resiliency, and protect key
technologies from competitors. This list of technologies and the associatedimplementation plans will form the core of a National Technology Strategy, as
referenced in Chapter 9 of this report.

o The creation and maintenance of such a list and implementation plans will help
produce a national consensus regarding which industries are most important in theemerging techno-economic competition. The result will be an important message
to Congress regarding where the country must prioritize and expend resources,

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as well as a powerful demand signal to industry. The figure below includes eighttechnologies that the Commission recommends be considered for the list.

U.S. Government Lists of Critical Technologies
NSCAIProposed
CriticalTechnology List

2018
National
DefenseStrategy

DoD Listof Critical
Emerging
Technologies

Commerce
ANPRM onEmerging
Technologies

PCAST Listof Industriesof the FutureS.3832 Endless
FrontierAct

WH NatStrategy
for
C&ET

Artificial
Intelligence
Biotechnology
Quantum
Computing
Semiconductors
and Advanced
HardwareAutonomy andRobotics
5G andAdvanced
Networking
Advanced
Manufacturing
EnergySystems• Expand the loan authority of the Development Finance Corporation to include
domestic industrial base capabilities supporting key emerging technologies.
o The President should issue an Executive Order that expands the loan authority of
the U.S. International Development Finance Corporation (DFC) to include domestic
industrial base capabilities related to any of the aforementioned technologies that are
identified by the Executive Office of the President as key to overall U.S. competitiveness.
■

■

p
583

Specifically, the Executive Order should delegate authority under Title III of
the Defense Production Act to the DFC to issue loans that “create, maintain,
protect, expand, or restore domestic industrial base capabilities” supporting the
aforementioned list of technologies, or “the resiliency of relevant domestic supply
chains.” This new authority should be of indefinite duration.
This action would build off of Executive Order 13922, which expanded similar
domestic loan authorities to DFC related to industries supporting “the national
response and recovery to the COVID-19 outbreak” until 2022.8

A S S O C I AT E D T E C H N O L O G I E So Expanding the domestic authorities of the DFC as it relates to critical technologies
will help the government support key platforms and projects which are criticalto future U.S. national security and economic competitiveness but lack sufficientprivate-sector capital.
■

The DFC should coordinate with the Technology Competitiveness Council
recommended in Chapter 9 of this report to identify specific platforms that are
most in need of such financing.

Ensuring U.S. Leadership in Biotechnology
The combination of advances in AI and biology have the potential to reshape the global
economy for the next century. Progress in genetic sequencing has given researchers theability to read the “code of life.” Given the significant quantity of data involved, AI willbe essential to fully understanding how genetic code interacts with biological processes.
Finally, advances in synthetic biology and genetic editing will give researchers the ability
to manipulate this code to perform specific functions. Together, these techniques will
enable transformational breakthroughs in biology and underpin most future scientificbreakthroughs related to human health, agriculture, and climate science. The nation
which is best able to simultaneously leverage both technologies will have substantial
strategic advantages for the foreseeable future, potentially becoming a global leader
in pharmaceuticals, reducing its reliance on foreign supply chains, and even ensuring
it has a healthier and more capable population. These technological breakthroughs will
also cause the biotechnology sector to become a major driver of overall U.S. economic
competitiveness.
Recommendation: Prioritize the Development of an Advanced Biotechnology R&D
Ecosystem

Recommendation

The United States must invest in key platforms that better position the U.S. academic andcommercial biotech industry to benefit from AI-enabled advancements in biology. It should
specifically look to support platforms that aggregate biodata, and specifically genetic data,
in a secure manner in order to enhance the ability of U.S. researchers to utilize AI to facilitate
breakthrough biotechnology research and innovation. Additionally, the United States should
support efforts to expand the scope and sophistication of U.S. biofabrication capabilities
to ensure it can keep pace with forthcoming research advancements. It should specificallysupport efforts to transform the biotechnology industry away from its current, verticallyintegrated models and encourage the development of multiple standardized, merchantbiofabrication facilities. Doing so would expand access to advanced biofabrication tools
among startups and laboratories by allowing firms to rapidly design new molecules andmaterials via the cloud and place immediate orders for fabrication.

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Actions for Congress and the Department of Health and Human Services:• Fund and establish a world-class biobank for genetic data.
o Congress should fund efforts to build a world-class biobank within the National
Institutes of Health (NIH). The current leading U.S. genetic database, GenBank, isunderfunded, difficult to access, and poorly curated, particularly in comparison toother leading genetic databases such as the U.K. BioBank or the China National
GeneBank. The entity should be securely and easily accessible by legitimate
researchers; contain a wide variety of whole human, animal, and plant genomes,
including de-identified metadata about phenotypes; and aggregate other open andpotentially even proprietary datasets for specialized uses. It must also include strong
privacy protections for human genetic data. Creating and staffing such an entity
would likely require a budget of approximately $100 million per year, on top of upfront construction costs.9• Direct funding to support advanced biotech manufacturing initiatives throughentities such as BARDA.
o The Department of Health and Human Services should direct funds to support
advanced biotech manufacturing initiatives through entities such as the Biomedical
Advanced Research and Development Authority (BARDA), and Congress should
prioritize such initiatives in future health-related spending bills. This could takethe form of financial incentives for advanced biotech manufacturing firms focusedon sophisticated, flexible, cloud-based fabrication, or R&D funding to support
advanced manufacturing techniques.

Recommendation

Recommendation: Prioritize Advanced Biotechnology Capabilities as Imperative for
National Security and Economic CompetitivenessThe growing importance of biotechnology leadership to health, food, production, andscience also makes it a national security imperative that the United States take proactive
steps to facilitate long-term U.S. leadership in the field. Advancements in biotechnology
will also create novel national security challenges, ranging from engineered pathogens
to augmented competitor human physiological or mental capabilities. The United Statescurrently is not postured to address such challenges, and biological threats have rarely
been a priority issue for the U.S. national security community. The COVID-19 pandemic
clearly illustrates that the United States must think more broadly about national security
threats than it has in the past, and that biological threats in particular have the potential toimpose significant costs on U.S. society and security.
U.S. competitors see the potential for AI to spur new, transformational advances inbiotechnology. China in particular is actively seeking global leadership in both fields, sees
its AI and biotechnology strategies as mutually reinforcing, and believes the synergies
between the two will translate into military advantage.10 China also faces fewer barriers tocollecting, using, and combining human biological data given its disregard for individual
privacy and bioethical principles. The global reach of China’s genomics giant, BGI, poses
similar threats in the biotechnology sector as Huawei does in the communications sector.

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Actions for the Executive Office of the President:• Update the U.S. National Biodefense Strategy to include additional AI-enabledbiological threats.11
o The National Security Council should update its National Biodefense Strategy,
which currently only focuses on natural or engineered pathogens, to include awider vision of biological threats.12 The strategy should specifically examine how AI
could enable new biological advances which pose unique national security threats,such as human enhancement, and how U.S. competitors could utilize advantages
in biotechnology or biodata as an instrument of national power. It should also
specifically consider how AI could identify and counter the creation of advanced,
engineered pathogens which target certain elements of the U.S. population or foodsupply. AI is facilitating a rapid evolution of the biotechnology field, and the U.S.
biodefense strategy must evolve with it.
• Direct departments and agencies to prioritize initiatives that promote U.S.
biotechnology leadership.
o Directing departments and agencies to prioritize initiatives promoting U.S.
biotechnology leadership would include aggressively promoting funding for basic
research in biology, particularly applications of biology that utilize AI; focusing
resources on forecasting how AI will enable future biotechnology breakthroughs;
and continuing to cultivate talent both inside and outside the government, as wellas commercial activity at the nexus of AI and biology. This will require an entity
which is empowered to coordinate across the economic, technological, and security
spheres, such as the Commission’s recommended Technology CompetitivenessCouncil.13
Recommendation: Publicly Highlight BGI’s Links to the Chinese Government

Recommendation

BGI is China’s de facto national champion in genetic sequencing and research and isamong the world leaders in DNA sequencing. It has research affiliations with multiple U.S.
universities, including the University of Washington and Washington State University.14 BGI
has also benefited from substantial support from the Chinese government, as well as its
2013 acquisition of a competing U.S. firm, Complete Genomics.15 There are indications
that BGI’s links with the Chinese government may run deeper than it publicly claims, as it
built and operates China National GeneBank, the Chinese government’s national genetic
database, and has used PLA-owned supercomputers to process genetic information.16
Chinese diplomats have pushed BGI-built COVID-19 testing kits, including in the UnitedStates, and by August 2020 BGI had “sold 35 million rapid COVID-19 testing kits to 180
countries, and built 58 labs in 18 countries.”17
BGI may be serving, wittingly or unwittingly, as a global collection mechanism for Chinese
government genetic databases, providing China with greater raw numbers and diversityof human genome samples as well as access to sensitive personal information about key
individuals around the world. The highest levels of the United States government should
publicly state these concerns so as to raise awareness among the U.S. commercial andp
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academic biotechnology communities, as well as U.S. allies, many of which currently havepartnerships or business dealings with BGI.
Action for the Department of State:• Launch a strategic communications campaign to publicly highlight the links
between the Chinese government and BGI.
o The Secretary of State should personally voice concern about BGI’s ties to
the Chinese government and instruct the Department to conduct a strategic
communications campaign to highlight those links and warn of the dangers of
the Chinese government obtaining personal genetic information via BGI. The
Department should also warn BGI and the Chinese government that it will closely
monitor BGI’s activities, and that should BGI be utilized as a mass DNA-collection
apparatus for the Chinese government it could face additional U.S. regulatory
action.

Recommendation

Recommendation: Pursue Global Cooperation on Smart Disease Monitoring
While pivoting to a more competitive national approach toward biotechnology policy,
the United States should also pursue efforts to enhance global cooperation on disease
monitoring. By pooling existing open-source health-related data with improved early warning
signals and data on zoonotic spillovers and transmission of novel viruses, governments will
be better postured to use AI to predict and contain future pandemics. Combining increasedtransparency and data sharing on disease outbreaks with AI tools—which can enhanceearly outbreak detection and contribute to real-time disease monitoring—could provide
substantial benefit for global public health if all countries, including China, participated ingood faith.18
Action for the Departments of State and Health and Human Services:• Support multilateral efforts to promote smart disease monitoring.
o The Departments of State and Health and Human Services should lead and support
multilateral efforts to promote smart disease monitoring. In particular, the UnitedStates should pursue efforts to integrate and standardize international healthrelated data sets and combine them with global data about zoonotic spillovers
to allow for the utilization of AI technologies to create shared, predictive, global
disease-monitoring tools.
Ensuring U.S. Leadership in Quantum Computing
Quantum computing has the potential to create new national security threats andopportunities by enhancing the speed and precision of existing AI systems and creating
new capabilities that could fundamentally alter the strategic environment. For example,
quantum computers may be able to more efficiently optimize logistics for the military orp587

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discover new materials for weapon systems.19 Quantum sensors and communications
are also poised to revolutionize the collection and transfer of sensitive information, which
directly affects how AI is trained and deployed in national security use cases.20 Failure
to step up investment in the R&D of materials and components for quantum computers,
open-source software tools, and hybrid quantum-classical algorithms that leverage noisy
intermediate-scale quantum computers may leave the United States vulnerable to strategic
surprise on behalf of competitors.21
Recommendation: Transition from Basic Research to National Security Applications of
Quantum Computing

Recommendation

Although the United States is well-positioned to take advantage of its early success inthe basic science of quantum computing, the U.S. Government must increase its focus
on fielding national security applications or risk falling behind strategic competitors.
Most notably, China has made significant investments in military applications of quantum
computing in an attempt to offset U.S. strengths.22 The Department of Defense (DoD) is stillrefining its approach to rapidly transition commercial technologies from research to fieldingin high-cost, hardware-intensive sectors such as quantum computing. In the long term,
DoD should prioritize efforts to rapidly procure technology across its innovation offices, but
this process could take several years of dedicated effort. In the interim, announcements
of priority applications will help spur private-sector investment and innovation in quantum
computing despite the absence of an integrated technology-procurement apparatus.23
Action for the President:• The President should direct departments and agencies to announce priority use
cases of quantum computers.
o The National Quantum Coordination Office (NQCO) should coordinate an effort bydepartments and agencies represented on the National Science and Technology
Council (NSTC) Subcommittee on Quantum Information Science (QIS) to announce
their priority use cases of quantum computers. By reflecting the combined viewsof federal entities engaging with the private sector, this would signal that a market
for practical applications of quantum computing exists, set clear and specific goals
for the private sector to pursue, and incentivize additional private investment.
Some applications of quantum computers may be too sensitive to reveal publicly,
but those that can be announced will provide direction to the private sector andfacilitate the commercialization of quantum computers, which can then be applied
to national security use cases.
Recommendation: Foster a Vibrant Domestic Quantum Fabrication Ecosystem

Recommendation

Due to the strategic implications of quantum computing and its application to AI, theUnited States must take steps now to cement its long-term status as the global leader inthe design and manufacturing of quantum processing units (QPUs). To avoid the situationin which the U.S. semiconductor industry currently finds itself, the United States mustp588

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establish trusted and assured sources for critical materials and components of QPUs,
ranging from manufacturing equipment to superconductors and dilution refrigerators.24
Although these materials and components may not yet represent choke points, they will
inevitably become more specialized as the manufacturing processes required to design
and produce QPUs continue to advance. Rather than reshoring the entire supply chain
for QPUs, the United States should work with its allies to develop a resilient network ofsuppliers for critical components that directly impact U.S. national security.
However, a secure supply chain is not sufficient to ensure U.S. leadership in quantum
computing. To benefit from future breakthroughs in the field, the United States must createa robust domestic ecosystem for the research, development, and application of quantum
computers that attracts top-tier talent from around the world.25 The U.S. Government
should offer incentives for the R&D of quantum computers and their components
while simultaneously creating demand for national security applications of quantum
technologies. The Quantum Economic Development Consortium (QED-C), proposed in
the National Quantum Initiative (NQI) Act of 2018, is an important step toward extending
U.S. leadership in next-generation computer hardware for years to come.26
Action for Congress:• Enact a package of provisions that incentivizes the domestic design andmanufacturing of quantum computers and their constituent materials.
o A tax credit for expenditures made in the United States on research anddevelopment, manufacturing equipment, and workforce training related to thedevelopment of quantum computers is a necessary, albeit not sufficient, step tomaintain U.S. competitiveness in this area. This provision could be modeled on the
Alternative Simplified Credit (ASC), which provides a credit of 14% of expenditures
on R&D in excess of 50% of base period expenditures. To help startups on the
cutting edge of research and development access funding that allows themto scale, the U.S. Government should also provide loan guarantees and equity
financing.

Recommendation

Recommendation: Make Quantum Computing Accessible to Researchers via the National
AI Research Resource (NAIRR)
Despite recent advances in the fields of quantum hardware and software, fault-tolerant
quantum computers (FTQCs) capable of performing general-purpose tasks are unlikely
to replace classical computers anytime soon. In the near term, the United States should
invest in noisy intermediate-scale quantum (NISQ) computers that are capable of deriving
probabilistic solutions from imperfect qubits.27 Hybrid quantum-classical techniques havealso shown promise, whereby classical computers delegate certain tasks to purpose-built
quantum devices within the same workflow. However, resources suitable for developing thistype of software are not readily accessible.28 By making classical and quantum computersavailable in the same workflow, the U.S. Government would lower barriers to innovation forp589

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startups in the quantum computing space and attract top-tier talent from around the world.
The resulting public-private partnerships would also encourage the commercializationof quantum computers and help the U.S. Government adopt those products for national
security use cases.
Action for the Executive Branch:• Make classical and quantum computers available in the same workflow via the
National AI Research Resource.
o By providing access to both classical and quantum computers via the National AI
Research Resource (NAIRR), which the Commission recommended establishing in
its First Quarter Recommendations and describes in greater detail in Chapter 11 of
this report,29 the U.S. Government would help researchers from industry, academia,
and government build and test software tools and algorithms that leverage bothclassical and quantum computers in a hybrid fashion. These types of applications
are likely to be the nearest-term use case of quantum computers.
Ensuring U.S. Leadership in 5G Telecommunications
AI systems require high-fidelity sensing as well as fast, safe, and secure networks. It is anational security imperative for the U.S. military and the nation as a whole to have accessto a powerful 5G network to enable future AI capabilities and ensure the network is trusted.
The United States must preserve this access and trust while building out commercial 5G
networks domestically and internationally.
Recommendation: Accelerate U.S. 5G Deployment Through Spectrum Sharing

Recommendation

The slow rollout of 5G networks in the United States compared to China risks undermining
U.S. advances in AI, both in the government and the private sector.30 The sub-6 GHz
spectrum, sometimes referred to as the mid-band or the “goldilocks” band of spectrum,is the critical portion of the spectrum for both DoD and commercial 5G operations. Sub6 GHz spectrum is critical for 5G civilian communications since it combines high data
rates with good range and penetration. Within DoD, it is also already used by many radarand communication systems because it also combines high discrimination capability
with long-range operations.31 In part due to its importance to military operations, DoD
has retained exclusive access to significant portions of the mid-band spectrum, which
limits commercial uses. Unfortunately, the lack of U.S. mid-band spectrum commercial
availability is substantially slowing the deployment of 5G networks domestically. Given thatsub-6 GHz is important for sensing using radar and civilian communications, spectrum
sharing between DoD and the private sector is the ideal approach to enabling access forboth purposes in a manner that balances national security and economic interests.32
Several U.S. Government agencies are working to address this problem by developing
spectrum-sharing capabilities within the 3- to 6-GHz range. In 2015, the Federalp590

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Communications Commission (FCC) established the Citizens Broadband Radio
Service (CBRS), the first U.S. spectrum sharing model.33 Since that time, the National
Telecommunications and Information Administration (NTIA) has studied, and has
collaborated with the DoD and FCC on, maximizing spectrum-sharing capabilities.34
The CBRS enables shared federal and non-federal use of the band. This work allows
the U.S. Navy and non-government providers to share the 3550-3700 MHz band acrossthree dynamically managed tiers: the Navy will maintain first priority access, followedby companies and organizations that purchase priority-access licenses, and finallycompanies and organizations that register at no cost. The FCC held its first auction forpriority-access licenses for this band in July 2020, which raised more than $4.5 billion
through the sale of 20,625 licenses.35 This is a promising but modest start and these
efforts must expand to a larger portion of the mid-band spectrum to be competitive with
China. To achieve spectrum sharing at a competitive level will require technical analysis
and engagement with industry. A comprehensive process will be critical to ensuring that
DoD maintains access to spectrum essential for operational effectiveness while alsobroadening commercial access to spectrum for civilian 5G networks.36
Action for the NTIA, FCC, and DoD:• Expand spectrum-sharing programs led by NTIA, FCC, and DoD, starting with a
one-year 5G spectrum-sharing demonstration program.
o The Commission urges NTIA, the FCC, and DoD to jointly expand spectrum-sharing
programs such as the CBRS and work to license additional sub-6GHz spectrum
to wireless carriers and equipment makers for commercial 5G use. Sharing andlicensing additional mid-band spectrum will ensure unrestricted DoD access in theevent of an emergency while also opening up 5G for commercial use. However,current spectrum-sharing capabilities must be further analyzed, tested, anddemonstrated before they can be scaled. The Commission supports a one-year
demonstration program that includes NTIA, FCC, DoD, and industry to assess the
network’s capabilities and its capacity to dynamically share spectrum between
government and civilian users. If successful, such a network would be rapidly scaledwith commercially available equipment.
Promote U.S. Leadership in Other Key Emerging Technologies
AI, microelectronics, biotechnology, quantum computing, and 5G telecommunications are
not the only emerging technologies that will underpin U.S. national competitiveness in the
21st century. The Commission assesses that the full spectrum of emerging technologies
key to U.S. technological leadership extends further and includes autonomy and robotics,
advanced manufacturing, and energy systems. The Commission therefore recommends
several actions to ensure U.S. leadership in these additional key emerging technologies.37

p591

A S S O C I AT E D T E C H N O L O G I E SAutonomy and Robotics
Recommendation: Incentivize the Development of World-Class Software Platforms for
Robotic and Autonomous Systems

Recommendation

Autonomous systems that rely on robotics to execute tasks in the real world are being applied
to everything from advanced manufacturing to warfighting.38 As AI continues to improve
the ability of these systems to match or exceed human capabilities, the United States
must position itself as a leading producer and adopter of robotic hardware and software
for civilian and military use cases. The United States currently lags behind countries such
as Japan and Korea on the manufacturing and installation of industrial robots, and China
has declared robotics as a core industry.39 As the United States reshores certain strategic
supply chains and increases its reliance on autonomous systems, continued access to
cutting-edge robotics will be a national security imperative.
Action for the National Institute of Standards and Technology:• Incentivize the development of world-class software platforms for robotic systems
by U.S. firms.
o By designing the software platforms upon which core robotic capabilities are built,
U.S. firms will be well-positioned to shape the next wave of industrialization. The
U.S. government should expand collaboration with industry on basic R&D, setinternational standards, and share data pertaining to robotic system developmentby expanding upon the work of the Intelligent Systems Division at the National
Institute of Standards and Technology (NIST).40 The U.S. government should
also incentivize the early adoption of robotic systems across the public and private
sectors by creating markets in areas ripe for automation.41 These efforts will yieldvaluable data and experience in scaling automation and facilitate the applicationof robotics to adjacent sectors. A multipronged approach along these lines will
position U.S. industry to compete more effectively in the market for robotic systems
software, a strategically important area that is compatible with existing U.S.
strengths.
Advanced Manufacturing
Recommendation: Accelerate Additive Manufacturing Production of Legacy Parts Acrossthe Department of Defense

Recommendation

The ability to manufacture high-tech products domestically is critical to a nation’s security
and its economic productivity. The United States must strive to develop manufacturing
capabilities in industries that are essential to crisis response or that would take too longto bring online in the event of a protracted conflict.42 Innovation also benefits from theco-location of firms engaged in technological design and those that produce finishedproducts, which enables rapid feedback and continuous iteration on product design.43
This link is particularly important in the defense sector, where communication between

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researchers, designers, and manufacturers can help quickly transition a technology fromthe lab to the field. However, the United States has relinquished manufacturing leadershipin high-tech industries that employ highly skilled workers to high-wage nations like Germany
and Japan.44 Meanwhile, China and other lower-wage nations are moving up the value
chain from low-value manufacturing processes, such as assembly, to more sophisticatedtechniques.45 Although the supply chain disruptions resulting from the COVID-19 pandemic
may prompt the return of some manufacturing to the United States, the broader trend of
offshoring the manufacturing of next-generation technologies appears likely to continue
unless the U.S. government takes appropriate action.46
Action for the Department of Defense:• Accelerate additive manufacturing of legacy parts across the Department of
Defense.
o Additive manufacturing and 3D printing have the potential to transform the
manufacturing industry by enabling the rapid production of complex objects on
demand and at the point of need.47 Although existing 3D printers cannot match
the quality of advanced traditional techniques, AI has shown the potential tosignificantly improve the accuracy of 3D printing.48 The DoD should proactively
support the improvement of 3D printing by identifying all legacy parts in active
weapon systems suited to production by additive manufacturing and 3D printersand commit to doing so by 2025.49
Energy Systems
Recommendation

Recommendation: Develop and Domestically Manufacture Energy Storage Technologies
to Meet U.S. Market Demand by 2030
Cheap and reliable access to energy is critical to U.S. national security. Although the UnitedStates is at the forefront of the exploration, extraction, and processing of oil and gas andpossesses significant domestic reserves, China is by far and away the leading producerof renewable energy and is investing heavily in advanced energy storage technologies,
such as batteries and their constituent materials.50 As the cost of intermittent renewablesources continues to fall, the United States must commit to developing and deploying thenext generation of energy storage devices, from long-duration stationary applications tobattery packs for electric vehicles.
Action for Congress:• Fund the Department of Energy’s initiative to develop and domesticallymanufacture energy storage technologies to meet U.S. market demand by 2030.
o Improving the cost and energy density of storage technologies will drive progressin sectors ranging from electric vehicles to distributed energy generation. The
Department of Energy (DoE) has set the ambitious goal of developing anddomestically manufacturing storage technologies capable of meeting the entiretyp593

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of U.S. market demand by 2030.51 Congress should fully fund the federal R&D
needed to achieve the DoE’s Energy Storage Grand Challenge roadmap by 2030
and establish appropriate incentives for the commercialization of the resulting
technologies.52
Further Consideration of Additional Technologies and Conclusion
While the Commission believes the eight emerging technologies discussed above andelsewhere in this report—AI, microelectronics, biotechnology, quantum computing, 5G
telecommunications, autonomy and robotics, advanced manufacturing, and energy
systems—will be crucial to future national competitiveness, this list is by no means
exhaustive. Other emerging technologies and platforms—everything from digital currencies
and other types of financial technology to space systems—will likely also play a major rolein the U.S. economy and its national security moving forward. And there are undoubtedly
technologies that have yet to be created which, in the near future, will have transformative
effects on the lives and security of American citizens.
We are at the beginning of a new era, in which technologies not only are the principal
driver of global markets and geopolitics, but they also advance and emerge faster thanever before. As the speed of technological development accelerates and an increasing
number of technologies have dual-use applications, techno-national security threats willcontinue to multiply. To meet this challenge, the U.S. government must continually assess
new technological advancements to determine their potential to disrupt industries, change
economies, and transform national security.
The process of technology horizon-scanning, forecasting, and proactively crafting
policies to address upcoming national security threats related to emerging technologies
must become an ingrained component of the U.S. national security process. Doing so
is not only essential, but also urgent. If the U.S. government waits to adapt to this new
reality until a subsequent commission makes a similar recommendation, it will likely beplaying technological catch-up from a position of national security weakness. As existing
technologies evolve and new ones emerge, the relationship between technology andnational security will only grow stronger, and the need for the United States to maintainoverall technical leadership will only increase.

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Blueprint for Action: Chapter 16 - Endnotes
The Commission identified these as essential to overall U.S. technological leadership in its 2019
Interim Report. See Interim Report, NSCAI at 31 (Nov. 2019), https://www.nscai.gov/previous-reports/.

1

2
Summary of the 2018 National Defense Strategy of the United States of America, U.S. Departmentof Defense at 3 (2018), https://dod.defense.gov/Portals/1/Documents/pubs/2018-National-DefenseStrategy-Summary.pdf.

Fiscal Year 2019 Industrial Capabilities: Report to Congress, U.S. Department of Defense at 132(June 23, 2020), https://www.businessdefense.gov/Portals/51/Documents/Resources/USA00095420%20RPT%20Subj%20FY19%20ICR%2007092020.pdf?ver=2020-07-10-124452-180.

3

4
83 Fed. Reg. 58201, Review of Controls for Certain Technologies, U.S. Department ofCommerce: Bureau of Industry and Security (Nov. 19, 2018), https://www.federalregister.gov/
documents/2018/11/19/2018-25221/review-of-controls-for-certain-emerging-technologies.
5
Recommendations for Strengthening American Leadership in Industries of the Future, President’s
Council of Advisors on Science and Technology (June 2020), https://science.osti.gov/-/media/_ /pdf/
about/pcast/202006/PCAST_ June_ 2020_Report.pdf.
6

S. 3832, 116th Cong. (2020), https://www.congress.gov/bill/116th-congress/senate-bill/3832.

National Strategy for Critical and Emerging Technologies, The White House at A-1 (Oct. 2020),
https://trumpwhitehouse.archives.gov/wp-content/uploads/2020/10/National-Strategy-for-CET.pdf.
7

Donald J. Trump, Executive Order 13922: Delegating Authority Under the Defense Production Act
to the Chief Executive Officer of the United States International Development Finance Corporation To
Respond to the COVID-19 Outbreak, The White House (May 14, 2020), https://www.federalregister.
gov/documents/2020/05/19/2020-10953/delegating-authority-under-the-defense-production-act-tothe-chief-executive-officer-of-the-united.

8

For comparison, the Chinese government provided approximately $117 million in initial funding to the
China National GeneBank for its construction and creation. See Zhuang Pinghui, China Opens First
National Gene Bank, Aiming to House Hundreds of Millions of Samples, South China Morning Post
(Sept. 22, 2016) https://www.scmp.com/news/china/article/2021623/chinas-noahs-ark-first-nationalgene-bank-opens-shenzhen.

910Elsa Kania, Minds at War: China’s Pursuit of Military Advantage Through Cognitive Science andBiotechnology, Prism (Jan. 2020), https://ndupress.ndu.edu/Portals/68/Documents/prism/prism_8-3/
prism_8-3_Kania_82-101.pdf.
11This recommendation is included in Chapter 1 of this report.

12
National Biodefense Strategy, The White House (2018), https://trumpwhitehouse.archives.gov/wpcontent/uploads/2018/09/National-Biodefense-Strategy.pdf.

See Chapter 9 of this report for additional details on the proposed Technology CompetitivenessCouncil.

13

14
See, e.g., BGI & US Collaborate on Precision Medicine Development, UW Medicine (May 10, 2016),
https://newsroom.uw.edu/story/bgi-uw-collaborate-precision-medicine-development.

In 2010, BGI received a $1.5 billion loan from the state-run China Development Bank. The precise
extent of government subsidies to BGI are unknown, but likely substantial. See Kirsty Needham,Special Report: COVID Opens New Doors for China’s Gene Giant, Reuters (Aug. 5, 2020), https://www.reuters.com/article/us-health-%20coronavirus-bgi-specialreport/special-report-covid-opensnew-doors-for-chinas-gene-giant-idUSKCN2511CE; see also Antonio Regaldo, China’s BGI Says
It Can Sequence a Genome for Just $100, MIT Technology Review (Feb. 26, 2020), https://www.
technologyreview.com/2020/02/26/905658/china-bgi-100-dollar-genome/.

15

16
China National Genebank Officially Opens, BGI (Sept. 22, 2016), https://www.bgi.com/us/company/
careers/china-national-genebank-officially-opens/.

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17See Kirsty Needham, Special Report: COVID Opens New Doors for China’s Gene Giant, Reuters(Aug. 5, 2020), https://www.reuters.com/article/us-health-coronavirus-bgi-specialreport/specialreport-covid-opens-new-doors-for-chinas-gene-giant-idUSKCN2511CE; see also Jeanne Whalen &Elizabeth Dwoskin, California Rejected Chinese Company’s Push to Help with Coronavirus Testing.
Was That the Right Move? Washington Post (July 2, 2020), https://www.washingtonpost.com/
business/2020/07/02/china-bgi-california-testing/.
18
See Chapters 9 and 15 of this report for additional information on cooperation on issues at theintersection of AI and global health.
19
Pontus Vikstål, et al., Applying the Quantum Approximate Optimization Algorithm to the TailAssignment Problem, Physical Review Applied Vol. 14, Iss. 3 (Sept. 3, 2020), https://doi.org/10.1103/
PhysRevApplied.14.034009; He Ma, et al., Quantum Simulations of Materials on Near-Term Quantum
Computers, npj Computational Materials (July 2, 2020), https://doi.org/10.1038/s41524-020-00353-z.
20
C.L. Degen, et al., Quantum Sensing, arXiv (June 7, 2017), https://arxiv.org/pdf/1611.02427.pdf;
Juan Yin, et al., Entanglement-Based Secure Quantum Cryptography over 1,120 Kilometres, Nature
582, 501-505 (June 15, 2020), https://doi.org/10.1038/s41586-020-2401-y.
21
In December 2020, a team of researchers in China demonstrated quantum advantage on a photonic
quantum computer. See Han-Sen Zhong, et al., Quantum Computational Advantage Using Photons,
Science (Dec. 18, 2020), https://science.sciencemag.org/content/370/6523/1460.
22
Elsa B. Kania & John Costello, Quantum Hegemony? China’s Ambitions and the Challenge to U.S.
Innovation Leadership, CNAS (Sept. 12, 2018), https://www.cnas.org/publications/reports/quantumhegemony.

For additional information and NSCAI views on quantum computing, AI, and national security, seeInterim Report and Third Quarter Recommendations, NSCAI at 154-163 (Oct. 2020), https://www.
nscai.gov/previous-reports/.

23

Applications of Quantum Technologies: Executive Summary, Defense Science Board at C-1(Oct. 2019), https://dsb.cto.mil/reports/2010s/DSB_QuantumTechnologies_Executive%20
Summary_10.23.2019_SR.pdf.

24

Dario Gil, How to Ensure the U.S.’s Quantum Future, Scientific American (Aug. 20, 2020), https://www.scientificamerican.com/article/how-to-ensure-the-uss-quantum-future/.

25

26
The bipartisan National Quantum Initiative Act of 2018 prompted a series of steps that establish
quantum computing as a strategic priority for the United States. The Department of Energy (DoE)
announced $625 million to establish five Quantum Information Science research centers over
five years led by the national laboratories. The National Science Foundation (NSF) announced$75 million to create three Quantum Leap Challenge Institutes over the same period. Lastly,
the President’s FY 2021 Budget recommended doubling federal investment levels in quantum
technologies by 2022. This continuing investment is necessary to determine the full potential ofquantum computing and maintain the United States’ position of leadership in next-generation
computer hardware. For more details, see Pub. L. 115-368, National Quantum Initiative Act, 115th
Cong. (2018), https://www.congress.gov/bill/115th-congress/house-bill/6227; Department ofEnergy Announces $625 Million for New Quantum Centers, U.S. Department of Energy (Jan. 10,2020), https://www.energy.gov/articles/department-energy-announces-625-million-new-quantumcenters; NSF Establishes 3 New Institutes to Address Critical Challenges in Quantum Information
Science, National Science Foundation (July 21, 2020), https://www.nsf.gov/news/special_reports/
announcements/072120.jsp; Recommendations for Strengthening American Leadership inIndustries of the Future, The President’s Council of Advisors on Science and Technology at 13 (June2020), https://science.osti.gov/-/media/_ /pdf/about/pcast/202006/PCAST_ June_ 2020_Report.
pdf?la=en&hash=019A4F17C79FDEE5005C51D3D6CAC81FB31E3ABC.

John Preskill, Quantum Computing in the NISQ Era and Beyond arXiv at 4, 14 (July 30, 2018),
https://arxiv.org/pdf/1801.00862.pdf.

27

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596

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Blueprint for Action: Chapter 16 - Endnotes
The Department of Energy and the Air Force offer access to commercial quantum capabilities, but
this access is not widespread, nor is it focused on hybrid quantum-classical software development.
See Adrian Cho, After Years of Avoidance, Department of Energy Joins Quest to Develop Quantum
Computers, Science (Jan. 10, 2018), https://www.sciencemag.org/news/2018/01/after-yearsavoidance-department-energy-joins-quest-develop-quantum-computers; Air Force ResearchLaboratory to Join IBM Q Network as First DOD-led IBM Q Hub, Wright-Patterson AFB (Aug. 2, 2019),
https://www.wpafb.af.mil/News/Article-Display/Article/1924271/air-force-research-laboratory-to-joinibm-q-network-as-first-dod-led-ibm-q-hub/.

28

See First Quarter Recommendations, NSCAI at 12-13 (March 2020), https://www.nscai.gov/
previous-reports/. In the FY 2021 NDAA, Congress took the first step toward implementing theCommission’s First Quarter recommendations by creating a task force to develop a road map for the
NAIRR. See Pub. L. 116-283, William M. (Mac) Thornberry National Defense Authorization Act for
Fiscal Year 2021, 134 Stat. 3388 (2021). In Chapter 11 of this report, the Commission urges Congress
to authorize and appropriate the funds necessary to carry out the task force’s road map immediately.

29

Dan Strumpf, U.S. vs. China in 5G: The Battle Isn’t Even Close, Wall Street Journal (Nov. 9, 2020),
https://www.wsj.com/articles/u-s-vs-china-in-5g-the-battle-isnt-even-close-11604959200.

3031Dana Deasy, Department of Defense Statement on Mid-Band Spectrum, U.S. Department of
Defense (Aug. 10, 2020), https://www.defense.gov/Newsroom/Speeches/Speech/Article/2307288/
department-of-defense-statement-on-mid-band-spectrum.

Milo Medin & Gilman Louie, The 5G Ecosystem: Risks & Opportunities for DoD, Defense Innovation
Board (April 3, 2019), https://media.defense.gov/2019/Apr/03/2002109302/-1/-1/0/DIB_5G_
STUDY_04.03.19.PDF.

32

3.5 GHz Band Overview, U.S. Federal Communications Commission (April 23, 2020), https://www.
fcc.gov/wireless/bureau-divisions/mobility-division/35-ghz-band/35-ghz-band-overview.

33

34
Edward Drocella, et al., Technical Feasibility of Sharing Federal Spectrum with Future Commercial
Operations in the 3450-3550 MHz Band, NTIA (Jan. 27, 2020), https://www.ntia.gov/report/2020/
technical-feasibility-sharing-federal-spectrum-future-commercial-operations-3450-3550.

See Public Notice: Auction of Priority Access Licenses in the 3550-3650 MHZ Band Closes, FCC(Sept. 2, 2020), https://docs.fcc.gov/public/attachments/DA-20-1009A1.pdf.

3536
In April 2019, the Defense Innovation Board issued a report which argued that the status quoof spectrum allocation is unsustainable and DoD must expand its sub-6GHz spectrum-sharing
operations to enable the United States to compete with China in 5G. Milo Medin & Gilman Louie,
The 5G Ecosystem: Risks & Opportunities for DoD, Defense Innovation Board (April 3, 2019), https://
media.defense.gov/2019/Apr/03/2002109302/-1/-1/0/DIB_5G_STUDY_04.03.19.PDF. For additional
information and views of the Commission on 5G, see First Quarter Recommendations, NSCAI at 54-57(March 2020), https://www.nscai.gov/previous-reports/.

Recommendations to ensure U.S. leadership in biotechnology, quantum computing, and 5G
telecommunications can be found above. Recommendations pertaining to semiconductors andadvanced computer hardware are covered in Chapter 13 of this report.

37

Summer Study on Autonomy, Defense Science Board (June 2016), https://dsb.cto.mil/reports/2010s/
DSBSS15.pdf.

38

39 China owns one-fifth of the global supply of industrial robots and sought to have 45% of its highend robots be produced domestically by the end of 2020. See Johnny Williamson, How Nations
Around the World Are Investing in Robotics Research, The Manufacturer (June 10, 2020), https://www.
themanufacturer.com/articles/how-nations-around-the-world-are-investing-in-robotics-research/.
Intelligent Systems Division, NIST (last accessed Feb. 2, 2021), https://www.nist.gov/el/intelligentsystems-division-73500.

40

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For example, the U.S. Postal Service could scale its Autonomous Mobile Robot pilot program from
25 sorting facilities to all sorting facilities by 2025. Autonomous Mobile Robots and the Postal Service,
U.S. Postal Service Office of Inspector General (April 9, 2018), https://www.uspsoig.gov/sites/default/
files/document-library-files/2019/RARC-WP-18-006.pdf.

41

An Executive Order 13806 report identifies 10 manufacturing risk archetypes. See Assessing
and Strengthening the Manufacturing and Defense Industrial Base and Supply Chain Resiliencyof the United States, U.S. Department of Defense (Sept. 2018), https://media.defense.gov/2018/
oct/05/2002048904/-1/-1/1/assessing-and-strengthening-the-manufacturing-and%20defenseindustrial-base-and-supply-chain-resiliency.pdf.

4243Strategy for American Leadership in Advanced Manufacturing, National Science and Technology
Council (Oct. 2018), https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/10/AdvancedManufacturing-Strategic-Plan-2018.pdf; Gregory Tassey, Rationales and Mechanisms for Revitalizing
US Manufacturing R&D Strategies, NIST (Jan. 29, 2010), https://www.nist.gov/system/files/
documents/2017/05/09/manufacturing_strategy_paper_0.pdf.

Report to the President on Ensuring American Leadership in Advanced Manufacturing, President’s
Council of Advisors on Science and Technology (June 2011), https://obamawhitehouse.archives.
gov/sites/default/files/microsites/ostp/pcast-advanced-manufacturing-june2011.pdf; Advanced
Manufacturing: A Snapshot of Priority Technology Areas Across the Federal Government, National
Science and Technology Council (April 2016), https://www.manufacturing.gov/sites/default/
files/2018-01/nstc_sam_technology_areas_snapshot.pdf.

44

Report to the President on Ensuring American Leadership in Advanced Manufacturing, President’s
Council of Advisors on Science and Technology at 3 (June 2011), https://obamawhitehouse.archives.
gov/sites/default/files/microsites/ostp/pcast-advanced-manufacturing-june2011.pdf.

4546
Advanced Manufacturing: Innovation Institutes Have Demonstrated Initial Accomplishments, but
Challenges Remain in Measuring Performance and Ensuring Sustainability, GAO-19-409 at 1 (May 23,2019), https://www.gao.gov/assets/700/699310.pdf.
47
Audit of the DoD’s Use of Additive Manufacturing for Sustainment Parts, U.S. Department of
Defense Inspector General (Oct. 17, 2019), https://media.defense.gov/2019/Oct/21/2002197659/-1/1/1/DODIG-2020-003.PDF.

Mark Anderson, 3D Print Jobs Are More Accurate With Machine Learning, IEEE Spectrum (Feb. 19,
2020), https://spectrum.ieee.org/tech-talk/artificial-intelligence/machine-learning/3d-print-jobs-newsaccurate-machine-learning.

48

For instance, in August 2020, the Department of Defense printed the first metal part for a B-52 jetengine. See Kyle Mizokami, The Old-School Engine That Powers the B-52 Gets a 3D-Printed Upgrade,
Popular Mechanics (Aug. 10, 2020), https://www.popularmechanics.com/military/aviation/a33535790/
air-force-3d-print-metal-part-turbofan-engine/.

49

Robert Rapier, Ten Countries That Dominate Fossil Fuel Production, Forbes (July 14, 2019), https://www.forbes.com/sites/rrapier/2019/07/14/ten-countries-that-dominate-fossil-fuel-production; Country
Rankings, International Renewable Energy Agency, (last accessed Feb. 2, 2021), https://www.irena.
org/Statistics/ View-Data-by-Topic/Capacity-and-Generation/Country-Rankings.

50

51Energy Storage, U.S. Department of Energy (last accessed Feb. 2, 2021), https://www.energy.gov/
oe/energy-storage.

Energy Storage Grand Challenge: Roadmap, U.S. Department of Energy (Dec. 2020), https://www.
energy.gov/sites/prod/files/2020/12/f81/Energy%20Storage%20Grand%20Challenge%20Roadmap.
pdf.

52

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APPENDICES

Appendicesp599

T H E N AT I O N A L S E C U R I T Y C O M M I S S I O N O N A R T I F I C I A L I N T E L L I G E N C E

Appendix A: Technical Glossary

601

Appendix B: Acronyms Found in This Report

617

Appendix C: Key Considerations for the Responsible
Development and Fielding of Artificial Intelligence (Abridged)

633

Appendix D: Draft Legislative Language

663

Appendix E: Funding Recommendation Table

729

Appendix F: Commissioner Bios

741

Appendix G: Commission Staff and Contributors

749

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APPENDIX A

Appendix A:
Technical Glossary
3D Chip Stacking: The process of building integrated circuits with both horizontal andvertical interconnections between transistors. This brings elements of the chip physicallycloser together, increasing density and allowing for greater performance (i.e., speed) atlower power levels and at a smaller footprint than comparable two-dimensional devices,which only feature horizontal interconnects.
Additive Manufacturing: A computer-controlled process in which successive layers ofmaterial are deposited to create a part that matches a 3D design.
Adversarial Machine Learning: A broad collection of techniques used to exploit
vulnerabilities across the entire machine learning stack and lifecycle. Adversaries may
target the data sets, algorithms, or models that an ML system uses in order to deceive
and manipulate their calculations, steal data appearing in training sets, compromise theiroperation, and render them ineffective.1 Adversarial AI may be used as a phrase thatbroadens the considerations to attacks on AI systems, including approaches that are lessdependent on data and machine learning.
Agile: A philosophy and methodology used to describe the continuous, iterative process
to develop and deliver software and other digital technologies. User requirements andfeedback inform incremental development and delivery by developers.2
AI Assurance: The defensive science of protecting AI applications from attack or
malfunction.
AI Digital Ecosystem: A technology stack driving the development, testing, fielding, andcontinuous update of AI-powered applications. The ecosystem is managed as a multilayer collection of shared AI essential building blocks (e.g., data, algorithms, tools, andtrained AI models) accessed through common interfaces.
AI Governance: The actions to ensure stakeholder needs, conditions, and options areevaluated to determine balanced, agreed-upon enterprise objectives; setting direction
through prioritization and decision-making; and monitoring performance and complianceagainst agreed-upon directions and objectives.3 AI governance may include policies on
the nature of AI applications developed and deployed versus those limited or withheld.

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TECHNICAL GLOSSARY

AI Lifecycle: The steps for managing the lifespan of an AI system: 1) Specify the system’s
objective. 2) Build a model. 3) Test the AI system. 4) Deploy and maintain the AI system. 5)
Engage in a feedback loop with continuous training and updates.4
AI Stack: AI can be envisioned as a stack of interrelated elements: talent, data, hardware,algorithms, applications, and integration.5
Algorithm: A series of step-by-step instructions or calculations to solve an instance ofa problem. There are fundamentally two ways that algorithms are implemented by AI:
explicit engineering of the algorithm (e.g., in symbolic reasoning and expert systems) or bymachine learning, where the algorithm is derived from data or feedback from interactions.
Anonymization: Also referred to as data de-identification, this is the process of removing
or replacing with synthetic values any identifiable information in data. This is intended tomake it impossible to derive insights on any specific individual in the data while remaining
useful for the intended use of the data.6 (See de-anonymization.)
Application Programming Interfaces (APIs): Programming tools for describing how one
program can access the functionality of another7 while hiding the implementation details
inside each program.
Application-Specific Integrated Circuit (ASIC): A chipset custom designed to perform aparticular task. ASICs could provide significant performance gains over generic chips but
are inflexible in their functions compared to central processing units.
Architecture: A set of values, constraints, guidance, and practices that support theactive evolution of the planning, designing, and construction of a system. The approachevolves over time, while simultaneously supporting the needs of current customers. 8
Architecture can refer to sets of components in a computing system and their operational
interrelationships as well as other important configurations such as the architecture of aneural network, which captures the patterns of connectivity within and between layers ofunits in the network model.
Artificial General Intelligence (AGI): A phrase that has been used to capture the possibilityof developing more general AI capabilities, in distinction to the typically narrow capabilities
of AI systems that have been developed to date. Some use the term to refer to the prospectof achieving more human-like intelligence, developing AI systems with the ability to performmany of the intellectual tasks that humans are capable of doing, or developing systems
that might employ a wide range of skills across multiple domains of expertise.
Artificial Intelligence (AI): The ability of a computer system to solve problems and to performtasks that have traditionally required human intelligence to solve.

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APPENDIX A

Auditability: A characteristic of an AI system in which its software and documentation
can be interrogated and yield information at each stage of the AI lifecycle to determine
compliance with policy, standards, or regulations.
Augmented Reality: Enhanced digital content, spanning visual, auditory, or tactile
information, overlaid onto the physical world.9
Authorization to Operate (ATO): The official management decision given by a seniororganizational official to authorize operation of an information system and to explicitly accept
the risk to organizational operations (including mission, functions, image, or reputation),organizational assets, individuals, and other organizations based on the implementation ofan agreed-upon set of security controls.10
Automation Bias: An unjustified degree of reliance on automated systems or their outcomes.
Autonomous: A system with functions capable of operating without direct human control.
Biological Sensors (Biosensors): Devices used to detect the presence or concentrationof a biological analyte, such as a biomolecule, a biological structure, or a microorganism.
Biosensors consist of three parts: a component that recognizes the analyte and produces
a signal, a signal transducer, and a reader device.11
Biometric Technologies: Technologies that leverage physical or behavioral humancharacteristics that can be used to digitally identify a person and grant access to systems,
devices, or data, such as face, voice, and gait recognition.12
Black Box: The nature of some AI techniques whereby the inferential operations are complex,
hidden, or otherwise opaque to their developers and end users in terms of providing an
understanding of how classifications, recommendations, or actions are generated andwhat overall performance will be.
Carbon Nanotubes: Nano-scale structures that can be used to make transistors and could
potentially replace silicon transistors in the future. Compared to existing silicon transistors,
carbon nanotube transistors are both capable of being shrunk to a smaller size and moreamenable to being stacked in three dimensions (see 3D chip stacking).
Cloud Computing: The act of running software within information technology environmentsthat abstract, pool, and share scalable resources across a network.13
Cloud Infrastructure: The components needed for cloud computing, which include
hardware, abstracted resources, storage, and network resources.14

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TECHNICAL GLOSSARY

Commonsense Reasoning: The process of forming a conclusion based on the basic
ability to perceive, understand, and judge things that are shared by (“common to”) mostpeople and can reasonably be expected without need for debate.15 Endowing computing
systems with the commonsense knowledge of humans has been found to be a difficult andstanding AI challenge.
Computational Thinking: The thought processes involved in formulating problems so theirsolutions can be represented as computational steps and algorithms.16
Computer Vision: The digital process of perceiving and learning visual tasks in order tointerpret and understand the world through cameras and sensors.17
Continuous Delivery: A process that builds on continuous integration by taking the stepof orchestrating multiple builds, coordinating different levels of automated testing, andmoving the code into a production environment in a process that is as automated as
possible.18
Continuous Integration: A process that aims to minimize the duration and effort requiredby “each” integration episode and deliver at any moment a product version suitable forrelease. In practice, this requires an integration procedure that is reproducible and mostly
automated. This is achieved through version control tools, team policies, and conventions.19
Data Architecture: The structure of an organization’s logical and physical data assets anddata management resources.20
Data Privacy: The right of an individual or group to maintain control over, and theconfidentiality of, information about themselves.21
Data Protection: The practice of safeguarding information from unauthorized access, use,
disclosure, disruption, modification, or destruction, to provide confidentiality, integrity, andavailability.22
De-anonymization: Matching anonymous data (also known as de-identified data) withpublicly available information, or auxiliary data, in order to discover the individual to whom
the data belong.23 (See anonymization.)
Deepfake: Computer-generated video or audio (particularly of humans) so sophisticatedthat it is difficult to distinguish from reality.24 Deepfakes have also been referred to assynthetic media.
Deep Learning: A machine learning implementation technique that exploits large quantities
of data, or feedback from interactions with a simulation or the environment, as training sets
for a network with multiple hidden layers, called a deep neural network, often employingp604

APPENDIX A

an iterative optimization technique called gradient descent, to tune large numbers ofparameters that describe weights given to connections among units.25
Deep Neural Networks (DNN): A deep learning architecture that is trained on data or
feedback, generating outputs, calculating errors, and adjusting its internal parameters. The
process is repeated possibly hundreds of thousands of times until the network achieves
an acceptable level of performance. It has proved to be an effective technique for image
classification, object detection, speech recognition, some kinds of game-playing, andnatural language processing––problems that challenged researchers for decades. Bylearning from data, DNNs can solve some problems much more effectively and also solve
problems that were never solvable before.26
Deployed AI: AI that has been fielded for its intended purpose within its relevant operational
environment.
DevSecOps: Enhanced engineering practices that improve the lead time and frequency
of delivery outcomes, promoting a more cohesive collaboration between development,
security, and operations teams as they work toward continuous integration and delivery.27
Differential Privacy: A criterion for a strong, mathematical definition of privacy in the context
of statistical and machine learning analysis used to enable the collection, analysis, andsharing of a broad range of statistical estimates, such as averages, contingency tables,and synthetic data, based on personal data while protecting the privacy of the individuals
in the data.28
Digital Ecosystem: The stakeholders, systems, tools, and enabling environments thattogether empower people and communities to use digital technology to gain access toservices, engage with each other, and pursue missional opportunities.29
Digital Infrastructure: The foundational components that enable digital technologies andservices. Examples of digital infrastructure include fiber-optic cables, cell towers, satellites,
data centers, software platforms, and end-user devices.30
Distributed System: A system whose components are located on different networked
computers, which communicate and coordinate their actions by passing messages to one
another in order to appear as a single system to the end user. 31
Domain-Specific Hardware Architectures: Hardware that is specifically designed to fulfill
certain narrow functions, seeking performance gains through specialization.
Edge Computing: A distributed-computing paradigm that brings computation and data
storage closer to the location where it is needed (i.e., the network edge where smart
sensors, devices, and systems reside along with points of human interaction) to improve
response times and save bandwidth.32
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TECHNICAL GLOSSARY

Expert System: A computer system emulating the decision-making ability of a human expert
through the use of reasoning, leveraging an encoding of domain-specific knowledge mostcommonly represented by sets of if-then rules rather than procedural code.33 The term
“expert system” was used largely during the 1970s and ’80s amidst great enthusiasm
about the power and promise of rule-based systems that relied on a “knowledge base” ofdomain-specific rules and rule-chaining procedures that map observations to conclusions
or recommendations.
Explainability: A characteristic of an AI system in which there is provision of accompanying
evidence or reasons for system output in a manner that is meaningful or understandable toindividual users (as well as to developers and auditors) and reflects the system’s process
for generating the output (e.g., what alternatives were considered, but not proposed, andwhy not).34
False Negative: An example in which the predictive model mistakenly classifies an item asin the negative class. For example, a false negative describes the situation in which a junkemail model specifies that a particular email message is not spam (the negative class)when the email message actually is spam, leading to the frustration of the junk message
appearing in an end user’s inbox.35 In a higher-stakes example, a false negative captures
the case in which a medical diagnostic model misses identifying a disease that is present
in a patient.
False Positive: An example in which the preductive model mistakenly classifies an item asin the positive class. For example, the model inferred that a particular email message wasspam (the positive class), but that email message was actually not spam, leading to delays
in an end user reading a potentially important message.36 In a higher-stakes situation, afalse positive describes the situation in which a disease is diagnosed as present when the
disease is not present, potentially leading to unnecessary and costly treatments.
Federated Data Repository: A virtual data repository that links data from distributed sources(e.g., other repositories), providing a common access portal for finding and accessing
data.
Field-Programmable Gate Array (FPGA): An integrated circuit featuring reconfigurable
interconnects that can be programmed by the user to be customized for specific functions
after it is manufactured. FPGAs feature greater flexibility than ASICs, but at a cost to
performance.
Gallium Nitride: An alternative material to silicon for transistors. Gallium nitride transistors
feature higher electron mobility than silicon and are capable of faster switching speed,
higher thermal conductivity, and lower on-resistance than comparable silicon solutions.

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Generative Adversarial Networks (GANs): An approach to training AI models usefulfor applications like data synthesis, augmentation, and compression where two neuralnetworks are trained in tandem: one is designed to be a generative network (the forger)
and the other a discriminative network (the forgery detector). The objective is for each
network to train and better itself off the other, reducing the need for big labeled training
data.37
Graphics Processing Unit (GPU): A specialized chip capable of highly parallel processing.
GPUs are well-suited for running machine learning and deep learning algorithms. GPUs
were first developed for efficient parallel processing of arrays of values used in computer
graphics. Modern-day GPUs are designed to be optimized for machine learning.
High-Performance Computing (HPC): Developing, deploying, and operating very highcapacity computers (along with the requisite software, hardware, facilities, and underpinninginfrastructure) to advance the computational upper limits of resolution, dimensionality, andcomplexity.38
Homomorphic Encryption: A technique that allows computation to be performed directly on
encrypted data without requiring access to a secret key. The result of such a computation
remains in encrypted form and can at a later point be revealed by the owner of the secretkey.39Human-Machine Teaming (or Human-AI Teaming): The ability of humans and AI systems
to work together to undertake complex, evolving tasks in a variety of environments withseamless handoff both ways between human and AI team members. Areas of effort include
developing effective policies for controlling human and machine initiatives,40 computing
methods that ideally complement people,41 methods that optimize goals of teamwork, anddesigns42 that enhance human-AI interaction.
Information Operations: The tactics, techniques, and procedures employed in both theoffensive and defensive use of information to pursue a competitive advantage.43
Internet of Things (IoT): A global infrastructure for the information society, enablingadvanced services by interconnecting (physical and virtual) things based on existing andevolving interoperable information and communication technologies.44
Intelligent Sensing: Utilizing advanced signal processing techniques, data fusion
techniques, intelligent algorithms, and AI concepts to better understand sensor data forbetter integration of sensors and better feature extraction, leading to actionable knowledge
that can be used in smart sensing applications.45
Interpretability: The ability to understand the value and accuracy of system output.
Interpretability refers to the extent to which a cause and effect can be observed withinp607

TECHNICAL GLOSSARY

a system or to which what is going to happen given a change in input or algorithmic
parameters can be predicted. Interpretability complements explainability.46
Legacy Systems: Outdated systems still in operation that are hard to maintain owing toshortage of skill sets and obsolete architecture.47
Machine Learning (ML): The study or the application of computer algorithms that improveautomatically through experience.48 Machine learning algorithms build a model based on
training data in order to perform a specific task, like aiding in prediction or decision-making
processes, without necessarily being explicitly programmed to do so.
Microelectronics: A subfield of electronics involving small components such as transistors,
capacitors, and resistors. These components are packaged together to form the integrated
circuits that are used to perform computations.
MLOps: Enhanced engineering practices that combine ML model development and ML
model operations technologies to support continuous integration and delivery of ML-based
solutions.49
Modeling and Simulation: Modeling the physical world to support the study, optimization,and testing of operations through simulation without interfering or interrupting ongoingprocesses. Modeling and simulation can be used to train AI systems, and AI technologies
can be used to enhance modeling and simulation.
Multi-Party Federated Learning: An ML setting where many clients (e.g., mobile devices
or whole organizations) collaboratively train a model under the orchestration of a central
server (e.g., service provider) while keeping the training data decentralized. It can mitigate
many of the systemic privacy risks and costs resulting from traditional, centralized ML anddata science approaches.50 However, it does introduce new attack vectors that must be
addressed.51
Multi-Source Data: Data obtained and aggregated from different origins.
Multimodal Data: Data comprising several signal or communication types, such as speech
and body gestures during human-to-human communication.
Natural Language Processing: The ability of a machine to process, analyze, and mimic
human language, either spoken or written.
Natural Language Understanding: The ability of a machine to represent and act on themeaning that a language expresses utilizing language semantically rather than statistically.
Neuromorphic Computing: Computing that mimics the human brain or neural network.52

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Object Recognition: The algorithmic process of finding objects in the real world from animage, typically using object models which are known a priori.53
One Shot (or Few Shot) Learning: An approach to machine learning that leverages existingknowledge to enable learning in some applications (e.g., object recognition) on a few
non-repeated examples, with the system rapidly learning similarities and dissimilarities
between the training examples.54
Open Knowledge Network (OKN): A vision to create an open knowledge graph of all known
entities and their relationships, ranging from the macro (e.g., have there been unusual
clusters of earthquakes in the U.S. in the past six months?) to the micro (e.g., what isthe best combination of chemotherapeutic drugs for a 56-year-old female with stage 3
brain cancer?). OKN is meant to be an inclusive, open, community activity resulting in aknowledge infrastructure that could facilitate and empower a host of applications and open
new research avenues, including how to create trustworthy knowledge networks/graphs.55
Packaging: The final stage of the semiconductor fabrication process, in which a chip isplaced in its protective case. For many years packaging was a low-value element of thesemiconductor design process. However, advanced packaging techniques are enabling
sophisticated new chip designs using processes such as 3D stacking, heterogeneous
integration, and modular chiplets to create more complex and sophisticated semiconductors.
Pattern Recognition: The field concerned with the automatic discovery of regularities indata through the use of computer algorithms, with the use of these regularities to takeactions such as classifying the data into different categories.56
Planning and Optimization: Determining necessary steps to complete a series of tasks,
which can save time and money and improve safety.
Platform Environment: Provides an application developer or user secured access toresources and tools (e.g., workflows, data, software tools, storage, and compute) on whichapplications can be developed or run.
Polymorphic Malware: A type of malware that constantly changes its identifiable features(i.e., signatures) in order to evade detection. Many of the common forms of malware canbe polymorphic, including viruses, worms, bots, trojans, or keyloggers.57
Precision: A metric for classification models. Precision identifies the frequency with which amodel was correct when classifying the positive class. It answers the question “How many
selected positive items are true positive?”—for example, the percentage of messages
flagged as spam that actually are spam.58
Prediction: Forecasting quantitative or qualitative outputs through function approximation,
applied on input data or measurements.59
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TECHNICAL GLOSSARY

Prior Art: The worldwide scientific and technical knowledge by which an invention isevaluated to determine if it is new.
Pseudonymization: A data management technique to strip identifiers linking data to anindividual. Concern exists that such data could still be linked with other data that allows fora person’s identity to be rediscovered.
PyTorch: A free and open-source software library for training neural networks and othermachine learning architectures, initially developed by Facebook AI Research.
Quantum Computer: A machine that relies on the properties of quantum mechanics toperform computations. Quantum computers encode information in qubits, which canexist in a linear combination of two states. These states can be physically realized ina number of ways, such as superconducting circuits, trapped ions, optical lattices, andlinear optics. Computation is performed by operating on the state of these qubits using
quantum logic gates. For example, if the qubit is realized as an ion, the quantum logic gate
might manipulate the ion’s energy state with lasers.
Recall: A metric for classification models. Recall identifies the frequency with which amodel correctly classifies the true positive items. It answers the question “How many true
positive items were correctly classified”? For example, the percentage of spam messages
that were flagged as spam.60
Reinforcement Learning: A method of training algorithms to make suitable actions by
maximizing rewarded behavior over the course of its actions.61 This type of learning cantake place in simulated environments, such as game-playing, which reduces the need forreal-world data.
Reliable AI: An AI system that performs in its intended manner within the intended domain
of use.
Responsible AI: An AI system that aligns development and behavior to goals and values.
This includes developing and fielding AI technology in a manner that is consistent withdemocratic values.62
Robotics: A broad field of study including autonomous systems that exist in the physicalworld, sensing their environment and taking actions to achieve specific goals.63
Robotic Process Automation (RPA): Software to help in the automation of tasks, especiallythose that are tedious and repetitive.
Robust AI: An AI system that is resilient in real-world settings, such as an object-recognition
application that is robust to significant changes in lighting. The phrase also refers toresilience when it comes to adversarial attacks on AI components.
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APPENDIX A

Self-Healing Robots: Robots that use structural materials to self-identify damage andinitiate healing on their own, repeatedly.64
Self-Replicating Robots: A means of manufacturing, so that fleets of autonomous rovers
can extract water and metals from local terrain—say on the moon or Mars—to construct
new industrial robots autonomously and continue the self-replication loop.
Self-Supervised Machine Learning: A collection of machine learning techniques that areused to train models or learn embedded representations without reliance on costly labeled
data; rather, an approach is to withhold part of each data sample and require the algorithm
to learn to predict the missing piece.65 Self-supervision has been used to train some of thelargest language models built to date by training on large amounts of natural language
data.66
Semi-Supervised Machine Learning: A process for training an algorithm on a combinationof labeled and unlabeled data. Typically, this combination will contain a very small amountof labeled data and a very large amount of unlabeled data. One approach is to use thecostly, smaller amount of labeled data to bootstrap a classification model, use that model
to generate predicted labels across the larger, unlabeled data, and then use the outcome
to retrain/refine the model and iterate until class label assignments stabilize.
Semiconductor Manufacturing Equipment (SME): The tools and equipment required
to fabricate semiconductors (e.g., extreme ultraviolet and argon fluoride immersion
lithography tools).
Semiconductor Photonics: As it relates to semiconductors, this refers to the use of light,
rather than electricity, to transfer information on a chip. This allows for much faster datatransfer speeds, resulting in significant performance improvements.
Semiconductors: The silicon-based integrated circuits that drive the operations andfunctioning of computers and most electronic devices.
Smart Sensors: Devices capable of pre-processing raw data and prioritizing the data totransmit and store, which is especially helpful in degraded or low-bandwidth environments.
Smart Systems: Information technology systems with autonomous functions enabled by AI.
Speech Recognition: The algorithmic process of turning speech signals into text orcommands.67
Supervised Machine Learning: A process for training algorithms by example. The training
data consists of inputs paired with the correct outputs. During training, the algorithm will
search for patterns in the data that correlate with the desired outputs and learn to predictthe correct output for newly presented input data over iterative training and model updates.
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TECHNICAL GLOSSARY

SWaP: Size, weight, and power, typically used in the context of reducing the overall
dimensions of a device, increasing its efficiency, and lowering the overall footprint andcost—all contributing factors to viable edge computing.68
Symbolic Logic: A tool for creating and reasoning with symbolic representations of objects
and propositions based on clearly defined criteria for logical validity.69
Synthetic Data Generation: The process of creating artificial data to mimic real sample data
sets. It includes methods for data augmentation that automate the process for generating
new example data from an existing data set. Synthetic data generation is increasingly
utilized to overcome the burden of creating large labeled datasets for testing and at times
training deep neural networks.
Technical Baseline: The government’s capability to understand underlying technology wellenough to make successful acquisition decisions independent of contractors.70
TensorFlow: A free and open-source software library for training neural networks and othermachine learning architectures, initially developed by Google Brain.
Test and Evaluation, Verification and Validation (TEVV) of AI Systems: A framework forassessing, incorporating methods and metrics to determine that a technology or system
satisfactorily meets its design specifications and requirements, and that it is sufficient for
its intended use.
Traceability: A characteristic of an AI system enabling a person to understand the technology,
development processes, and operational capabilities (e.g., with transparent and auditable
methodologies along with documented data sources and design procedures).
Unintended Bias: Ways in which algorithms might perform more poorly than expected(e.g., higher false positives or false negatives), particularly when disparate outcomes areproduced (e.g. across categories, classes or groups).
Unsupervised Machine Learning: A process for training a model in which the model learns
from the data itself without any data labels. Two common approaches are clustering (inwhich inherent groupings are discovered) and association (in which rules that describe
large portions of the data are discovered).71
Virtual Reality: A simulated experience in a computer-generated synthetic, artificial world
involving immersion, sensory feedback, and interactivity.72

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Appendix A - Endnotes
See Adversarial Machine Learning 101, GitHub/MITRE (last accessed Feb. 18, 2021), https://github.com/mitre/advmlthreatmatrix/blob/master/pages/adversarial-ml-101.md#adversarial-machinelearning-101; see also Ionut Arghire, Microsoft, MITRE Release Adversarial Machine Learning Threat
Matrix, Security Week (last accessed Feb. 16, 2021), https://www.securityweek.com/microsoft-mitrerelease-adversarial-machine-learning-threat-matrix.
1

GAO-20-590G, Agile Assessment Guide, U.S. Government Accountability Office at 169 (Sept.
2020), https://www.gao.gov/assets/710/709711.pdf.

2

3

See Glossary, ISACA (last accessed Feb. 13, 2021), https://www.isaca.org/resources/glossary.

Note that for data-driven AI systems, step 2 is expanded and replaced with 2.a) Acquire data tomeet the objective, and 2.b) Train the AI system on the data. These two steps are usually repeated,with data acquisition and training continuing until desired performance objectives are attained. For
further discussion on the ML lifecycle, see Saleema Amershi, et al., Software Engineering for Machine
Learning: A Case Study, IEEE Computer Society (May 2019), https://www.microsoft.com/en-us/
research/publication/software-engineering-for-machine-learning-a-case-study/.

4

The stack of elements listed here is an adaptation from Andrew W. Moore, Martial Hebert, andShane Shaneman. See Andrew Moore, et al., The AI Stack: A Blueprint for Developing and Deploying
Artificial Intelligence, Proc. SPIE 10635 (May 4, 2018), https://doi.org/10.1117/12.2309483. For agraphical depiction of the AI stack, see About, Carnegie Mellon University Artificial Intelligence (lastaccessed Jan. 1, 2021), https://ai.cs.cmu.edu/about.

5

See Recital 26 EU General Data Protection Regulation (EU-GDPR), PrivazyPlan (last accessed Feb.
17, 2021), https://www.privacy-regulation.eu/en/recital-26-GDPR.htm.

6

Vinton G. Cerf, APIs, Standards, and Enabling Infrastructure, Communications of the ACM, Vol. 62
No. 5, at 5 (May 2019), https://m-cacm.acm.org/magazines/2019/5/236425-apis-standards-andenabling-infrastructure/fulltext?mobile=true.

7

GAO-20-590G, Agile Assessment Guide, U.S. Government Accountability Office at 169 (Sept.
2020), https://www.gao.gov/assets/710/709711.pdf.

8

9

See Augmented Reality, Google (last accessed Feb. 13, 2021), https://arvr.google.com/ar/.

10
See Authorization to Operate, NIST Computer Security Resource Center (last accessed Feb. 13,2021), https://csrc.nist.gov/glossary/term/authorization_to_operate.
11

See Biosensors, Nature (last accessed Feb. 13, 2021), https://www.nature.com/subjects/biosensors.

12
Maria Korolov, What Is Biometrics? 10 Physical and Behavioral Identifiers That Can Be Used for
Authentication, CSO (Feb. 12, 2019), https://www.csoonline.com/article/3339565/what-is-biometricsand-why-collecting-biometric-data-is-risky.html.
13
See Understanding Cloud Computing, Red Hat (last accessed Feb. 13, 2021), https://www.redhat.
com/en/topics/cloud.
14
See What Is Cloud Infrastructure?, Red Hat (last accessed Feb. 13, 2021), https://www.redhat.com/
en/topics/cloud-computing/what-is-cloud-infrastructure.

See Matt Turek, Machine Common Sense (MCS), DARPA (last accessed Feb. 13, 2021), https://www.darpa.mil/program/machine-common-sense.
15

16
See Alfred V. Aho, Ubiquity Symposium: Computational and Computational Thinking, ACM (January2011), https://ubiquity.acm.org/article.cfm?id=1922682.

See Computer Vision: What It Is and Why It Matters, SAS (last accessed Feb. 13, 2021), https://sas.
com/en_in/insights/analytics/computer-vision.html.

17

18
GAO-20-590G, Agile Assessment Guide, U.S. Government Accountability Office at 171 (Sept.
2020), https://www.gao.gov/assets/710/709711.pdf.
19

p
613

Id. at 172.

TECHNICAL GLOSSARY

20
See Thor Olavsrud, What Is Data Architecture? A Framework for Managing Data, CIO (Nov. 4, 2020),
https://www.cio.com/article/3588155/what-is-data-architecture-a-framework-for-managing-data.html.

Digital Strategy 2020-2024, USAID at 48 (June 2020), https://www.usaid.gov/sites/default/files/
documents/15396/USAID_Digital_Strategy.pdf.

21

22

Id.

23
See Jake Frankenfield, De-Anonymization, Investopedia (Dec. 27, 2020), https://www.
investopedia.com/terms/d/deanonymization.asp#:~:text=De%2Danonymization%20is%20a%20
technique,person%2C%20group%2C%20or%20transaction.

Interim Report, NSCAI at 9 (Nov. 2019), https://www.nscai.gov/wp-content/uploads/2021/01/NSCAIInterim-Report-for-Congress_ 201911.pdf.

24

25

See Ian Goodfellow, et al., Deep Learning, MIT Press, (2016), https://www.deeplearningbook.org/.

26
Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to
DoD, MITRE, at 9-25 (Jan. 2017), https://fas.org/irp/agency/dod/jason/ai-dod.pdf.

See Understanding the Differences Between Agile and DevSecOps– From a Business Perspective,
General Services Administration (last accessed Feb. 13, 2021), https://tech.gsa.gov/guides/
understanding_differences_agile_devsecops/.

27

Kobbi Nissim, et al., Differential Privacy: A Primer for a Non-technical Audience, Working Groupof the Privacy Tools for Sharing Research Data Project, Harvard University (Feb. 14, 2018), https://
privacytools.seas.harvard.edu/files/privacytools/files/pedagogical-document-dp_new.pdf.

28

Digital Strategy 2020-2024, USAID at 4 (June 2020), https://www.usaid.gov/sites/default/files/
documents/15396/USAID_Digital_Strategy.pdf.

2930

Id. at 49.

Maarten van Steen & Andrew Tanenbaum, Distributed Systems (3rd ed.), distributed-systems.net(2017), https://www.distributed-systems.net/index.php/books/ds3/.

31

32
See Eric Hamilton, What Is Edge Computing: The Network Edge Explained, Cloudwards (Dec. 27,2018), https://www.cloudwards.net/what-is-edge-computing.
33

Peter Jackson, Introduction to Expert Systems (3rd ed.), Addison Wesley at 2 (1998).

34
For further discussion see P. Jonathon Phillips, et al., Four Principles of Explainable Artificial
Intelligence, NIST (Aug. 2020), https://www.nist.gov/system/files/documents/2020/08/17/NIST%20
Explainable%20AI%20Draft%20NISTIR8312%20%281%29.pdf.
35
See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2020), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.
36
See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2020), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.

Ian Goodfellow, et al., Generative Adversarial Nets, Neural Information Processing Systems (2014),
https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.

37

Fiscal Year 2019: Stockpile Stewardship and Management Plan– Biennial Plan Summary, Reportto Congress, U.S. Department of Energy at 3-7 (Oct. 2018), https://www.energy.gov/sites/prod/
files/2018/10/f57/FY2019%20SSMP.pdf.

38

39 See Introduction, Homomorphic Encryption Standardization (last accessed Feb. 13, 2021), https://
homomorphicencryption.org/introduction/.
Eric Horvitz, Principles of Mixed-Initiative User Interfaces, CHI ’99: Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems at 159-166 (May 1999), https://dl.acm.org/doi/
pdf/10.1145/302979.303030.

40

p
614

APPENDIX A

Appendix A - Endnotes
Bryan Wilder, et al., Learning to Complement Humans, Proceedings of the Twenty-Ninth
International Joint Conference on Artificial Intelligence (IJCAI-20) at 1526-1533 (Jan. 2021), https://www.ijcai.org/Proceedings/2020/0212.pdf.
4142Saleema Amershi, et al., Guidelines for Human-AI Interaction, CHI ’19: Proceedings of the CHI
Conference on Human Factors in Computing Systems, at 1-13 (May 2019), https://dl.acm.org/doi/
pdf/10.1145/3290605.3300233.
43
Catherine Theohary, Defense Primer: Information Operations, Congressional Research Service(Dec. 15, 2020), https://fas.org/sgp/crs/natsec/IF10771.pdf.

See interactive ITU Terms and Definitions, United Nations International Telecommunication
Union (last accessed Feb. 15, 2021), https://www.itu.int/net/ITU-R/asp/terminology-definition.
asp?lang=en&rlink={42AA741E-A0A7-48C4-905B-AAAFDA29E5F2}.

44

See Intelligent Sensors, MDPI Sensors (last accessed Feb. 13. 2021), https://www.mdpi.com/
journal/sensors/sections/Intelligent_Sensors.

45

46
See Richard Gall, Machine Learning vs Interpretability: Two Concepts That Could Help Restore Trustin AI, KDnuggets (Dec. 2018), https://www.kdnuggets.com/2018/12/machine-learning-explainabilityinterpretability-ai.html.
47
A. Sivagnana Ganesan & T. Chithralekha, A Survey on Survey of Migration of Legacy Systems, ICIA16: Proceedings of the International Conference on Informatics and Analytics at 1-10 (Aug. 2016),
https://dl.acm.org/doi/10.1145/2980258.2980409.
48

Thomas M. Mitchell, Machine Learning, McGraw-Hill (1997).

See 2021 Technology Spotlight: The Emergence of MLOps, Booz Allen Hamilton (2021), https://www.boozallen.com/content/dam/boozallen_site/dig/pdf/white_paper/the-emergence-of-mlops.pdf.
49

50
Peter Kairouz, et al., Advances and Open Problems in Federated Learning, arXiv (Dec. 10, 2019),
https://arxiv.org/pdf/1912.04977.pdf.
51
See Vale Tolpegin et al., Data Poisoning Attacks Against Federated Learning Systems, ArXiv (Aug.
11, 2020), https://arxiv.org/abs/2007.08432; Arjun Nitin Bhagoji, et al., Analyzing Federated Learning
Through an Adversarial Lens, arXiv (Nov. 25, 2019), https://arxiv.org/abs/1811.12470.

See Beyond Today’s AI: New Algorithmic Approaches Emulate the Human Brain’s Interactions with
the World, Intel (last accessed Feb. 13, 2021), https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html.

52

Ramesh Jain, et al., Machine Vision, McGraw-Hill at 459 (1995), https://www.cse.usf.edu/~r1k/
MachineVisionBook/MachineVision.files/MachineVision_Chapter15.pdf.
53

54
Adam Santoro, et al., One-Shot Learning with Memory-Augmented Neural Networks, arXiv (May 19,2016), https://arxiv.org/pdf/1605.06065.pdf.

See About Workshop, Open Knowledge Network at National Institutes of Health, Subcommittee on
Networking & Information Technology Research & Development, Big Data Interagency Working Group,(Oct. 4-5, 2017), https://www.nitrd.gov/nitrdgroups/index.php?title=Open_Knowledge_Network.

55

56
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer at 1 (2006), https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-MachineLearning-2006.pdf.

p615

TECHNICAL GLOSSARY

See Nate Lord, What Is Polymorphic Malware? A Definition and Best Practices for Defending
Against Polymorphic Malware, Digital Guardian (July 17, 2020), https://digitalguardian.com/
blog/what-polymorphic-malware-definition-and-best-practices-defending-against-polymorphicmalware#:~:text=Definition%20of%20Polymorphic%20Malware,bots%2C%20trojans%2C%20or%20
keyloggers.

57

See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2020), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.

58

59
Trevor Hastie, et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction(2nd ed.), Springer at 9-11 (Jan. 13, 2017), https://web.stanford.edu/~hastie/ElemStatLearn/printings/
ESLII_print12_toc.pdf.

See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2021), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.

60

61
Richard S. Sutton & Andrew G. Barto, Reinforcement Learning: An Introduction (2nd ed.), MIT Press(2018).
62
Key Considerations for Responsible Development and Fielding of Artificial Intelligence, NSCAI (July22, 2020), https://www.nscai.gov/previous-reports/.
63
See Erico Guizzo, What Is a Robot?, IEEE (May 28, 2020), https://robots.ieee.org/learn/what-is-arobot/.

See Evan Ackerman, Soft Self-Healing Materials for Robots That Cannot Be Destroyed, IEEE (Sept.
5, 2019), https://spectrum.ieee.org/automaton/robotics/robotics-hardware/soft-selfhealing-materialsfor-robots-that-cannot-be-destroyed.

64

65
See Andrew Zisserman, Self-Supervised Learning, Google DeepMind (last accessed Feb. 17, 2021),
https://project.inria.fr/paiss/files/2018/07/zisserman-self-supervised.pdf.
66
Jacob Devlin, et al., BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding, arXiv (May 24, 2019), https://arxiv.org/pdf/1810.04805.pdf.
67
Jianliang Meng, et al., Overview of the Speech Recognition Technology, 2012 Fourth International
Conference on Computational and Information Sciences at 199-202 (2012), https://ieeexplore.ieee.
org/document/6300437/.
68
See What Is Low-SWaP?, REDCOM (last accessed Feb. 13, 2021), https://www.redcom.com/whatis-low-swap-size-weight-and-power/.
69
Tony Roy, Symbolic Logic: An Accessible Introduction to Serious Mathematical Logic at 2-3 (Feb. 8,2021), https://tonyroyphilosophy.net/symbolic-logic/.

William LaPlante, Owning the Technical Baseline, Defense AT&L at 18-20, (July-Aug. 2015), https://apps.dtic.mil/dtic/tr/fulltext/u2/1016084.pdf.

70

See Jason Brownlee, Supervised and Unsupervised Machine Learning Algorithms, in Machine
Learning Algorithms, Machine Learning Mastery (Aug. 20, 2020), https://machinelearningmastery.
com/supervised-and-unsupervised-machine-learning-algorithms/.

71

See What Is Virtual Reality? Virtual Reality Society (last accessed Feb. 13, 2021), https://www.vrs.
org.uk/virtual-reality/what-is-virtual-reality.html.

72

p
616

APPENDIX B

Appendix B: Acronyms Foundin This Report(alphabetical order)

A

p
617

AAAI

Association for the Advancement of Artificial Intelligence

AaaS

applications as a service

AAMAS

Autonomous Agents and Multiagent Systems

AISC

AI Strategic Challenge

AAF

Adaptive Acquisition Framework

AAL

Army Applications Laboratory

ABMS

Advanced Battle Management System

ADL

Advanced Distributed Learning

AFC

Army Futures Command

AFOSR

Air Force Office of Scientific Research

AFRL

Air Force Research Lab

AGI

artificial general intelligence

ACM SIGKDD

Association for Computing Machinery’s Special Interest Group on Knowledge
Discovery and Data MiningAI

artificial intelligence

AI CoEAI Center of ExcellenceAIM

Augmenting Intelligence using Machines

AIPfd

AI Partnership for Defense

ACRONYMS FOUND IN THIS REPORT

Amii

Alberta Machine Intelligence Institute

ANPRM

Advance Notice of Proposed Rulemaking

API

Application Programming Interface

ArF

Argon fluoride

ARO

Army Research Office

ARPA

Academic Research Protection Act

ASC

Alternative Simplified Credit

ASIC

application-specific integrated circuit

ATO

Authorization (or Authority) to Operate

AVC

Bureau of Arms Control, Verification and Compliance

B
BA

Budget Activity

BARDA

Biomedical Advanced Research and Development Authority

BioMADE

Bioindustrial Manufacturing and Design Ecosystem

BIRD

Binational Industrial Research & Development Foundation

BIS

Bureau of Industry and Security

BSF

Binational Science Foundation

C
C2

command and control

C&ET

critical and emerging technologies

CBP

U.S. Customs and Border Protection

CBRS

Citizens Broadband Radio Service

CCMD

combatant command

CCP

Chinese Communist Partyp
618

APPENDIX B

p
619

CCW

Convention on Certain Conventional WeaponsCD

cardiovascular disease

CDC

Centers for Disease Control and Prevention

CDO

chief data officer

CFIUS

Committee on Foreign Investment in the United States

CFR

Code of Federal Regulations

CHIPS

Creating Helpful Incentives to Produce Semiconductors

CIACentral Intelligence Agency

CIO

chief information officer

CISA

Cybersecurity and Infrastructure Security Agency

CMI

Component Mission Initiative

CNIPA

China National Intellectual Property Administration

COE

Center of Excellence

CONOPS

concept(s) of operations(s)COTScommercial off-the-shelf

COVID-19

coronavirus disease 2019

CReATE

Coding Repository and Transformation Environment

CRISPR

clustered regularly interspaced short palindromic repeats

CRCL

civil rights and civil libertiesCS

computer science

CSC

U.S. Cyberspace Solarium Commission

CSET Bureau

Bureau of Cyberspace Security and Emerging Technologies

CSIS

Center for Strategic and International Studies

CSTD

Comprehensive Science and Technology Dialogue

CTO

chief technology officer

ACRONYMS FOUND IN THIS REPORT

D
DA

Decision Authority

DAC

Development Assistance Committee

DARPA

Defense Advanced Research Projects Agency

DDI

Bureau for Development, Democracy, and Innovation at USAID

DEXCOM

Deputies Executive Committee

DFC

U.S. International Development Finance Corporation

DFFT

data free flow with trust

DFI

development finance institution

DIA

Defense Intelligence Agency

DIB

Defense Innovation Board

DIU

Defense Innovation Unit

DHS

Department of Homeland Security

D/MR

Deputy Secretary of State for Management and ResourcesDNA

deoxyribonucleic acid

DNI

Director of National Intelligence

DoD

Department of Defense

DoDD

Department of Defense Directive

DoE

Department of Energy

DOI

Department of the Interior

DOJ

Department of Justice

DOT

Department of Transportation

DOT&E

Director, Operational Test and Evaluation

DOTMLPF-P

doctrine, organization, training, materiel, leadership and education, personnel,
facilities, and policy

DPC

Domestic Policy Councilp620

APPENDIX B

DRL

Bureau of Democracy, Human Rights, and Labor

E
EB

Bureau of Economic and Business Affairs

ECRA

Export Control Reform Act of 2018

EDT

emerging and disruptive technology

E.O.

Executive Order

EOP

Executive Office of the President

ERI

Electronics Resurgence Initiative

ESA

European Space Agency

ETC

Emerging Technology Coalition

ETTAC

Emerging Technology Technical Advisory Committee

EU

European Union

EUV

extreme ultraviolet

EXIM

Export-Import Bank of the United States

F

p
621

FAIR

Facebook AI Research

FAR

Federal Acquisition Regulation

FARA

Foreign Agents Registration Act

FBI

Federal Bureau of Investigation

FCC

Federal Communications Commission

FCEN

Financial Crimes Enforcement Network

FDA

U.S. Food and Drug Administration

FFRDC

Federally Funded Research and Development Center

FIRRMA

Foreign Investment Risk Review Modernization Act of 2018

ACRONYMS FOUND IN THIS REPORTFISMAFederal Information Security Modernization Act

FPGA

field-programmable gate array

FSI

Foreign Service Institute

FTQC

fault-tolerant quantum computer

FWCI

field-weighted citation impact

FY

fiscal year

FYDP

Future Years Defense PlanGG20Group of 20

GAN

generative adversarial network

GAO

U.S. Government Accountability OfficeGDPgross domestic product

GEC

Global Engagement Center at Department of State

GGEGroup of Governmental Experts

GIST

Global Innovation through Science and Technology

GPAI

Global Partnership on Artificial Intelligence

GPS

Global Positioning System

GPT-3

Generative Pre-trained Transformer 3

GPUgraphics processing unit

GSA

U.S. General Services Administration

H
HPC

high-performance computing

HHMI

Howard Hughes Medical Institute

HHS

Health and Human Servicesp622

APPENDIX B

HR

human resources

HQE

highly qualified expert

HSI

human-system interactions

HUMINT

human intelligence

I

p
623

I&W

indication(s) and warning(s)IARPAIntelligence Advanced Research Projects Activity

IC

U.S. Intelligence Community

IC ITE

Intelligence Community Information Technology Environment

ICRC

International Committee of the Red Cross

ICT

information and communications technology

IDDI

International Digital Democracy Initiative

IECInternational Electrotechnical Commission

IEEE

Institute of Electrical and Electronic Engineers

IER

International Entrepreneur Rule

IFBHRInternet Freedom and Business & Human Rights Section

IFI

international financial institution

IHL

International Humanitarian Law

IMINT

imagery intelligence

INL

Bureau of International Narcotics and Law Enforcement Affairs

IoT

internet of thingsIPintellectual property

IPEC

U.S. Intellectual Property Enforcement Coordinator

IPA

Intergovernmental Personnel Act

IPHE

International Partnership for Hydrogen and Fuel Cells in the Economy

ACRONYMS FOUND IN THIS REPORT

ISAC

Information Sharing and Analysis Center

ISIS

Islamic State of Iraq and Syria

ISN

Bureau of International Security and Nonproliferation

ISO

International Organization for Standardization

ISR

intelligence, surveillance, and reconnaissance

ISTS

International Science and Technology Strategy

IT

information technology

ITF-CCADInternational Task Force to Counter and Compete Against Disinformation

IT SRMCIT Modernization Senior Risk Management Council

ITU

International Telecommunication Union

ITU-TInternational Telecommunication Union–Telecommunication Standardization Sector

IUSSTF

Indo-U.S. Science and Technology ForumJ
JAIC

Joint Artificial Intelligence Center

JCF

Joint Common Foundation

JCIDS

Joint Capabilities Integration and Development System

JIATF

Joint Interagency Task Force

JROC

Joint Requirements Oversight Council

JSP

Joint Strategic Plan

JWAC

Joint Warfare Analysis CenterK
K-12

kindergarten to 12th gradep624

APPENDIX B

L
LAWS

lethal autonomous weapon systems

LKIE

learning, knowledge, and information exchange

LOAC

Law of Armed ConflictM
M&A

mergers and acquisitions

M&S

modeling and simulation

MAIEI

Montreal AI Ethics Institute

MAIRI

Multilateral AI Research Institute

MASINT

Measurement and signature intelligence

MCC

Millennium Challenge Corporation

MDA

Milestone Decision Authorities

MDAP

Major Defense Acquisition Program

MediFORMedia Forensics

MEMT

Multi-Engine Machine Translation

Mila

Montreal Institute for Learning Algorithms

MIT

Massachusetts Institute of Technology

MITE

Malign Information Threat ExecutiveMLmachine learning

N

p
625

NAIRR

National Artificial Intelligence Research Resource

NASA

National Aeronautics and Space Administration

NATO

North Atlantic Treaty Organization

NBA

National Basketball Association

ACRONYMS FOUND IN THIS REPORTNCEAINational Council for Expanding American Innovation

NCPS

National Cybersecurity Protection System

NCSC

National Counterintelligence and Security Center

NCTC

National Counterterrorism Center

NDAA

National Defense Authorization Act

NDEA

National Defense Education Act

NDS

National Defense Strategy

NEA

Nuclear Energy Agency

NEC

National Economic Council

NIFA

National Institute of Food and Agriculture

NIH

National Institutes of Health

NIJ

National Institute of JusticeNIS

National Intelligence Strategy

NISQ

noisy intermediate-scale quantum

NIST

National Institute of Standards and Technology

NITRD

Networking and Information Technology Research and Development

NLP

natural language processing

NLU

natural language understanding

NOAA

National Oceanic and Atmospheric Administration

NQCO

National Quantum Coordination Office

NQI

National Quantum Initiative

NRDC

National Reserve Digital Corps

NSF

National Science Foundation

NSTC

National Science and Technology Council

NSA

National Security Agencyp626

APPENDIX B

NSC

National Security Council

NSF

National Science Foundation

NSIB

National Security Innovation BaseNSIN

National Security Innovation Network

NSS

National Security Strategy

NTF

National Technology Foundation

NTS

National Technology Strategy

NTIA

National Telecommunications and Information Administration

NVLAP

National Voluntary Laboratory Accreditation Program

O

p
627

ODNI

Office of the Director of National IntelligenceOECD

Organisation for Economic Co-operation and Development

OES

Bureau of Oceans and International Environmental and Scientific Affairs

OISE

Office of International Science and Engineering

OMB

Office of Management and Budget

ONR

Office of Naval Research

OPM

U.S. Office of Personnel Management

ORSA

operational research and systems analysis

OSD

Office of the Secretary of Defense

OSINT

open-source intelligence

OSTP

Office of Science and Technology Policy

OTA

Other Transaction Authority

OUSD (A&S)

Office of the Under Secretary of Defense for Acquisition and Sustainment

OUSD (I&S)

Office of the Under Secretary of Defense for Intelligence and Security

OUSD (R&E)

Office of the Under Secretary of Defense for Research and Engineering

ACRONYMS FOUND IN THIS REPORT

OZOpportunity Zone

P
PaaS

platforms as a service

PAL

Permissive Action Link

PCAST

President’s Council of Advisors on Science and Technology

P/CLR

Privacy, Civil Liberties, and Civil Rights

P/CRCLPrivacy, Civil Rights, and Civil Liberties

PCLOB

Privacy and Civil Liberties Oversight Board

PCT

Patent Cooperation Treaty

PDDNI

Principal Deputy Director of National Security

PEprogram element

PED

processing, exploitation, and dissemination

PGNN

physics-guided neural network

PhD

doctoral graduate

PIA

Privacy Impact Assessment

PII

personally identifiable information

PLA

People’s Liberation ArmyPM

Bureau of Political-Military AffairsPMprogram manager

PoR

Program of Record

PPBE

Planning, Programming, Budget, and Execution

PPML

privacy-preserving machine learningp
628

APPENDIX B

Q
QED-C

Quantum Economic Development Consortium

QIS

Quantum Information Science

QPU

quantum processing unit

R
R&D

research and development

RAI

responsible AI

RDT&E

research, development, test, and evaluation

REN-ISAC

Research and Education Networks Information and Sharing Analysis Center

RL

reinforcement learning

RMF

Risk Management Framework

RPA

robotic process automation

S

p
629

S&E

science and engineering

SBIR

Small Business Innovation Research Program

S/CCI

Office of the Coordinator for Cyber Issues

SDK

software development kit

SDO

standards developing organization

SemaForsemantic forensics

SEP“standard essential” patents

SFS

scholarship for service

SGE

Special Government Employee

SIAC

Strategic Intelligence Analysis Cell

SIGINTsignals intelligence

ACRONYMS FOUND IN THIS REPORT

SMART

Science, Mathematics, and Research for Transformation

SME

semiconductor manufacturing equipment

SMIC

Semiconductor Manufacturing International Corporation

SORN

System of Records Notice

SSD

Strategic Security Dialogue

S&T

science and technology

STAS

Office of the Science and Technology Adviser to the Secretary of State

State/Q

Under Secretary of State for Science, Research and Technology

STEM

science, technology, engineering, and mathematics

STTR

Small Business Technology Transfer Program

SWaP

size, weight, and power

T
TCC

Technology Competitiveness Council

T&E

test(ing) and evaluation

TET

Technology Engagement Team at Department of State

TEVVtest(ing) and evaluation, verification and validation

TRC

Technology Research Center

TSMC

Taiwan Semiconductor Manufacturing Corporation

TTCP

Technical Cooperation ProgramU
UARC

University Affiliated Research Center

U.K.

United Kingdom

UN

United Nations

U.S.

United States

p630

APPENDIX B

p
631

U.S.C.

United States Code

USAF

U.S. Air Force

USAID

U.S. Agency for International Development

USASOC

U.S. Army Special Operations Command

USCIS

U.S. Citizenship and Immigration Services

USD(A&S)

Under Secretary of Defense for Acquisition and Sustainment

USD(R&E)

Under Secretary of Defense for Research and Engineering

USDA

U.S. Department of Agriculture

USDSA

U.S. Digital Service Academy

USERRA

Uniformed Services Employment and Reemployment Rights Act

USISTEF

United States–India Science & Technology Endowment Fund

USPTO

U.S. Patent and Trademark Office

USTDA

U.S. Trade and Development Agency

USTR

Office of the U.S. Trade Representative

ACRONYMS FOUND IN THIS REPORT

V
VA

U.S. Department of Veterans Affairs

VCJCS

Vice Chairman of the Joint Chiefs of Staff

W
WH

White House

WIPO

World Intellectual Property Organization

WLIF

Warfighting Lab Incentive Fund

Numbers
3D

three-dimensional

3SIIF

Three Seas Initiative Investment Fund

5G

fifth-generation standard for broadband cellular networks

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APPENDIX C

Appendix C:
Key Considerations for the
Responsible Development
and Fielding of Artificial
Intelligence (Abridged)
Prefatory Note:
The paradigm and recommended practices described here stem from the Commission’sline of effort dedicated to Ethics and Responsible Artificial Intelligence (AI). The Commission
has recommended that heads of departments and agencies critical to national security (ata minimum, the Department of Defense, Intelligence Community, Department of Homeland
Security, Federal Bureau of Investigation, Department of Energy, Department of State, andDepartment of Health and Human Services) should implement the Key Considerations
as a paradigm for the responsible development and fielding of AI systems. This includes
developing processes and programs aimed at adopting the paradigm’s recommended
practices, monitoring their implementation, and continually refining them as best practices
evolve.
This approach would set the foundation for an intentional, government-wide, coordinatedeffort to incorporate recommended practices into current processes for AI development
and fielding. However, our overarching aim is to allow agencies to continue to havethe flexibility to craft policies and processes according to their specific needs. The
Commission is mindful of the required flexibility that an agency needs when conducting
the risk assessment and management of an AI system, as these tasks will largely dependon the context of the AI system.
This recommendation, along with a set of recommended considerations and practices,
was made originally in July 2020. Here we present a revised and updated version aspart of the Commission’s Final Report. Many of the points made here are also reflected in
Chapter 7 of the report.

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )The content herein is an abridged version of the content included in the extended version,
which will be featured on NSCAI’s website in March 2021 at www.nscai.gov. In the morecomprehensive document, we provide additional details and references for technicalimplementers.
Introduction
The Commission acknowledges the efforts undertaken to date to establish ethics
guidelines for AI systems.1 While some national security agencies have adopted,2 or are in
the process of adopting, AI principles,3 other agencies have not provided such guidance.
In cases where principles are offered, it can be difficult to translate the high-level concepts
into concrete actions. In addition, agencies would benefit from the establishment ofgreater consistency in policies to further the responsible development and fielding of AI
technologies across government.
This Commission has identified five broad categories of challenges and maderecommendations for both responsibly developing and fielding AI systems. These
recommendations include immediate actions and future work the U.S. government should
undertake to help establish best practices to overcome these challenges. Collectively,
they form a paradigm for aligning AI system development and AI system behavior to goals
and values. The first section, Aligning Systems and Uses with American Values and theRule of Law, provides guidance specific to implementing systems that abide by American
values, most of which are shared by democratic nations. The section also covers aligning
the run-time behavior of systems to the related, more technical encodings of objectives,
utilities, and trade-offs. The four following sections (on Engineering Practices, System
Performance, Human-AI Interaction, and Accountability & Governance) serve in supportof core American values and further outline practices needed to develop and field AI
systems that are understandable, reliable, robust, and trustworthy.
Recommended practices span multiple phases of the AI lifecycle and establish a baseline
for the responsible development and fielding of AI technologies. The Commission uses
“development” to refer to “designing, building, and testing during development and prior
to deployment” and “fielding” to refer to “deployment, monitoring, and sustainment.”
The Commission recommends that heads of departments and agencies implement
the Key Considerations as a paradigm for the responsible development and fielding ofAI systems. This includes developing policies and processes to adopt the paradigm’s
recommended practices, monitor their implementation, and continually refine them as bestpractices evolve. These recommended practices should apply both to systems that aredeveloped by departments and agencies as well as to those that are acquired. Systems
acquired (whether commercial off-the-shelf systems or through contractors) should be
subjected to the same rigorous standards and recommended practices in the acquisitions
and acceptance processes. As such, the government organization overseeing the biddingp634

APPENDIX C

process should require that vendors articulate how their practices align with the Key
Considerations’ recommended practices in their proposals, submissions, and bids.
In each of the five sections that follow, we first provide a conceptual overview of the scopeand importance of the topic. We then illustrate examples of a current challenge relevant tonational security departments that underscores the need to adopt recommended practices
in this area. Then, we provide a list of recommended practices that agencies should adopt,
acknowledging research, industry tools, and exemplary models within government that
could support agencies in the adoption of recommended practices. Finally, in areas wherebest practices do not exist or are especially challenging to implement, we note the needfor future work as a priority; this includes, for example, R&D and standards development.
We also identify potential areas in which collaboration with allies and partners would be
beneficial for interoperability and trust and note that the Key Considerations can inform
potential future efforts to discuss military uses of AI with strategic competitors.
I. Aligning Systems and Uses with American Values and the Rule of Law(1) Overview
Our values guide our decisions and our assessment of their outcomes. Our values shape
our policies, our sensitivities, and how we balance trade-offs among competing interests.
America’s values, and commitment to upholding them, are reflected in the U.S. Constitution
and U.S. laws, regulations, policies, and processes.
One of the seven principles we set forth in the Commission’s Interim Report (November2019) is the following:
The American way of AI must reflect American values—including having therule of law at its core. For federal law enforcement agencies conducting national
security investigations in the United States, that means using AI in ways that areconsistent with constitutional principles of due process, individual privacy, equal
protection, and non-discrimination. For American diplomacy, that means standing
firm against uses of AI by authoritarian governments to repress individual freedom
or violate the human rights of their citizens. And for the U.S. military, that means
finding ways for AI to enhance its ability to uphold the laws of war and ensuringthat current frameworks adequately cover AI.
Values established in the U.S. Constitution, and further operationalized in legislation, include
freedoms of speech and assembly as well as the rights to due process, inclusion, fairness,
non-discrimination (including equal protection), and privacy (including protection fromunwarranted government interference in one’s private affairs). These values are codified
in the U.S. Constitution and the U.S. Code.4 International treaties that the United States
has ratified also demonstrate our values by affirming our commitments to human rights
and human dignity.5 Within America’s national security departments, our commitment top635

K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )protecting and upholding privacy and civil liberties is further embedded in the policies andprograms of the Intelligence Community (IC),6 the Department of Homeland Security,7 theDepartment of Defense (DoD),8 and oversight entities (e.g., the Privacy and Civil Liberties
Oversight Board).9 In the military context, core values such as distinction and proportionality
are embodied in the nation’s commitment to, and the DoD’s policies to uphold, the Uniform
Code of Military Justice and the Law of Armed Conflict (LOAC).10
Other values are reflected in treaties, rules, and policies, such as the Convention Against
Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment11; the DoD’s
Rules of Engagement12; and the DoD’s directive concerning autonomy in weapon
systems.13 While not an exhaustive list of U.S. values, the paradigm of considerations andrecommended practices for AI that we introduce resonates with these values, as they havebeen acknowledged as critical by the U.S. government and national security departments
and agencies. Further, many of these values are common to America’s like-minded
partners, who share a commitment to democracy, human dignity, and human rights.
Our values demand that the development and fielding of AI respect these foundational
values and that they enable human empowerment as well as accountability. They
require that the operation of AI systems and components be compliant with our laws andinternational legal commitments and with our departmental policies. In short, American
values must inform the way we develop and field AI systems and the way our AI systems
behave in the world.
(2) Examples of Current Challenges
Machine learning (ML) techniques can assist DoD agencies with large-scale data analyses
to support and enhance decision-making about personnel. As an example, the Proposed
New Disability Construct (PNDC) seeks to leverage data analyses to identify service
members on the verge of ineligibility for deployment due to concerns with their readiness.
Other potential analyses, including factors that lead to success or failure in promotion, cansupport personnel evaluations. Caution and proven practices are needed, however, toavoid pitfalls in fairness and inclusiveness, several of which have been highlighted in highprofile challenges in areas like criminal justice, recruiting and hiring, and face recognition.14
Attention should be paid to challenges with decision support systems like PNDC to avoid
harmful disparate impact.15 Likewise, factors weighed in performance evaluations andpromotions must be carefully considered to avoid inadvertently reinforcing existing biases
through ML-assisted decisions.16

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APPENDIX C(3) Recommendations for Adoption
A. Developing uses and building systems that behave in accordance with American
values and the rule of law. To implement core American values, it is important to:
1. Employ technologies and operational policies that align with privacy preservation,
fairness, inclusion, human rights, and the law of armed conflict (LOAC). Technologies
and policies throughout the AI lifecycle should support achieving these goals. They
should ensure that AI uses and systems are consistent with these values and mitigate
the risk that AI system uses/outcomes will violate these values.
• An explicit analysis of outcomes that would violate these values should be
performed. Policy should prohibit disallowed outcomes that would violate thevalues above. During system development, analysis of system-specific disallowed
outcomes should be performed.17 As the technology advances, applications
evolve, and our understanding of the implications of use grows, these policies
should periodically be refreshed.
B. Representing objectives and trade-offs. Another important practice for aligning AI
systems with values is to consider values as (1) embodied in choices about engineering
trade-offs and (2) explicitly represented in the goals and utility functions of an AI system.18
Recommended practices for representing objectives and trade-offs include the following:
1. Consider and document value considerations in AI systems by specifying how tradeoffs with accuracy are handled. This includes documenting the choices made whenselecting operating thresholds that have implications for performance, such as theratio of true positive and false positive rates or the precision (how many selected items
are relevant?) versus recall (how many relevant items are selected?). For example,
consider a system designed to recommend if a person entering the U.S. should be
pulled aside for more detailed inspection and interview. Precision refers to how manyof the people selected for additional processing are valid security concerns; recall
refers to how many valid security concerns are flagged for added processing. The
trade-off is between allowing a valid security concern to slip past review and detaining
persons who are not a security concern. Setting thresholds to increase precision (i.e.,
reduce the number of persons detained needlessly) will drive down recall (i.e., detain
fewer valid security concerns).
2. Consider and document value considerations in AI systems that rely on representationsof objective or utility functions, especially when assigning weighting that captures theimportance of different goals for the system. As an illustration of multiple goals andvalue weights, consider shopping for a new car. A buyer may identify factors that areimportant in the decision, such as gas mileage, safety, reliability, and performance.
These clearly interact in some cases—for example, gas mileage and performance arelikely in tension, and safety is likely correlated partly with vehicle size, which is likely inp637

K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )tension with gas mileage. When reviewing a set of new cars, the best pick for a buyer
will depend on the priorities placed on these factors.
3. Conduct documentation, reviews, and set limits that reflect disallowed outcomes(through constraints on allowed performance) to ensure compliance with values.
(4) Recommendations for Future Action
Future R&D. R&D is needed to advance capabilities for preserving and ensuring thatdeveloped or acquired AI systems will act in accordance with American values andthe rule of law. For instance, the Commission notes the need for R&D to assure that thepersonal privacy of individuals is protected in the acquisition and use of data for AI systemdevelopment. This includes advancing ethical practices with the use of personal data,
including disclosure and consent about data collection and use models (including usesof data to build base models that are later retrained and fine-tuned for specific tasks), theuse of anonymity techniques and privacy-preserving technologies, and uses of related
technologies such as multiparty computation (to allow collaboration on the pooling of datafrom multiple organizations without sharing data sets). Additionally, we need to understandthe compatibility of data usage policies and privacy-preserving approaches with regulatory
approaches such as the European Union’s General Data Protection Regulation (GDPR).
II. Engineering Practices(1) Overview
The government and its partners (including vendors), should adopt recommended
practices for creating and maintaining trustworthy and robust AI systems that areauditable (able to be interrogated and yield information at each stage of the AI lifecycle todetermine compliance with policy, standards, or regulations19); traceable (to understandthe technology, development processes, and operational methods applicable to AI
capabilities, for example with transparent and auditable methodologies, data sources,and design procedure and documentation20); interpretable (to understand the value andaccuracy of system output21); and reliable (to perform in the intended manner within theintended domain of use22). There are no broadly directed best practices or standards toguide organizations in the building of AI systems that are consistent with designated AI
principles, but potential approaches, minimal standards, and engineering proven practices
are available.23
Additionally, several properties of the engineering methods and models used in ML (e.g.,
data-centric methods) are associated with weaknesses that make the systems brittle andexploitable in specific ways—and vulnerable to failure modalities not seen in traditional
software systems. Such failures can rise inadvertently or as the intended results ofmalicious attacks and manipulation.24 Recent frameworks integrate adversarial attacks25
and unintended faults throughout the lifecycle26 into a single taxonomy that describes bothintentional and unintentional failure modes.27

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APPENDIX C

Intentional failures are the result of malicious actors explicitly attacking some aspect of AI
system behavior. Taxonomies (e.g., from NIST) on malicious attacks explain the rapidly
developing Adversarial Machine Learning (AML) landscape. Attacks span ML training andtesting, and each has associated defenses.28 Categories of intentional failures introduced
by adversaries include training data poisoning attacks (contaminating training data),
model inversion (recovering training data used in the model through careful queries), andML supply chain attacks (compromising the ML model as it is being downloaded for use).29
National security uses of AI will be the subject of sustained adversarial efforts; AI developed
for this community must remain current with a rapidly developing understanding of thenature of vulnerabilities to attacks as these attacks grow in sophistication. Technical andprocess advances that contribute to reducing vulnerability and to detecting and alerting
about attacks must also be monitored routinely.
Unintentional failures can be introduced at any point in the AI development and deployment
lifecycle. In addition to faults that can be inadvertently introduced into any softwaredevelopment effort, distinct additional failure modes can be introduced for ML systems.
Examples of unintentional AI failure modes include reward hacking (when AI systems learn
to achieve a programmed goal in a way that contradicts the programmer’s intent) anddistributional shifts (when a system is tested in one kind of environment but is unable toadapt to changes in other kinds of environments).30 Another area of failure is the inadequate
specification of objectives (as described in Section 1 above on Representing Objectives
and Trade-offs), leading to unexpected and costly behaviors and outcomes.31 As AI
systems that are separately developed and tested are composed and interact with otherAI systems (within one’s own services, forces, and agencies, and between U.S. systems
and those of allies, adversaries, and potential adversaries), additional unintentional failures
can occur.32(2) Examples of Current Challenges
To make high-stakes decisions, and often in safety-critical contexts, the DoD and IC must
be able to depend on the integrity and security of the data used to train some kinds of ML
systems. The challenges of doing so have been echoed by the leadership of the DoD andthe IC,33 including concerns with detecting adversarial attacks such as data poisoning.
(3) Recommendations for Adoption
Critical engineering practices needed to operationalize AI principles (such as “traceable”
and “reliable”34) are described in the non-exhaustive list below. These practices spandevelopment and fielding of AI systems.
1. Refine design and development requirements, informed by the concept of operations
and risk assessment, including characterization of failure modes and associatedimpacts. Conduct systems analysis of operations and identify mission successp639

K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )metrics and potential functions that can be performed by AI technology. Incorporate
early analyses of use cases and scenario development, assess general feasibility
and compliance with disallowed outcomes expressed in policy. Critically assess
reproducibility (how readily research results can be replicated by a third party) andtechnical maturity. This includes broad stakeholder engagement and hazard analysis
with multidisciplinary experts who ask key questions about potential disparate impacts
and document the process undertaken to ensure fairness and the lack of unwanted
bias in the ML application.35 The feasibility of meeting these requirements may triggera review of whether and where it is appropriate to use AI in the system being proposed.
• Risk assessment. Trade-offs and risks, including a system’s potential societal
impact, should be discussed with a diverse, interdisciplinary group. This includes
an analysis of the system’s potential societal impact and of the impacts of thesystem’s failure modes. Risk-assessment questions should be asked about criticalareas relevant to the national security context, including privacy and civil liberties,
LOAC, human rights,36 system security, and the risks of a new technology being
leaked, stolen, or weaponized.37
2. Produce documentation of the AI lifecycle. Whether building and fielding an AI
system or “infusing AI” into a preexisting system, require documentation in certain
areas.38 These include the data used in ML technologies and the origin of the data39;
algorithm(s) used to build models, model characteristics, and intended uses of the AI
capabilities; connections between and dependencies within systems, and associatedpotential complications; the selected testing methodologies, performance indicators,
and results for models used in the AI component; and required maintenance (including
re-testing requirements) and technical refresh (including for when a system is used in adifferent scenario/setting or if the AI system is capable of online learning or adaptation).
3. Leverage infrastructure to support traceability, including auditability and forensics.
Invest resources and build capabilities that support the traceability of AI systems.
Traceability captures key information about the system’s development and deployment
process for relevant personnel to adequately understand the technology.40 Audits
should support analyses of specific actions and characterizations of longer-term
performance and assure that performance on tests of the system and on real-world
workloads meet requirements.
4. For security and robustness, address intentional and unintentional failures.
• Adversarial attacks and use of robust ML methods. Expand notions of adversarial
attacks to include various ML attacks41 (as described above) and seek latest
technologies that demonstrate the ability to detect and notify operators of attacks
and also tolerate attacks (i.e., to enable systems to withstand or to degrade
gracefully when targeted by a deliberate attack).42

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640

APPENDIX C• Follow and incorporate advances in intentional and unintentional ML failures.
Given the rapid evolution of the field of study of intentional and unintentional
ML failures, national security organizations must follow and adapt to the latest
knowledge about failures and proven practices for system monitoring, failure
detection, engineering, and protections during operation. Related efforts and
R&D focus on developing and deploying robust AI methods.43• Adopt a DevSecOps lifecycle for AI systems focused on potential failuremodes. This includes developing and regularly refining threat models to capture
and characterize various attacks, establish a matrixed focus for developing andrefining threat models, and ensuring DevSecOps addresses ML development,
fielding, and when ML systems are under attack.44• Limit consequences of system failure through system architecture. Build an
overall system architecture that monitors component performance and handles
errors when anomalies are detected; build AI components to be self-protecting
and self-checking; and include aggressive stress testing under conditions ofintended use.
5. Conduct red teaming for both intentional and unintentional failure modalities. Bring
together multiple perspectives to rigorously challenge AI systems, exploring the risks,
limitations, and vulnerabilities in the context in which they’ll be deployed (i.e., red
teaming).
• To mitigate intentional failure modes, assume an offensive posture and use
methods to make systems more resistant to adversarial attacks, work withadversarial testing tools, and deploy teams dedicated to trying to break systems
and make them violate rules for appropriate behavior.45• To mitigate unintentional failure modes, test ML systems per a thorough list ofrealistic conditions they are expected to operate in. When selecting third-party
components, consider the impact that a security vulnerability in them could
have on the security of the larger system into which they are integrated. Have an
accurate inventory of third-party components and a plan to respond when newvulnerabilities are discovered.
•
Organizations should consider establishing broader enterprise-widecommunities of AI red teaming capabilities that could be applied to multiple AI
developments (e.g., at a DoD service or IC element level, or higher).

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )(4) Recommendations for Future Action• Documentation strategy. As noted in our First Quarter
Recommendations, a common documentation strategy is needed to ensure sufficient
documentation by all national security departments and agencies.46 In the meantime,
agencies should pilot documentation approaches across the AI lifecycle to help informsuch a strategy.
• Standards. To improve traceability, future work is needed by standard-setting bodies,alongside national security departments/agencies and the broader AI community, todevelop audit trail requirements per mission needs for high-stakes AI systems including
safety-critical applications (e.g., weapon system controls).
• Future R&D. R&D is needed to advance capabilities for cultivating more robust
methods that can overcome adverse conditions; to advance approaches that enableassessment of types and levels of vulnerability and immunity; and to tolerate attacks.
R&D is also needed to advance capabilities to support risk assessment, including
standards, methods, and metrics for evaluating degrees of auditability, traceability,interpretability, explainability, and reliability. For interpretability in particular, R&D isalso needed to improve our understanding of the efficacy of interpretability tools andpossible interfaces.
III. System Performance(1) Overview
Fielding AI systems in a responsible manner includes establishing confidence that thetechnology will perform as intended. An AI system’s performance must be assessed,47
including assessing its capabilities and blind spots with data representative of realworld scenarios or with simulations of realistic contexts,48 and its reliability, robustness(i.e., resilience in real-world settings, including withstanding adversarial attacks on AI
components), and security during development and deployment.49 System performancemust also measure compliance with requirements derived from values such as fairness.
Testing protocols and requirements are essential for measuring and reporting on system
performance. (Here, “testing” broadly refers to what the DoD calls “Test and Evaluation,
Verification and Validation” [TEVV]. This testing includes both what DoD refers to as
Developmental Test and Evaluation and Operational Test and Evaluation.) AI systems
present new challenges to established testing protocols and requirements as they increasein complexity, particularly for operational testing. However, existing methods like highfidelity performance traces and means for sensing shifts (e.g., changes in the statistical
distribution of data in operation versus model training) allow for the continuous monitoringof an AI system’s performance.

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APPENDIX C

When evaluating system performance, it is especially important to take into account holistic,
end-to-end system behavior—the consequence of the interactions and relationships
among system elements rather than the independent behavior of individual elements.
While system engineering and national security communities have focused on system ofsystems engineering for years, specific attention must be paid to undesired interactions
and emergent performance in AI systems. Multiple relatively independent AI systems canbe viewed as distinct agents interacting in the environment of the system of systems,
and some of these agents will be humans in and on the loop. Industry has encounteredand documented problems in building “systems of systems” out of multiple AI systems.50
A related problem is encountered when the performance of one model in a pipelinechanges, degrading the overall pipeline behavior.51 As America’s AI-intensive systems
may increasingly be composed and/or interoperable with allied AI-intensive systems,
these become important topics for coordination with allies.
(2) Examples of Current Challenges
Unexpected interactions and errors commonly occur in integrated simulations andexercises, illustrating the challenges of predicting and managing behaviors of systems
composed of multiple components. Intermittent failures can transpire after composing
different systems; these failures are not necessarily the result of any one component
having errors, but rather are due to the interactions of the composed systems.52(3) Recommendations for Adoption
Critical practices to ensure optimal system performance are described in the following
non-exhaustive list:
A. Model training and model testing procedures should cover key aspects of performanceand appropriate performance metrics.
1. Use regularly updated standards for testing and reporting of system performance.
Standards for metrics and reporting are needed to adequately:a. Achieve consistency across testing and test reporting for critical areas.
b. Test for blindspots.53
c. Test for fairness. When testing for fairness, conduct sustained fairness
assessments throughout development and deployment and document
deliberations made on the appropriate fairness metrics to use. Agencies should
conduct outcome and impact analysis to detect when subtle assumptions inthe system show up as unexpected and undesired outcomes in the operational
environment.54
d. Articulate system performance. Clearly document system performance andcommunicate to the end user the meaning/significance of such performance
metrics.

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )2. Consider and document the representativeness of the data and model for the
specific context at hand. When using classification and prediction technologies,
explicitly consider and document challenges with representativeness of data used inanalyses and the fairness/accuracy of inferences and recommendations made withsystems leveraging that data when applied in different populations/contexts.
3. Evaluate an AI system’s performance relative to current benchmarks where possible.
Such benchmarks should assist in determining if a proposed AI system’s performancemeets or exceeds current best performance.
4. Evaluate aggregate performance of human-machine teams. Consider that thecurrent benchmark might be the current best performance of a human operator or thecomposed performance of the human-machine team. Where humans and machinesinteract, it is important to measure the aggregate performance of the team rather thanthe AI system alone.55
5. Provide sustained attention to reliability and robustness. Employ tools and techniques
to carefully bound assumptions of robustness of the AI component in the larger systemarchitecture. Provide sustained attention to characterizing the actual performance(for normal and boundary conditions) throughout development and deployment.56
For systems of particularly high potential consequences of failure, considerable
architecture and design work will have been put into making the overall system failsafe.
6. For systems of systems, test machine-machine/multi-agent interaction. Individual
AI systems will be combined in various ways in an enterprise to accomplish broader
missions beyond the scope of any single system, which can introduce its own
problems.57 As a priority during testing, challenge (or “stress test”) interfaces andusage patterns with boundary conditions and assumptions about the operational
environment and use.
B. Maintenance and deployment
Given the dynamic nature of AI systems, best practices for maintenance are also criticallyimportant. Recommended practices include:
1. Specify maintenance requirements for datasets as well as for systems, given that
their performance can degrade over time.58
2. Continuously monitor and evaluate AI system performance, including the use ofhigh-fidelity traces to determine continuously if a system is going outside of acceptableparameters.59

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APPENDIX C

3. Conduct iterative model testing and validation. Training and testing that provide
characteristics on capabilities might not transfer or generalize to specific settings ofusage; thus, testing and validation may need to be done recurrently, and at strategicintervention points, but especially for new deployments and classes of tasks.60
4. Monitor and mitigate emergent behavior. There will be instances when systems arecomposed in ways not anticipated by the developers, thus requiring monitoring theactual performance of the composed system and its components.
(4) Recommendations for Future Action• Future R&D. R&D is needed to advance capabilities for TEVV of AI systems tobetter understand how to conduct persistent and iterative TEVV and build checks
and balances into an AI system. Improved methods are needed to explore, predict,and control individual AI system behavior so that when AI systems are composed into
systems of systems, their interaction does not lead to unexpected negative outcomes.
• Metrics. Progress on a common understanding of TEVV concepts and requirements is
critical for progress in widely used metrics for performance. Significant work is needed
to establish what appropriate metrics should be used to assess system performanceacross attributes for responsible AI according to applications/context profiles. (Suchattributes, for example, include fairness, interpretability, reliability, and robustness.)
Future work is needed to develop: (1) definitions, taxonomy, and metrics needed toenable agencies to better assess AI performance and vulnerabilities; and (2) metrics
and benchmarks to assess reliability and intelligibility of produced model explanations.
In the near term, guidance is needed on: (1) standards for testing intentional andunintentional failure modes; (2) exemplar data sets for benchmarking and evaluation,
including robustness testing and red teaming; and (3) defining characteristics of AI
data quality and training environment fidelity (to support adequate performance andgovernance).61• International collaboration and cooperation. Collaboration is needed to align on
how to test and verify AI system reliability and performance, including along sharedvalues (such as fairness and privacy). Such collaboration will be critical among allies
and partners for interoperability and trust. Additionally, these efforts could potentially
include dialogues between the U.S. and strategic competitors on establishing common
standards of AI safety and reliability testing to reduce the chances of inadvertent
escalation.
IV. Human-AI Interaction & Teaming(1) Overview
Responsible AI development and fielding requires striking the right balance of leveraging
human and AI reasoning, recommendation, and decision-making processes. Ultimately,p645

K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )all AI systems will have some degree of human-AI interaction as they all will be developed
to support humans. And some systems will serve as more than just support tools and will
adopt roles of teammates that actively collaborate with humans.
(2) Examples of Current Challenges
There is an opportunity to develop AI systems to complement and augment humanunderstanding, decision-making, and capabilities. Decisions about developing andfielding AI systems for specific domains or scenarios should consider the relative strengthsof AI capabilities and human intellect across the expected range of tasks, considering AI
system maturity or capability and how people and machines might coordinate.
Designs and methods for human-AI interaction can be employed to enhance humanAI teaming.62 Methods in support of effective human-AI interaction can help AI systems
understand when and how to engage humans for assistance, when AI systems shouldtake initiative to assist human operators, and, more generally, how to support the creationof effective human-AI teams. In engaging with end users, it may be important for AI
systems to infer and share with end users well-calibrated levels of confidence about theirinferences, to provide human operators with an ability to weigh the importance of machine
output or pause to consider details behind a recommendation more carefully. Methods,
representations, and machinery can be employed to provide insight about AI inferences,including the use of interpretable machine learning.63
Research directions include developing and fielding machinery aimed at reasoning abouthuman strengths and weaknesses, such as recognizing and responding to the potential
for costly human biases of judgment and decision-making in specific settings.64 Other
work centers on mechanisms to consider the ideal mix of initiatives, including when andhow to rely on human expertise versus on AI inferences.65 As part of effective teaming, AI
systems can be endowed with the ability to detect the focus of attention, workload, andsensitivity to interruption of human operators and consider these inferences in decisions
about when and how to engage with operators.66 Directions of effort include developing
mechanisms for identifying the most relevant information or inferences to provide end
users with different skill levels in different settings.67 Consideration must be given to theprospect of introducing bias, including potential biases that may arise because of theconfiguration and sequencing of rendered data. For example, IC research68 shows that
confirmation bias can be triggered by the order in which information is displayed, and thisorder can consequently impact or sway intel analyst decisions. Careful design and study
can help to identify and mitigate such bias.

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APPENDIX C(3) Recommendations for Adoption
Critical practices to ensure optimal human-AI interaction are described in the nonexhaustive list below. These recommended practices span the entire AI lifecycle.
A. Identification of functions of humans in design, engineering, and fielding of AI.
1. Given AI and human capabilities and complementarities, as well as requirements
for accountability and human judgment, define the tasks of humans and the goals
and mission of the human-machine team across the AI lifecycle. This entails noting
needs for feedback loops, including opportunities for oversight.
2. Define functions and responsibilities of humans during system operation andassign them to specific individuals. Functions and responsibilities will vary foreach domain and project and should be periodically revisited.
B. Explicit support of human-AI interaction and collaboration.
1. Extend human-AI design methodologies and guidelines. AI systems designs
should take into account the defined tasks of humans in human-AI collaborations
in different scenarios; ensure that the mix of human-machine actions in theaggregate is consistent with the intended behavior and accounts for the waysthat human and machine behavior can co-evolve69; and also avoid automation
bias (that places unjustified confidence in the results of the computation) andunjustified reliance on humans in the loop as fail-safe mechanisms. Practices
should allow for auditing of the human-AI pair and designs should be transparent
to allow for an understanding of how the AI is working day-to-day, supported byan audit trail if things go wrong. Based on context and mission need, designs
should ensure usability of AI systems by AI experts, domain experts, and novices,
as appropriate.
2. Employ algorithms and functions in support of interpretability and explanation.
Algorithms and functions that provide individuals with task-relevant knowledge
and understanding should take into account that key factors in an AI system’s
inferences and actions can be understood differently by various audiences(e.g., real-time operators, engineers and data scientists, and oversight officials).
Interpretability and explainability exists in degrees. In this regard, interpretability
intersects with traceability, audit, and documentation practices.
3. Design systems to provide cues to human operator(s) about the level ofconfidence the system has in its results or behaviors. AI system designs should
appropriately convey uncertainty and error bounding. For instance, a user interface
should convey system self-assessment of confidence alerts when the operational
environment is significantly different from the environment the system was trained
for and indicate internal inconsistencies that call for caution.
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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )4. Refine policies for machine-human initiative and handoff. Policies, and aspectsof human-computer interaction, system interface, and operational design, should
define when and how information or tasks should be passed from a machine to ahuman operator and vice versa.
5. Leverage traceability to assist with system development and understanding.
Traceability processes must capture details about human-AI interaction to
retroactively understand where challenges occurred, and why, in order to improvesystems and their use for redress. Infrastructure and instrumentation70 can also
help assess humans, systems, and environments to gauge the impact of AI at alllevels of system maturity and to measure the effectiveness and performance forhybrid human-AI systems in a mission context.
6. Conduct training. Train and educate individuals responsible for AI development
and fielding, including human operators, decision-makers, and procurement
officers.71(4) Recommendations for Future Action• Future R&D. R&D is needed to advance capabilities of AI technologies to perceive
and understand the meaning of human communication, including spoken speech, written
text, and gestures. This research should account for varying languages and cultures, withspecial attention to diversity given that AI often performs worse in cases impacting genderand racial minorities. It is also needed to improve human-machine teaming, including
disciplines and technologies centered on decision sciences, control theory, psychology,
economics (human aspects and incentives), and human factors engineering. R&D for
human-machine teaming should also focus on helping systems understand human blind
spots and biases and optimizing factors such as human attention, human workload, ideal
mixing of human and machine initiative, and passing control between the human andmachine. R&D also is needed to optimize the ability of humans and AI to work together
to undertake complex, evolving tasks in a variety of environments, as well as for diverse
groupings of machines to cooperate with each other, with broader systems, and withhuman counterparts to achieve shared objectives.
• Training. Ongoing work is needed to train the workforce that will interact with, collaborate
with, and be supported by AI systems. In its First Quarter Recommendations, theCommission provided recommendations for such training. Operators should receive training
on the specifics of the system and application, the fundamentals of AI and data science,and refresher trainings (e.g., when systems are deployed in new settings and unfamiliar
scenarios, and when predictive models are revised with new data, as performance may
shift with updates and introduce behaviors unfamiliar to operators).

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APPENDIX C

V. Accountability and Governance(1) Overview
National security departments and agencies must specify who will be held accountablefor both specific system outcomes and general system maintenance and auditing, inwhat way, and for what purpose. Government must address the difficulties in preserving
human accountability, including for end users, developers, testers, and the organizations
employing AI systems. End users and those affected by the actions of an AI system shouldhave the opportunity to appeal an AI system’s determinations. Accountability and appellate
processes must exist for AI decisions, inferences, recommendations, and actions.
(2) Examples of Current Challenges
If a contentious outcome occurs, overseeing entities need the technological capacity tounderstand what in the AI system caused this. For example, if a soldier uses an AI-enabled
weapon and the result violates international law of war standards, an investigating body or
military tribunal should be able to re-create what happened through audit trails and otherdocumentation. Without policies requiring such technology and the enforcement of thosepolicies, proper accountability would be elusive, if not impossible. Moreover, auditing
trails and documentation will prove critical as courts begin to grapple with whether AI
system determinations reach the requisite standards to be admitted as evidence. Building
the traceability infrastructure to permit auditing (as described in Engineering Practices)
will increase the costs of building AI systems and take significant work—a necessary
investment given our commitment to accountability, discoverability, and legal compliance.
(3) Recommendations for Adoption
Critical accountability and governance practices are identified in the non-exhaustive list
below.
1. Appoint full-time responsible AI leads to join senior leadership. Every department
and agency critical to national security and each branch of the armed services, at aminimum, should have a dedicated, full-time responsible AI lead who is part of thesenior leadership team. Such leads should oversee the implementation of the Key
Considerations recommended practices alongside the department or agency’s
respective AI principles.
2. Identify responsible actors. Determine and document the people accountable for a
specific AI system or any given part of the system and the processes involved. This
includes identifying who is responsible for the development or procurement; operation(including the system’s inferences, recommendations, and actions during usage), andmaintenance of an AI system, as well as the authorization of a system and enforcementof policies for use. Determine and document the mechanism/structure for holding suchactors accountable and to whom it should be disclosed for proper oversight.

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )3. Require technology to strengthen accountability processes and goals. Document
the chains of custody and command involved in developing and fielding AI systems toknow who was responsible at which point in time. Improving traceability and auditability
capabilities will allow agencies to better track a system’s performance and outcomes.72
Policy should establish requirements about information that should be captured about
the development process and about system performance and behavior in operation.
4. Adopt policies to strengthen accountability and governance. Identify or, if lacking,establish policies that allow individuals to raise concerns about irresponsible AI
development/fielding (e.g., via an ombudsman). This requires ensuring a governancestructure is in place to address grievances and harms if systems fail, which supports
feedback loops and oversight to ensure that systems operate as they should.
Agencies should institute specific oversight and enforcement practices, including
auditing and reporting requirements; a mechanism that would allow thorough reviewof the most sensitive/high-risk AI systems to ensure auditability and compliance withresponsible use and fielding requirements; an appealable process for those foundat fault for developing or using AI irresponsibly; and grievance processes for thoseaffected by the actions of AI systems. Agencies should leverage best practices fromacademia and industry for conducting internal audits and assessments,73 while also
acknowledging the benefits offered by external audits.74
5. Support external oversight. Remain responsive and facilitate oversight throughdocumentation processes and other policy decisions.75 For instance, supporting
traceability and specifically documentation to audit trails will allow for external
oversight.76 Self-assessment alone might prove to be inadequate in all scenarios.77
Congress can provide a key oversight function throughout the AI lifecycle, asking
critical questions of agency leaders and those responsible for AI systems.
(4) Recommendations for Future Action
Currently no external oversight mechanism exists specific to AI in national security.
Notwithstanding the important work of Inspectors General in conducting internal oversight,
open questions remain as to how to complement current practices and structures.

p
650

APPENDIX C

Appendix C - Endnotes
Examples of efforts to establish ethics guidelines are found within the U.S. government, industry,
and internationally. See, e.g., Draft Memorandum for the Heads of Executive Departments andAgencies: Guidance for Regulation of Artificial Intelligence Applications, Office of Management andBudget (Jan. 1, 2019), https://www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memoon-Regulation-of-AI-1-7-19.pdf; Jessica Fjeld & Adam Nagy, Principled Artificial Intelligence: Mapping
Consensus in Ethical and Rights-Based Approaches to Principles for AI, Berkman Klein Center (Jan.
15, 2020), https://cyber.harvard.edu/publication/2020/principled-ai; OECD Principles on AI, OECD(last visited June 17, 2020), https://www.oecd.org/going-digital/ai/principles/; Ethics Guidelines for
Trustworthy AI, European Commission at 26-31 (April 8, 2019), https://ec.europa.eu/digital-singlemarket/en/news/ethics-guidelines-trustworthy-ai; Assessment List for Trustworthy Artificial Intelligence(ALTAI) for Self-assessment, European Commission (July 17, 2020), https://ec.europa.eu/digitalsingle-market/en/news/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment.

1

2
C. Todd Lopez, DOD Adopts 5 Principles of Artificial Intelligence Ethics, U.S. Department of
Defense (Feb. 5, 2020), https://www.defense.gov/Explore/News/Article/Article/2094085/dod-adopts5principles-of-artificial-intelligence-ethics/ [hereinafter Lopez, DoD Adopts 5 Principles].

See Ben Huebner, Presentation: AI Principles, Intelligence and National Security Alliance 2020
Spring Symposium: Building an AI-Powered IC (March 4, 2020), https://www.insaonline.org/2020spring-symposium-building-an-ai-powered-ic-event-recap/.
3

See, e.g., U.S. Const. amendments I, IV, V, and XIV; Americans with Disabilities Act of 1990, 42
U.S.C. § 12101 et seq.; Title VII of the Consumer Credit Protection Act, 15 U.S.C. §§ 1691-1691f; Title
VII of the Civil Rights Act of 1964, 42 U.S.C. § 2000e et seq.

4

International Covenant on Civil and Political Rights, UN General Assembly, United Nations, Treaty
Series, vol. 999, at 171 (Dec. 16, 1966), https://www.refworld.org/docid/3ae6b3aa0.html. As notedin the Commission’s Interim Report, America and its like-minded partners share a commitment todemocracy, human dignity, and human rights. Interim Report, NSCAI (Nov. 2019), https://www.nscai.
gov/previous-reports/. Many, but not all nations, share commitments to these values. Even whenvalues are shared, however, they can be culturally relative, for instance, across nations, owing tointerpretative nuances.

5

6See, e.g., Daniel Coats, Intelligence Community Directive 107, Office of the Director of National
Intelligence (Feb. 28, 2018), https://fas.org/irp/dni/icd/icd-107.pdf (on protecting civil liberties andprivacy); IC Framework for Protecting Civil Liberties and Privacy and Enhancing Transparency Section
702, Intel.gov (Jan. 2020), https://www.intelligence.gov/index.php/ic-on-the-record/guide-to-posteddocuments#SECTION_702-OVERVIEW (on privacy and civil liberties implication assessments andoversight); Principles of Professional Ethics for the Intelligence Community, Office of the Directorof National Intelligence (last accessed June 17, 2020), https://www.dni.gov/index.php/who-we-are/organizations/clpt/clpt-related-menus/clpt-related-links/ic-principles-of-professional-ethics (ondiversity and inclusion).

See, e.g., Privacy Office, U.S. Department of Homeland Security (last accessed June 3, 2020),
https://www.dhs.gov/privacy-office#; CRCL Compliance Branch, U.S. Department of Homeland
Security (last accessed May 15, 2020), https://www.dhs.gov/compliance-branch.
7

See Samuel Jenkins & Alexander Joel, Balancing Privacy and Security: The Role of Privacy and Civil
Liberties in the Information Sharing Environment, IAPP Conference 2010 (2010), https://dpcld.defense.
gov/Portals/49/Documents/Civil/IAPP.pdf.

89
See Projects, U.S. Privacy and Civil Liberties Oversight Board (last visited June 17, 2020), https://www.pclob.gov/Projects.
10
See Department of Defense Law of War Manual, U.S. Department of Defense (Dec. 2016), https://
dod.defense.gov/Portals/1/Documents/pubs/DoD%20Law%20of%20War%20Manual%20-%20
June%202015%20Updated%20Dec%202016.pdf?ver=2016-12-13-172036-190 [hereinafter DoD Law
of War Manual]; see also AI Principles: Recommendations on the Ethical Use of Artificial Intelligenceby the Department of Defense: Supporting Document, DoD Defense Innovation Board (Oct. 31, 2019),
https://media.defense.gov/2019/Oct/31/2002204459/-1/-1/0/DIB_ AI_Principles_supporting_document.
pdf (“More than 10,000 military and civilian lawyers within DoD advise on legal compliance withregard to the entire range of DoD activities, including the Law of War. Military lawyers train DoD
personnel on Law of War requirements, for example, by providing additional Law of War instruction
prior to a deployment of forces abroad. Lawyers for a Component DoD organization advise on thep651

K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )issuance of plans, policies, regulations, and procedures to ensure consistency with Law of War
requirements. Lawyers review the acquisition or procurement of weapons. Lawyers help administer
programs to report alleged violations of the Law of War through the chain of command and also
advise on investigations into alleged incidents and on accountability actions, such as commanders’decisions to take action under the Uniform Code of Military Justice. Lawyers also advise commanders
on Law of War issues during military operations.”).
11
Convention against Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment,
United Nations General Assembly (Dec. 10, 1984), https://www.ohchr.org/en/professionalinterest/
pages/cat.aspx.
12
See DoD Law of War Manual at 26 (“Rules of Engagement reflect legal, policy, and operational
considerations, and are consistent with the international law obligations of the United States, including
the law of war.”).

See Department of Defense Directive 3000.09 on Autonomy in Weapon Systems, U.S. Departmentof Defense (Nov. 21, 2012), https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodd/300009p.pdf (“Autonomous and semi-autonomous weapon systems shall be designed to allow
commanders and operators to exercise appropriate levels of human judgment over the use of force.”).

13

14See, e.g., Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System,
Partnership on AI, https://www.partnershiponai.org/report-on-machine-learning-in-risk-assessmenttools-in-the-u-s-criminal-justice-system/; Jeffrey Dastin, Amazon Scraps Secret AI Recruiting Tool thatShowed Bias Against Women, Reuters (Oct. 10, 2018), https://www.reuters.com/article/us-amazoncom-jobs-automation-insight/amazonscraps-secret-ai-recruiting-tool-that-showed-bias-againstwomen-idUSKCN1MK08G [hereinafter Dastin, Amazon Scraps Secret AI Recruiting Tool]; Andi Peng
et al., What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring,
Proceedings of the 7th AAAI Conference on Human Computation and Crowdsourcing (Oct. 2019),
https://arxiv.org/pdf/1909.03567.pdf; Patrick Grother, et al., Face Recognition Vendor Test (FRVT) Part
Three: Demographic Effects, National Institute of Standards and Technology (Dec. 2019), https://doi.
org/10.6028/NIST.IR.8280.

PNDC provides predictive analytics to improve military readiness; enable earlier identificationof service members with potential unfitting, disabling, or career-ending conditions; and offer
opportunities for early medical intervention or referral into disability processing. To do so, PNDC
provides recommendations at multiple points in the journey of the non-deployable service member
through the Military Health System to make “better decisions” that improve medical outcomes
and delivery of health services. This is very similar to the OPTUM decision support system thatrecommended which patients should get additional intervention to reduce costs. Analysis showed
millions of U.S. patients were processed by the system, with substantial disparate impact on Blackpatients compared to white patients. Shaping development from the start to reflect bias issues (which
can be subtle) would have produced a more equitable system and avoided scrutiny and suspension ofsystem use when findings were disclosed. Heidi Ledford, Millions of Black People Affected by Racial
Bias in Health Care Algorithms, Nature (Oct. 26, 2019), https://www.nature.com/articles/d41586-01903228-6.

15

16

See e.g., Dastin, Amazon Scraps Secret AI Recruiting Tool.

17
This combined approach of stable policy-level disallowed outcomes and system-specific disallowed
outcomes is consistent with DoD practices for system safety, for example. See Department of Defense
Standard Practice: System Safety, U.S. Department of Defense (May 11, 2012), https://www.dau.edu/
cop/armyesoh/DAU%20Sponsored%20Documents/MIL-STD-882E.pdf. Depending on the context,
mitigating harm per values and disallowed outcomes might entail the use of fail-safe technologies.
See Eric Horvitz, Reflections on Safety and Artificial Intelligence, Exploratory Technical Workshop on
Safety and Control for AI (June 27, 2016), http://erichorvitz.com/OSTP-CMU_ AI_Safety_framing_talk.
pdf. See also Dorsa Sadigh & Ashish Kapoor, Safe Control Under Uncertainty with Probabilistic Signal
Temporal Logic, Proceedings of Robotics: Science and Systems XII (2016), https://www.microsoft.
com/en-us/research/wp-content/uploads/2016/11/RSS2016.pdf.
18
Mohsen Bayati, et al., Data-Driven Decisions for Reducing Readmissions for Heart Failure: General
Methodology and Case Study, PLOS One Medicine (Oct. 8, 2014), https://doi.org/10.1371/journal.
pone.0109264; Eric Horvitz & Adam Seiver, Time-Critical Action: Representations and Application,
Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (Aug. 1997), https://
arxiv.org/pdf/1302.1548.pdf.

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APPENDIX C

Appendix C - Endnotes
See Inioluwa Deborah Raji, et al., Closing the AI Accountability Gap: Defining an End-to-End
Framework for Internal Algorithmic Auditing, ACM FAT (Jan. 3, 2020), https://arxiv.org/abs/2001.00973[hereinafter Raji, Closing the AI Accountability Gap].
19

20

See Lopez, DoD Adopts 5 Principles.

Model Interpretability in Azure Machine Learning, Microsoft (Nov. 16, 2020), https://docs.microsoft.
com/en-us/azure/machine-learning/how-to-machine-learning-interpretability.

21

22

Lopez, DoD Adopts 5 Principles.

Jessica Cussins Newman, Decision Points in AI Governance: Three Case Studies Explore Efforts
to Operationalize AI Principles (May 5, 2020), Berkeley Center for Long-Term Cybersecurity, https://cltc.berkeley.edu/ai-decision-points/; Raji, Closing the AI Accountability Gap; Miles Brundage, etal., Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (April 20,2020), https://arxiv.org/abs/2004.07213 [hereinafter Brundage, Toward Trustworthy AI Development];
Saleema Amershi, et al., Software Engineering for Machine Learning: A Case Study, Microsoft(March 2019), https://www.microsoft.com/en-us/research/uploads/prod/2019/03/amershi-icse-2019_
Software_Engineering_for_Machine_Learning.pdf.
23

Dario Amodei, et al., Concrete Problems in AI Safety, arXiv (July 25, 2016), https://arxiv.org/
abs/1606.06565.

24

Guofu Li, et al., Security Matters: A Survey on Adversarial Machine Learning, arXiv (Oct. 23, 2018),
https://arxiv.org/abs/1810.07339; Elham Tabassi et al., NISTIR 8269: A Taxonomy and Terminologyof Adversarial Machine Learning (Draft), National Institute of Standards and Technology (Oct. 2019),
https://csrc.nist.gov/publications/detail/nistir/8269/draft.
25

26
José Faria, Non-Determinism and Failure Modes in Machine Learning, 2017 IEEE 28th International
Symposium on Software Reliability Engineering Workshops (Oct. 2017), https://ieeexplore.ieee.org/
document/8109300.

Ram Shankar Siva Kumar, et al. Failure Modes in Machine Learning (Nov. 11, 2019), https://docs.
microsoft.com/en-us/security/engineering/failure-modes-in-machine-learning [hereinafter Kumar,
Failure Modes in Machine Learning].

27

See Elham Tabassi et al., NISTIR 8269: A Taxonomy and Terminology of Adversarial Machine
Learning (Draft), National Institute of Standards and Technology (Oct. 2019), https://csrc.nist.gov/
publications/detail/nistir/8269/draft. See also Kumar, Failure Modes in Machine Learning.

28

For 11 categories of attack, and associated overviews, see the Intentionally-Motivated Failures
Summary in Kumar, Failure Modes in Machine Learning.

29

30
For more on reward hacking, see Jack Clark, et al., Faulty Reward Functions in the Wild (Dec. 21,
2016), https://openai.com/blog/faulty-reward-functions/. For more on distributional shifts, see Colin
Smith, et al., Hazard Contribution Modes of Machine Learning Components, AAAI-20 Workshop on
Artificial Intelligence Safety (SafeAI 2020) (Feb. 7, 2020), https://ntrs.nasa.gov/archive/nasa/casi.ntrs.
nasa.gov/20200001851.pdf (Unexpected performance represents emergent runtime output, behavior,or effects at the system level, e.g., through unanticipated feature interaction … that was also notpreviously observed during model validation.).

Thomas Dietterich & Eric Horvitz, Rise of Concerns About AI: Reflections and Directions,
Communications of the ACM at 38-40 (Oct. 2015), http://erichorvitz.com/CACM_Oct_ 2015-VP.pdf.
See also Ziad Obermeyer et al., Dissecting Racial Bias in an Algorithm Used to Manage the Health ofPopulations, Science (Oct. 25, 2019), https://science.sciencemag.org/content/366/6464/447.

31

32

p
653

Kumar, Failure Modes in Machine Learning.

K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )For concerns about generative adversarial networks (GANS) voiced by Gen. Shanahan, JAIC, see
Don Rassler, A View from the CT Foxhole: Lieutenant General John N.T. “Jack” Shanahan, Director,
Joint Artificial Intelligence Center, Department of Defense, Combating Terrorism Center at West
Point (Dec. 2019), https://ctc.usma.edu/view-ct-foxhole-lieutenant-general-john-n-t-jack-shanahandirector-joint-artificial-intelligence-center-department-defense/. Concerns about GANS, information
authenticity, and reliable and understandable systems were voiced by Dean Souleles, IC. See
Afternoon Keynote, Intelligence and National Security Alliance 2020 Spring Symposium: Building an
AI-Powered IC (March 4, 2020), https://www.insaonline.org/2020-spring-symposium-building-an-aipowered-ic-event-recap/.

33

34

See Lopez, DOD Adopts 5 Principles.

There is no single definition of fairness. System developers and organizations fielding applications
must work with stakeholders to define fairness and provide transparency via disclosure of assumed
definitions of fairness. Definitions or assumptions about fairness and metrics for identifying fair
inferences and allocations should be explicitly documented. This should be accompanied by adiscussion of alternate definitions and rationales for the current choice. These elements should be
documented internally as ML components and larger systems are developed. This is especially
important as establishing alignment on the metrics to use for assessing fairness encounters an added
challenge when different cultural and policy norms are involved when collaborating on development
and use with allies.

35

36
For more on the importance of human rights impact assessments of AI systems, see Report of
the Special Rapporteur to the General Assembly on AI and Its Impact on Freedom of Opinion andExpression, UN Human Rights Office of the High Commissioner (Aug. 29, 2018), https://www.ohchr.
org/EN/Issues/FreedomOpinion/Pages/ReportGA73.aspx. For an example of a human rights risk
assessment for AI in categories such as nondiscrimination and equality, political participation,
privacy, and freedom of expression, see Mark Latonero, Governing Artificial Intelligence:
Upholding Human Rights & Dignity, Data Society (Oct. 2018), https://datasociety.net/wp-content/
uploads/2018/10/DataSociety_Governing_ Artificial_Intelligence_Upholding_Human_Rights.pdf.

For exemplary risk assessment questions that IARPA has used, see Richard Danzig, Technology
Roulette: Managing Loss of Control as Many Militaries Pursue Technological Superiority, Center for a
New American Security at 22 (June 28, 2018), https://s3.amazonaws.com/files.cnas.org/documents/
CNASReport-Technology-Roulette-DoSproof2v2.pdf?mtime=20180628072101.

37

Documentation recommendations build off of a legacy of robust documentation requirements. See
Department of Defense Standard Practice: Documentation of Verification, Validation, and Accreditation(VV&A) For Models and Simulations, Department of Defense (Jan. 28, 2008), https://acqnotes.com/
Attachments/MIL-STD-3022%20Documentation%20of%20VV&A%20for%20Modeling%20&%20
Simulation%2028%20Jan%2008.pdf.

38

39
For an industry example, see Timnit Gebru, et al., Datasheets for Datasets, Microsoft (March2018), https://www.microsoft.com/en-us/research/publication/datasheets-for-datasets/. For moreon data, model, and system documentation, see Annotation and Benchmarking on Understanding
and Transparency of Machine Learning Lifecycles (ABOUT ML), an evolving body of work from thePartnership on AI about documentation practices at https://www.partnershiponai.org/about-ml/.
Documenting caveats of re-use for both data sets and models is critical to avoid “off-label” useharms, as one senior official notes. David Thornton, Intelligence Community Laying Foundation
for AI Data Analysis, Federal News Network (Nov. 1, 2019), https://federalnewsnetwork.com/
allnews/2019/11/intelligence-community-laying-the-foundation-for-ai-data-analysis/.

Jonathan Mace, et al., Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems,
Communications of the ACM, Vol. 63 No. 3, at 94-102 (March 2020), https://m-cacm.acm.org/
magazines/2020/3/243034-pivot-tracing/fulltext [hereinafter Mace, Pivot Tracing].

40

41
Aleksander Madry, et al., Towards Deep Learning Models Resistant to Adversarial Attacks, MIT(Sept. 4, 2019), https://arxiv.org/abs/1706.06083 [hereinafter Madry, Towards Deep Learning Models
Resistant to Adversarial Attacks].

See e.g., id.; Thomas Dietterich, Steps Toward Robust Artificial Intelligence, Association for the
Advancement of Artificial Intelligence (Fall 2017), https://www.aaai.org/ojs/index.php/aimagazine/
article/view/2756/2644; Eric Horvitz, Reflections on Safety and Artificial Intelligence (June 27, 2016),
http://erichorvitz.com/OSTP-CMU_ AI_Safety_framing_talk.pdf.

42

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APPENDIX C

Appendix C - Endnotes
On adversarial attacks on ML, see Kevin Eykholt, et al., Robust Physical-World Attacks on Deep
Learning Visual Classification, IEEE Conference on Computer Vision and Pattern Recognition at
1625-1634 (June 18-23, 2018), https://ieeexplore.ieee.org/document/8578273. On directions withrobustness, see Madry, Towards Deep Learning Models Resistant to Adversarial Attacks. For a moreexhaustive list of sources see Key Considerations for Responsible Development & Fielding of Artificial
Intelligence: Extended Version, NSCAI (2021) (on file with the Commission).
43

Ram Shankar Siva Kumar, et al., Adversarial Machine Learning—Industry Perspectives, 2020 IEEE
Symposium on Security and Privacy (SP) Deep Learning and Security Workshop (May 21, 2020),
https://arxiv.org/abs/2002.05646.

44

Dou Goodman, et al., Advbox: A Toolbox to Generate Adversarial Examples That Fool Neural
Networks (Aug. 26, 2020), https://arxiv.org/abs/2001.05574.

45

46
See First Quarter Recommendations, NSCAI (March 2020), https://www.nscai.gov/previous-reports/.
Ongoing efforts to share best practices for documentation among government agencies through
GSA’s AI Community of Practice further indicate the ongoing need and desire for common guidance.

Ben Shneiderman, Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy, International
Journal of Human-Computer Interaction 2020 at 495-504 (March 23, 2020), https://doi.org/10.1080/10
447318.2020.1741118 [hereinafter Shneiderman, Human-Centered Artificial Intelligence: Reliable, Safe
& Trustworthy].
47

However, test protocols must acknowledge that test sets may not be fully representative of realworld usage.

48

Brundage, Toward Trustworthy AI Development; Ece Kamar, et al., Combining Human andMachine Intelligence in Large-Scale Crowdsourcing, Proceedings of the 11th International
Conference on Autonomous Agents and Multiagent Systems (June 2012), https://dl.acm.org/
doi/10.5555/2343576.2343643 [hereinafter Kamar, Combining Human and Machine Intelligence inLarge-Scale Crowdsourcing].

49

50
One example is “Hidden Feedback Loops,” where systems that learn from external-world behavior
may also shape the behavior they are monitoring. See D. Sculley, et al., Machine Learning: The HighInterest Credit Card of Technical Debt, Google (2014), https://research.google/pubs/pub43146/.

Megha Srivastava, et al., An Empirical Analysis of Backward Compatibility in Machine Learning
Systems, KDD’20 (Aug. 11, 2020), https://arxiv.org/abs/2008.04572 [hereinafter Srivastava, An
Empirical Analysis of Backward Compatibility in Machine Learning Systems].
51

David Sculley, et al., Hidden Technical Debt in Machine Learning Systems, Proceedings of the 28th
International Conference on Neural Information Processing Systems (Dec. 2015), https://dl.acm.org/
doi/10.5555/2969442.2969519.

52

53
Ramya Ramakrishnan, et al., Blind Spot Detection for Safe Sim-to-Real Transfer, Journal of Artificial
Intelligence Research 67 at 191-234 (Feb. 4, 2020), https://www.jair.org/index.php/jair/article/
view/11436.

See Microsoft’s AI Fairness checklist as an example of an industry tool to support fairness
assessments; Michael A. Madaio, et al., Co-Designing Checklists to Understand Organizational
Challenges and Opportunities around Fairness in AI, CHI 2020 (April 25-30, 2020), http://www.
jennwv.com/papers/checklists.pdf [hereinafter Madaio, Co-Designing Checklists to Understand
Organizational Challenges and Opportunities around Fairness in AI].

54

55

Kamar, Combining Human and Machine Intelligence in Large-Scale Crowdsourcing.

56

See Shneiderman, Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy.

57
Cynthia Dwork, et al., Individual Fairness in Pipelines, arXiv (April 12, 2020), https://arxiv.org/
abs/2004.05167; Srivastava, An Empirical Analysis of Backward Compatibility in Machine Learning
Systems.

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )Artificial Intelligence (AI) Playbook for the U.S. Federal Government, Artificial Intelligence Working
Group, ACT-IAC Emerging Technology Community of Interest (Jan. 22, 2020), https://www.actiac.org/
act-iac-white-paper-artificial-intelligence-playbook.

58

Ori Cohen, Monitor! Stop Being A Blind Data-Scientist, Towards Data Science (Oct. 8, 2019),
https://towardsdatascience.com/monitor-stop-being-a-blind-data-scientist-ac915286075f; Mace, Pivot
Tracing at 94-102.

59

Eric Breck, et al., The ML Test Score: A Rubric for ML Production Readiness and Technical Debt
Reduction, 2017 IEEE International Conference on Big Data (Dec. 11-14, 2017), https://ieeexplore.
ieee.org/stamp/stamp.jsp?arnumber=8258038&tag=1.

60

61
The 2021 NDAA expansion of the National Institute of Standards & Technology (NIST) mission
authorizes the standards body to provide such guidance: “National Institute of Standards
and Technology Activities (Title LIII, Sec. 5301)—expands NIST mission to include advancing
collaborative frameworks, standards, guidelines for AI, supporting the development of a riskmitigation framework for AI systems, and supporting the development of technical standards andguidelines to promote trustworthy AI systems.” Pub. L. 116-283, William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021, 134 Stat. 3388 (2021).
62
Saleema Amershi, et al., Guidelines for Human-AI Interaction, CHI ’19: Proceedings of the
CHI Conference on Human Factors in Computing Systems (May 2019), https://dl.acm.org/
doi/10.1145/3290605.3300233.
63
Rich Caruana, et al., Intelligible Models for HealthCare: Predicting Pneumonia Risk
and Hospital 30-day Readmission, Proceedings of the 21th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining (Aug. 10-13, 2015), https://www.
semanticscholar.org/paper/Intelligible-Models-for-HealthCare%3APredicting-Risk-Caruana-Lou/
cb030975a3dbcdf52a01cbd1c140711332313e13.
64Eric Horvitz, Reflections on Challenges and Promises of Mixed-Initiative Interaction, AI Magazine(Summer 2007), http://erichorvitz.com/mixed_initiative_reflections.pdf.

Eric Horvitz, Principles of Mixed-Initiative User Interfaces, Proceedings of CHI ’99 ACM
SIGCHI Conference on Human Factors in Computing Systems (May 1999), https://dl.acm.org/
doi/10.1145/302979.303030; Kamar, Combining Human and Machine Intelligence in Large-Scale
Crowdsourcing.

65

66
Eric Horvitz, et al., Models of Attention in Computing and Communications: From Principles
to Applications, Communications of the ACM at 52-59 (March 2003), https://cacm.acm.org/
magazines/2003/3/6879-models-of-attention-in-computingand-communication/fulltext.

Eric Horvitz & Matthew Barry, Display of Information for Time-Critical Decision Making, Proceedingsof the Eleventh Conference on Uncertainty in Artificial Intelligence (Aug. 1995), https://arxiv.org/
pdf/1302.4959.pdf.

67

68
There has been considerable research in the IC on the challenges of confirmation bias for analysts.
Some experiments demonstrated a strong effect that the sequence in which information is presented
alone can shape analyst interpretations and hypotheses. Brant Cheikes, et al., Confirmation Bias inComplex Analyses, MITRE (Oct. 2004), https://www.mitre.org/sites/default/files/pdf/04_0985.pdf.
This highlights the care that is required when designing the human-machine teaming when complex,
critical, and potentially ambiguous information is presented to analysts and decision-makers.
69
Shneiderman, Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy at 495-504. An
example of co-evolution of machine and human behavior is in ML spam filters. As human spammers
determine what characteristics are getting email flagged as spam, they change how they generatespam, which requires the spam-detection models to evolve in a constant “arms race.”
70
Infrastructure includes tools (hardware and software) in the test environment that support monitoring
system performance (such as the timing of exchanges among systems or the ability to generate testdata). Instrumentation refers to the presence of monitoring and additional interfaces to provide insight
into a specific system under test.

Jamie Berryhill, et al., Hello, World: Artificial Intelligence and Its Use in the Public Sector, OECD
Working Papers on Public Governance (Nov. 21, 2019), https://doi.org/10.1787/726fd39d-en.

71

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656

APPENDIX C

Appendix C - Endnotes
72

See Raji, Closing the AI Accountability Gap.

See Id. (“In this paper, we present internal algorithmic audits as a mechanism to check thatthe engineering processes involved in AI system creation and deployment meet declared ethicalexpectations and standards, such as organizational AI principles”); see also Madaio, Co-Designing
Checklists to Understand Organizational Challenges and Opportunities Around Fairness in AI.

73

For more on the benefits of external audits, see Brundage, Toward Trustworthy AI Development.
For an agency example, see Aaron Boyd, CBP Is Upgrading to a New Facial Recognition Algorithm
in March, Nextgov.com (Feb. 7, 2020), https://www.nextgov.com/emerging-tech/2020/02/cbpupgrading-new-facialrecognition-algorithm-march/162959/ (highlighting a NIST algorithmic
assessment on behalf of U.S. Customs and Border Protection).

74

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Maranke Wieringa, What to Account for When Accounting for Algorithms, Proceedings of the 2020
ACM FAT Conference (Jan. 2020), https://doi.org/10.1145/3351095.3372833.
76

Raji, Closing the AI Accountability Gap.

77

Brundage, Toward Trustworthy AI Development.

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APPENDIX C

Technical Glossary to the Key Considerations Appendix
This glossary provides a working set of definitions specific to the NSCAI Key Considerations.
The Commission acknowledges that the definitions of the terms below may diverge fromother scholarly or government definitions and were developed to be accessible to a broad
audience.
AI Component: A software object that uses AI, meant to interact with other components,
encapsulating certain functionality or a set of functionalities. An AI component has a clearly
defined interface and conforms to a prescribed behavior common to all components within
an architecture.1
AI Lifecycle: The steps for managing the lifespan of an AI system: 1) Specify the system’s
objective. 2) Build model. 3) Test the AI system. 4) Deploy and maintain the AI system. 5)
Engage in a feedback loop with continuous training and updates.2
AI System: A system designed or adapted to interact with an anticipated operational
environment to achieve one or more intended purposes while complying with applicable
constraints and that uses AI to provide a substantial part of its capabilities.3
Artificial Intelligence (AI): The ability of a computer system to solve problems and to performtasks that have traditionally required human intelligence to solve.
Auditability: A characteristic of an AI system in which its software and documentation
can be interrogated and yield information at each stage of the AI lifecycle to determine
compliance with policy, standards, or regulations.
DevSecOps: Enhanced engineering practices that improve the lead time and frequency
of delivery outcomes, promoting a more cohesive collaboration between development,
security, and operations teams as they work toward continuous integration and delivery.
Differential Privacy: A criterion for a strong, mathematical definition of privacy in the context
of statistical and ML analysis used to enable the collection, analysis, and sharing of abroad range of statistical estimates, such as averages, contingency tables, and synthetic
data, based on personal data while protecting the privacy of the individuals in the data.4
False Negative: An example in which the predictive model mistakenly classifies an item asin the negative class. For example, a false negative describes the situation in which a junkemail model specifies that a particular email message is not spam (the negative class),
when the email message actually is spam, leading to frustration of the junk message
appearing in an end user’s inbox.5 In a higher-stakes example, a false negative captures
the case in which a medical diagnostic model misses identifying a disease that is present
in a patient.

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )False Positive: An example in which the model mistakenly classifies an item as in thepositive class. For example, the model inferred that a particular email message was spam(the positive class), but that email message was actually not spam, leading to delays inan end user reading a potentially important message.6 In a higher-stakes situation, a false
positive describes the situation in which a disease is diagnosed as present when the
disease is not present, potentially leading to unnecessary and costly treatments.
High-Fidelity Performance Traces: A commonly used technique useful in debugging
and performance analysis. Concretely, trace recording implies detection and storage of
relevant events during run-time, for later off-line analysis. High fidelity traces refers to theamount of fine-grained detail captured in the traces.7
Human Factors Engineering: The discipline that takes into account human strengths andlimitations in the design of interactive systems that involve people, tools and technology,
and work environments to ensure safety, effectiveness, and ease of use.8
Human in the Loop: The term describes a system architecture in which active humanjudgment and engagement are part of the operation of a system, and a human is an
integral part of the system behavior. An example is the human operator of a remotely
piloted vehicle or a decision support system that makes recommendations for a human todecide on.
Human on the Loop: This term describes a system architecture in which a human has asupervisory role in the operation of the system but is not an integral part of the systembehavior. An example is an operator monitoring a fleet of warehouse robots—they operateautonomously but can be shut down if the operator determines something is wrong.
Machine Learning (ML): The study or the application of computer algorithms that improveautomatically through experience.9 Machine learning algorithms build a model based on
training data in order to perform a specific task, like aiding in prediction or decision-making
processes, without necessarily being explicitly programmed to do so.
Model Testing: Testing assesses the performance of a trained model against new,previously unseen inputs, to demonstrate that the model generalizes to produce accurate
results beyond just the training data.10
Model Training: Training a model simply means learning (determining) good values for allof the internal parameters that determine the model’s performance. In supervised learning,for example, a machine learning model is trained by examining many labeled examples
and attempting to find a model that minimizes the discrepancies between the real (labelled)
values and the values produced by the model.11

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APPENDIX C

Technical Glossary to the Key Considerations Appendix
Multi-Party Federated Learning: A machine learning architecture in which many clients(e.g., mobile devices or whole organizations) collaboratively train a model under theorchestration of a central server (e.g., a service provider) while keeping the training data
decentralized. It can mitigate many of the systemic privacy risks and costs resulting fromtraditional, centralized machine learning and data science approaches.12 However, it does
introduce new attack vectors that must be addressed.13
Precision: A metric for classification models. Precision identifies the frequency with whicha model was correct when classifying the positive class. It answers the question “Howmany selected positive items are true positive?” For example, the percentage of messages
flagged as spam that are spam.14
Privacy-Preserving AI: Techniques for protecting the privacy of people associated with thetraining data from adversarial attacks. These techniques include federated learning anddifferential privacy.15
Recall: A metric for classification models. Recall identifies the frequency with which amodel correctly classifies the true positive items. It answers the question “How many true
positive items were correctly classified?” For example, the percentage of spam messages
that were flagged as spam.16
Reliable AI: An AI system that performs in its intended manner within the intended domain
of use.
Robust AI: An AI system that is resilient in real-world settings, such as an object-recognition
application that is robust to significant changes in lighting. The phrase also refers toresilience when it comes to adversarial attacks on AI components.
Run-Time Behavior: The behavior of a program while it is executing (i.e., running on one ormore processors).
Trustworthy AI: Trustworthy AI has three components: (1) it should be lawful, ensuring
compliance with all applicable laws and regulations; (2) it should be ethical, demonstrating
respect for, and ensuring adherence to, ethical principles and values; and (3) it should berobust, both from a technical and social perspective, because, even with good intentions,
AI systems can cause unintentional harm.17

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K E Y C O N S I D E R AT I O N S F O R T H E R E S P O N S I B L E D E V E L O P M E N T A N D F I E L D I N G O F A R T I F I C I A L I N T E L L I G E N C E ( A B R I D G E D )Technical Glossary to the Key Considerations Appendix - Endnotes
See NIST, NISTIR 7298 Rev. 3, Glossary of Key Information Security Terms (July 2019), https://csrc.
nist.gov/glossary/term/component.

1

Note that for data-driven AI systems step 2 is expanded and replaced with 2.a) Acquire data to meetthe objective, and 2.b) Train the AI system on the data; and these two steps are usually repeated,with data acquisition and training continuing until desired performance objectives are attained. For
further discussion on the ML lifecycle, see Saleema Amershi, et al., Software Engineering for Machine
Learning: A Case Study, IEEE Computer Society (May 2019), https://www.microsoft.com/en-us/
research/publication/software-engineering-for-machine-learning-a-case-study/.

2

3
See Hilary Sillitto, et al., Systems Engineering and System Definitions, International Council on
Systems Engineering, (Jan. 8, 2019), https://www.incose.org/docs/default-source/default-documentlibrary/final_-se-definition.pdf.

Kobbi Nissim, et al., Differential Privacy: A Primer for a Non-technical Audience, Working Groupof the Privacy Tools for Sharing Research Data Project, Harvard University, (Feb. 14, 2018), https://
privacytools.seas.harvard.edu/files/privacytools/files/pedagogical-document-dp_new.pdf.

4

See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2020), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.

5

6

Id.

See Johan Kraft, et al., Trace Recording for Embedded Systems: Lessons Learned
from Five Industrial Projects, Runtime Verification at 315-329, https://link.springer.com/
chapter/10.1007%2F978-3-642-16612-9_ 24.

78
See Human Factors Engineering, U.S. Department of Health and Human Services: Agency for
Healthcare Research and Quality (Sept. 2019), https://psnet.ahrq.gov/primer/human-factorsengineering.
9

Thomas M. Mitchell, Machine Learning, McGraw-Hill (1997).

10
See Rob Ashmore, et al., Assuring the Machine Learning Lifecycle: Desiderata, Methods, andChallenges, arXiv at 4 (May 2019), https://arxiv.org/abs/1905.04223.

See Descending into ML: Training and Loss, Google (last accessed Feb. 15, 2021), https://developers.google.com/machine-learning/crash-course/descending-into-ml/training-and-loss.
11

12Peter Kairouz, et al., Advances and Open Problems in Federated Learning, arXiv (Dec. 10, 2019),
https://arxiv.org/pdf/1912.04977.pdf.

See Vale Tolpegin, et al., Data Poisoning Attacks Against Federated Learning Systems, ArXiv (Aug.
11, 2020), https://arxiv.org/abs/2007.08432; Arjun Nitin Bhagoji, et al., Analyzing Federated Learning
Through an Adversarial Lens, arXiv (Nov. 25, 2019), https://arxiv.org/abs/1811.12470.

13

14
See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2020), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.

For a discussion on how privacy-preserving machine learning works, see Roxanne Heston & Helon
Toner, Have Your Data and Use It Too: A Federal Initiative for Protecting Privacy While Advancing AI,
Day One Project (Jan. 23, 2020), https://www.dayoneproject.org/post/have-your-data-and-use-ittoo-a-federal-initiative-for-protecting-privacy-while-advancing-ai; see also Georgios Kaissis, et al.,Secure, Privacy-Preserving and Federated Machine Learning in Medical Imaging, Nature MachineIntelligence at 305-311 (June 8, 2020), https://doi.org/10.1038/s42256-020-0186-1.

15

See Frank Liang, Evaluating the Performance of Machine Learning Models, Towards Data Science(April 18, 2020), https://towardsdatascience.com/classifying-model-outcomes-true-false-positivesnegatives-177c1e702810.

16

See Ethics Guidelines for Trustworthy AI, European Commission: High-Level Expert Group on
Artificial Intelligence at 5 (April 8, 2019), https://ec.europa.eu/futurium/en/ai-alliance-consultation.

17

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APPENDIX D

Appendix D:
Draft Legislative Language
The following legislative text represents the Commission staff’s best efforts to capture theCommission’s final recommendations in legislative form. The Commission defers to theHouse and Senate members, staff, and legislative counsels as to appropriate drafting.
CHAPTER 1: EMERGING THREATS IN THE AI ERA
Blueprint for Action
Combatting Malign Information Operations Enabled by AI
Recommendation: A National Strategy for the Global Information Domain.
Congress should direct the Executive Branch to transmit a National Strategy for the
Global Information Domain that categorizes the global information domain as an arena ofcompetition vital to the national security of the United States.
SEC. ___.—NATIONAL STRATEGY FOR THE GLOBAL INFORMATION DOMAIN.—(a) IN GENERAL.—Not later than 270 days after the date of the enactment of thisAct, the President shall transmit to Congress a National Strategy for the Global Information
Domain that addresses the global information domain as an arena of competition vital to
the national security of the United States.
(b) ISSUES ADDRESSED.—The National Strategy for the Global Information
Domain required by subsection (a) shall, at a minimum:(1) Prioritize the global information domain as an arena for international
competition;(2) Detail how adversarial state and non-state actors are attempting todefine and control the global information domain in order to shape global opinion
and achieve strategic advantage;(3) Account for the critical role of artificial intelligence-enabled maligninformation in the efforts of adversarial state and non-state actors to achieve thesegoals;(4) Identify and prioritize actions to defend, counter, and compete against
malign information operations as a national security threat;p663

D R A F T L E G I S L AT I V E L A N G U A G E(5) As necessary, update critical infrastructure designations and require
relevant departments and agencies to update sector-specific plans to reflect
emerging technologies; and(6) Establish organizational structures for U.S. national security agencies
to counter and compete against the threat.
CHAPTER 2: FOUNDATIONS OF FUTURE DEFENSE
Blueprint for Action
Recommendation: Drive Change through Top-Down Leadership.
In the National Defense Authorization Act (NDAA) for Fiscal Year (FY) 2022, establish a
Steering Committee on Emerging Technology and National Security Threats and designatethat it be tri-chaired by the Deputy Secretary of Defense, the Vice Chairman of the Joint
Chiefs of Staff, and the Principal Deputy Director of National Intelligence.
SEC. ___.—ROLE OF INTELLIGENCE COMMUNITY IN STEERING COMMITTEE ON
EMERGING TECHNOLOGY.—
Section 236 of the William M. (Mac) Thornberry National Defense Authorization Act
for Fiscal Year 2021, is amended—(1) in subsection (b), by—(A) redesignating paragraph (8) as paragraph (9); and(9):(B) inserting the following new paragraph before redesignated paragraph“(8) One or more representatives of the Intelligence Community, to include
the Principal Deputy Director of National Intelligence.”(2) by redesignating paragraph (c) as paragraph (d); and inserting the following
new paragraph before redesignated paragraph (d):“(c) LEADERSHIP.—The Steering Committee shall be chaired by the Deputy
Secretary of Defense, the Vice Chairman of the Joint Chiefs of Staff, and the Principal
Deputy Director of National Intelligence.”
The Steering Committee on Emerging Technology recommendation is also featured in
Chapters 3 and 5.
Recommendation: Build the Technical Backbone.
Prioritize funding for the Department’s digital ecosystem and associated activities. The
Armed Services Committees should use the FY 2022 NDAA to direct the Department ofp664

APPENDIX D

Defense to develop a resourcing plan for the digital ecosystem that establishes, sustains,and incentivizes use of its various components as enterprise-wide, enduring resources. The
Committees should also authorize the obligation of funds to begin work on the ecosystem.
SEC. ___.—RESOURCING PLAN FOR DIGITAL ECOSYSTEM.—(a) IN GENERAL.—Within one year after the date of the enactment of this Act, theSecretary of Defense shall develop a plan for the development of a modern digital ecosystemthat embraces state of the art tools and modern processes to enable development, testing,fielding, and continuous update of artificial intelligence-powered applications at speed
and scale from headquarters to the tactical edge.
(b) CONTENTS OF PLAN.—At a minimum, the plan required by subsection (a)shall include—(1) an open architecture and an evolving reference design and guidance
for needed technical investments in the proposed ecosystem that address issues
including common interfaces, authentication, applications, platforms, software,
hardware, and data infrastructure; and(2) a governance structure, together with associated policies andguidance, to drive the implementation of the reference throughout the Department
on a federated basis.
Recommendation: Train and Educate Warfighters.
Component 1: Integrate Digital Skill Sets and Computational Thinking into Military Junior
Leader Education.
Require the military services to integrate digital skills and computational thinking into precommissioning and entry-level training.
SEC. ___.—INTEGRATING DIGITAL SKILL SETS AND COMPUTATIONAL THINKING
INTO MILITARY JUNIOR LEADER EDUCATION.—Not later than 270 days after the dateof the enactment of this Act, the Chief of Staff of the Army, the Chief of Naval Operations,the Chief of Staff of the Air Force, and the Commandant of the Marine Corps (collectively,
the Service Chiefs) shall expand the curriculum for military junior leader education toincorporate appropriate training material related to problem definition and curation,a conceptual understanding of the artificial intelligence lifecycle, data collection andmanagement, probabilistic reasoning and data visualization, and data-informed decisionmaking. Whenever possible, the new training and education should include the use ofexisting artificial intelligence-enabled systems and tools.
Component 2: Integrate Emerging and Disruptive Technologies into Service-level
Professional Military Education.
Require the military services to integrate emerging and disruptive technologies into servicelevel Professional Military Education.
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D R A F T L E G I S L AT I V E L A N G U A G E

SEC. ___.—INTEGRATION OF MATERIAL ON EMERGING TECHNOLOGIES INTO
PROFESSIONAL MILITARY EDUCATION.—Not later than one year after the date of theenactment of this Act, the Secretary of Defense, in consultation with the Joint Chiefs ofStaff, shall ensure that the curriculum for professional military education is revised in eachof the military services to incorporate periodic courses on militarily significant emerging
technologies that increasingly build the knowledge base, vocabulary, and skills necessary
to intelligently analyze and utilize emerging technologies in the tactical, operational, andstrategic levels of warfighting and warfighting support.
SEC. ___.—SHORT COURSE ON EMERGING TECHNOLOGIES FOR SENIOR
CIVILIAN AND MILITARY LEADERS.—(a) IN GENERAL.—Not later than one year after the date of the enactment of thisAct, the Secretary of Defense shall establish a short course on emerging technologies
for general and flag officers and senior executive-level civilian leaders. The short course
shall be taught on an iterative, two-year cycle and shall address the most recent, most
relevant technologies and how these technologies may be applied to military and businessoutcomes in the Department of Defense.
(b) THROUGHPUT OBJECTIVES.—In assessing participation in the short course
authorized by subsection (a), the Secretary of Defense shall ensure that:(1) In the first year that the course is offered, no fewer than twenty percent
of general flag officers and senior executive-level civilian leaders are certified ashaving passed the short course required by subsection (a); and(2) In each subsequent year, an additional ten percent of general flag
officers and senior executive-level civilian leaders are certified as having passed
such course, until such time as eighty percent of such officers and leaders are so
certified.
Component 3: Create Emerging and Disruptive Technology Coded Billets in the Departmentof Defense.
Require the Department of Defense to create emerging and disruptive technology criticalbillets that must be filled by emerging technology certified leaders.
SEC. ___.—EMERGING TECHNOLOGY-CODED BILLETS WITHIN THE DEPARTMENT
OF DEFENSE.—(a) IN GENERAL.—Not later than one year after the date of the enactment of thisAct, the Secretary of Defense shall ensure that the military services—(1) code appropriate billets to be filled by emerging technology-qualified
officers; andp
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APPENDIX D(2) develop a process for officers to become emerging technologyqualified.
(b) APPROPRIATE POSITIONS.—Emerging technology-coded positions may
include, as appropriate—(1) positions responsible for assisting with acquisition of emerging
technologies;(2) positions responsible for helping integrate technology into field units;(3) positions responsible for developing organizational and operational
concepts;(4) positions responsible for developing training and education plans; and(5) leadership positions at the operational and tactical levels within the
military services.
(c) QUALIFICATION PROCESS.—The process for qualifying officers for emerging
technology-coded billets shall be modeled on a streamlined version of the joint qualification
process and may include credit for serving in emerging technology focused fellowships,
emerging technology focused talent exchanges, emerging technology focused positions
within government, and educational courses focused on emerging technologies.
Recommendation: Accelerate Adoption of Existing Digital Technologies.
Component 3: Expand Use of Specialized Acquisition Pathways and Contracting
Approaches.
Authorize the use of a rapid contracting mechanism for the software acquisition pathway.
SEC. ___.—RAPID CONTRACTING MECHANISM FOR SOFTWARE ACQUISITION.—(a) IN GENERAL.—Not later than 270 days after the date of the enactment of thisAct, the Secretary of Defense shall establish an agile contracting mechanism to support thesoftware acquisition pathway developed pursuant to section 800 of the National Defense
Authorization Act for Fiscal Year 2020 and embedded in Department of Defense Directives
5000.02 and 5000.87.
(b) CHARACTERISTICS.—The agile contracting mechanism established pursuant
to subsection (a) shall authorize processes pursuant to which—(1) a contract is awarded on the basis of statements of qualifications andpast performance data submitted by contractors, supplemented by discussions
with two or more contractors determined to be the most highly-qualified, without
regard to price;p667

D R A F T L E G I S L AT I V E L A N G U A G E(2) the contract identifies the contractor team to be engaged for the work,and substitutions shall not be made during the base contract period without theadvance written consent of the contracting officer;(3) the contractor reviews existing software in consultation with theuser community and utilizes user feedback to define and prioritize software
requirements, and to design and implement new software and software upgrades,
as appropriate;(4) an independent, non-advocate cost estimate is developed in parallelwith engineering of the software, leveraging agile cost estimation best practices
rather than counting source lines of code; and(5) value-based performance metrics are established and can be
automatically generated by users to address issues such as deployment rate andspeed of delivery, response rate such as the speed of recovery from outages
and cybersecurity vulnerabilities, and assessment and estimation of the size andcomplexity of software development effort.
Component 4: Modernize the Budget and Oversight Processes for Digital Technologies.
Update title 10, Section 181 to designate USD(R&E) Co-Chair and Chief Science Advisor
to the JROC.
SEC. ___.—ENHANCED ROLE OF UNDER SECRETARY OF DEFENSE FORRESEARCH AND ENGINEERING ON THE JOINT REQUIREMENTS OVERSIGHTCOUNCIL.—Section 181 of title 10, United States Code, is amended—(1) in subsection (b), by.—(A) inserting “the Secretary of Defense and” before “the Chairman of the
Joint Chiefs of Staff”;(7);(B) redesignating paragraphs (2) through (6) as paragraphs (3) through(C) inserting a new paragraph (2), as follows:“(2) leveraging awareness of global technology trends, threats, andadversary capabilities to address gaps in joint military capabilities and validate
technical feasibility of requirements developed by the military services;”; and(D) in redesignated paragraphs (4)(B) and (5) by inserting “the Secretaryof Defense and” before “the Chairman of the Joint Chiefs of Staff”;

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APPENDIX D(2) in subsection (c), by—(A) striking “Chairman of the Joint Chiefs of Staff for making
recommendations about” in paragraph (1)(A) and inserting “Council for”;(B) redesignating subparagraphs (B) through (E) of paragraph (1) as
subparagraphs (C) through (F);(C) adding a new paragraph (1)(B), as follows:“(B) The Under Secretary of Defense for Research and Engineering, whois the co-Chair of the Council and is the Chief Science Advisor to the Council.”;(D) by striking in paragraph (2) “(B), (C), (D), and (E)” and inserting “(C),(D), (E), and (F)”; and(E) by amending paragraph (3) to read as follows:“(3) In making any recommendation to the Secretary and the Chairmanof the Joint Chiefs of Staff pursuant to this section, the Co-Chairs of the Council
shall provide any dissenting view of members of the Council with respect to such
recommendation.”; and(3) in subsection (d), by—(A) striking subparagraph (1)(D); and(B) redesignating subparagraphs (E) through (H) of paragraph (1) asparagraphs (D) through (G).
Direct the Secretary of Defense to establish the dedicated AI fund.
SEC. ___.—ARTIFICIAL INTELLIGENCE DEVELOPMENT AND PROTOTYPINGFUND.—(a) IN GENERAL.—The Secretary of Defense shall establish a fund to be known
as the ‘‘Artificial Intelligence Development and Prototyping Fund’’ to support operational
prototyping and speed the transition of artificial intelligence-enabled applications into both
service-specific and joint mission capabilities with priority on joint mission capabilities for
Combatant Commanders. The Fund shall be managed by the Under Secretary of Defense
for Research and Engineering, in consultation with the Joint Artificial Intelligence Center,
the Joint Staff, and the military services.

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D R A F T L E G I S L AT I V E L A N G U A G E(b) TRANSFER AUTHORITY.—Amounts available in the Fund may be transferred
to a military department for the purpose of carrying out a development or prototyping
program selected by the Under Secretary of Defense for Research and Engineering forthe purposes described in paragraph (1). Any amount so transferred shall be credited tothe account to which it is transferred. The transfer authority provided in this subsection isin addition to any other transfer authority available to the Department of Defense.
(c) CONGRESSIONAL NOTICE.—The Under Secretary of Defense for Researchand Development shall notify the congressional defense committees of all transfers underparagraph (2). Each notification shall specify the amount transferred, the purpose of
the transfer, and the total projected cost and estimated cost to complete the acquisition
program to which the funds were transferred.
CHAPTER 3: AI AND WARFARE
Blueprint for Action
Recommendation: Establish AI-readiness performance goals.
Require the Secretary of Defense to establish performance objectives and accompanying
metrics for AI and digital readiness and provide an update to Congress no later than 120
days after approving these goals.
SEC. ___.—ARTIFICIAL INTELLIGENCE READINESS GOALS.—(a) IN GENERAL.—Not later than one year after the date of the enactment of thisAct, the Secretary of Defense shall review the potential applications of artificial intelligenceand digital technology to Department of Defense platforms, processes and operations,and establish performance objectives and accompanying metrics for the incorporation of
artificial intelligence and digital readiness into such platforms, processes and operations.
(b) SKILLS GAPS.—As a part of the review required by subsection (a), theSecretary shall direct the military departments and defense components to—(1) conduct a comprehensive review of skill gaps in the fields of softwaredevelopment, software engineering, knowledge management, data science, andartificial intelligence;(2) assess the number and qualifications of civilian personnel needed forboth management and specialist tracks in such fields;(3) assess the number of military personnel (officer and enlisted) needed
for both management and specialist tracks in such fields; and(4) establish recruiting, training, and talent management goals to achieve
and maintain staffing levels needed to fill identified gaps and meet the Department’s
needs for skilled personnel.
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APPENDIX D(c) REPORT TO CONGRESS.—Not later than 120 days after the completion of thereview required by subsection (a), the Secretary shall report to Congress on the findings ofthe review and any action taken or proposed to be taken by the Secretary to address suchfindings.
Recommendation: Promote AI interoperability and the adoption of critical emerging
technologies among allies and partners.
Component 6: Modify authorities and processes in order to improve DoD’s ability to
conduct international capability development.
SEC. ___.—ENHANCED AUTHORITY TO ENTER INTO COOPERATIVE RESEARCH
AND DEVELOPMENT AGREEMENTS WITH INTERNATIONAL PARTNERS.—(a) AUTHORITY OF SECRETARY OF DEFENSE.—Section 2350a of title 10, UnitedStates Code, is amended—(1) In subsection (a), by—(A) Adding a new subparagraph (F) at the end of paragraph (2),
as follows:“(F) Any business, academic or research institution, or other nongovernmental entity organized pursuant to the laws of a country referred
to in subparagraphs (C), (D) and (E), subject to the consent of the country
involved.”;(B) Amending paragraph (3) by striking “a country referred to in
subparagraph (E) of paragraph (2),” and inserting “a country referred to in
subparagraph (E) of paragraph (2) or a non-governmental entity referred
to in subparagraph (F) of such paragraph,”; and(C) Adding a new paragraph (4), as follows:“(4) The Secretary may delegate the authority to enter memorandaof understanding pursuant to this section to the secretary of a military
department, the Director of the Joint Artificial Intelligence Center, and the
Director of the Defense Advanced Research Projects Agency, subject
to such terms and conditions as may be necessary to ensure that any
agreements entered are consistent with the foreign policy and defense
policy of the United States.”; and(2) In paragraph (1) of subsection (b), by striking “will improve, through theapplication of emerging technology,” and inserting “is likely to improve, throughthe application or enhancement of emerging technology,”;p
671

D R A F T L E G I S L AT I V E L A N G U A G E(3) In subsection (c), by adding at the end the following new sentence:“If a foreign partner is expected to contribute significantly to the developmentof a new or novel capability, full consideration shall be given to non-monetarycontributions, including the value of research and development capabilities andthe strategic partnerships.”(b) AUTHORITY OF THE PRESIDENT.—Section 2767 of title 22, United States
Code, is amended—(1) in subsection (c), by adding at the end the following new sentence:“If a foreign partner is expected to contribute significantly to the developmentof a new or novel capability, full consideration shall be given to non-monetarycontributions, including the value of research and development capabilities andthe strategic partnerships.”(2) in subsection (f), by inserting before the semicolon in subparagraph (4)the following: “(and a description of any non-monetary contributions made by suchparticipants)”; and(3) in subsection (j), by—(A) amending the title to read as follows: “Cooperative projectagreements with friendly foreign countries not members of NATO andwith non-governmental organizations in NATO and friendly non-NATOcountries”; and(B) amending paragraph (2) to read as follows:“(2) The President may enter into a cooperative project agreementwith any business, academic or research institution, or other nongovernmental entity organized pursuant to the laws of NATO member ora friendly foreign country that is not a member of NATO, subject to theconsent of the country involved.”
CHAPTER 5: AI AND THE FUTURE OF NATIONAL INTELLIGENCE
Blueprint for Action
Recommendation: Empower the IC’s science and technology leadership.
Designate the Director of S&T within ODNI as the IC CTO and grant that position additional
authorities for establishing policies on, and supervising, IC research and engineering,
technology development, technology transition, appropriate
prototyping activities,
experimentation, and developmental testing activities.

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APPENDIX D

Grant the Director of National Intelligence sufficient budgetary authorities to enforce
technical standards across the IC, including the ability to fence or otherwise withhold
funding for programs that are not compliant with established common standards andpolicies.
SEC. ___.—CHIEF TECHNOLOGY OFFICER FOR THE INTELLIGENCE COMMUNITY.—
Section 3030 of title 50, United States Code, is amended—(1) in subsection (a), by striking “who shall be appointed by the Director of National
Intelligence” and inserting “who shall be appointed by the Director of National Intelligenceand shall serve as the Chief Technology Officer for the Intelligence Community.”; and(2) in subsection (c), by—(7); and(A) redesignating paragraphs (2) through (5) as paragraphs (4) through(B) inserting new paragraphs (2) and (3), as follows:“(2) establish policies for the intelligence community on researchand engineering, technology development, technology transition,
prototyping activities, experimentation, and developmental testing, andoversee the implementation of such policies;“(3) establish common technical standards and policies necessary
to rapidly scale artificial intelligence-enabled applications across theintelligence community;”.
Suggested Report Language: The Chief Technology Officer for the Intelligence Community
shall collect information on each Intelligence Community element’s compliance withapplicable standards and policies for artificial intelligence research and development,
and shall provide such information to the Director of National Intelligence. The Intelligence
Committees encourage the Director of National Intelligence to closely review the complianceinformation and place a temporary hold on an Intelligence Community element that fails
to execute artificial intelligence research and development funds in accordance with theapplicable standards and policies.
Establish a fund that would allow the DNI to identify and invest in AI applications withoutsized potential that may not have an identified source of agency or program funding as
they near the end of their S&T life cycle.
SEC. ___.—ARTIFICIAL INTELLIGENCE CRITICAL APPLICATIONS FUND FOR THE
INTELLIGENCE COMMUNITY.—(a) IN GENERAL.—The Director of National Intelligence shall establish a fundp673

D R A F T L E G I S L AT I V E L A N G U A G E

to be known as the ‘‘Artificial Intelligence Critical Applications Fund’’ to support agile
development and fielding of artificial intelligence-enabled applications with exceptional
potential for the intelligence community. The Fund shall be managed by the Directorof Science and Technology, in consultation with the National Intelligence Science andTechnology Committee established pursuant to section 3030 of title 50, United States
Code.
(b) TRANSFER AUTHORITY.—Amounts available in the Fund may be transferred
to any element of the intelligence community for the purpose of carrying out a development
or fielding program selected by the Director of Science and Technology for the purposes
described in subsection (a). Any amount so transferred shall be credited to the accountto which it is transferred. The transfer authority provided in this subsection is in addition
to any other transfer authority available to the Director of National Intelligence and theintelligence community.
(c) CONGRESSIONAL NOTICE.—The Director of National Intelligence shallnotify the congressional intelligence committees and the congressional appropriationscommittees of all transfers under paragraph (2). Each notification shall specify the amounttransferred, the purpose of the transfer, and the total projected cost and estimated cost tocomplete the acquisition program to which the funds were transferred.
Establish a 10-year, $1,000,000,000 Program of Record to provide long-term, predictable
funding for technologies identified in the technology annex to the National Intelligence
Strategy.
SEC. ___.—ARTIFICIAL INTELLIGENCE TECHNOLOGY ROADMAP AND FUNDING
PLAN FOR THE INTELLIGENCE COMMUNITY.—(a) IN GENERAL.—Not later than 180 days after the date of the enactment of thisAct, the Director of National Intelligence, in consultation with the Secretary of Defense,shall develop a technology annex to the National Intelligence Strategy and a ten-year plan
to provide long-term, predictable funding of up to one billion dollars to implement the steps
identified in such annex.
(b) CONTENTS OF TECHNOLOGY ANNEX.—The technology annex required bysubsection (a) shall provide a technology roadmap for the adoption of artificial intelligenceenabled applications to solve operational intelligence requirements, including:(1) A description of challenges faced in the intelligence community’s efforts
to analyze the global environment and monitor technological advancements,
adversarial capability development, and emerging threats;(2) Identification of technical capabilities, including artificial intelligencecapabilities, needed to enable steps to address each challenge;p674

APPENDIX D(3) A prioritized, time-phased plan for developing or acquiring suchtechnical capabilities, that takes into account research and development
timelines, a strategy for public private partnerships, and a strategy for connecting
researchers to end users for early prototyping, experimentation, and iteration;(4) Any additional or revised acquisition policies and workforce training
requirements that may be needed to enable intelligence community personnel toidentify, procure, integrate, and operate the technologies identified in the annex;(5) Identification of infrastructure requirements for developing anddeploying technical capabilities, including:(A) data, compute, storage, and network needs;(B) a resourced and prioritized plan for establishing suchinfrastructure; and(C) an analysis of the testing, evaluation, verification, and validation
requirements to support prototyping and experimentation and a resourced
plan to implement them, including standards, testbeds, and red-teams fortesting artificial intelligence systems against digital “denial & deception”
attacks.
(6) Consideration of human factor elements associated with priority
technical capabilities, including innovative human-centric approaches to userinterface, human-machine teaming, and workflow integration;(7) Consideration of interoperability with allies and partners, including
areas for sharing of data, tools, and intelligence products; and(8) Flexibility to adapt and iterate annex implementation at the speed oftechnological advancement.
Recommendation: Improve coordination between the IC and DoD.
Revise the National Defense Authorization Act for Fiscal Year 2021 (FY 2021 NDAA)
provision authorizing a Steering Committee on Emerging Technology by designating it to
be tri-chaired by the Deputy Secretary of Defense, the Vice Chairman of the Joint Chiefs ofStaff, and the Principal Deputy Director of National Intelligence.
See Chapter 2 recommendation “Drive Change through Top-Down Leadership” forproposed legislative text.

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Recommendation: Aggressively pursue security clearance reform for clearances at the
Top Secret level and above, and enforce security clearance reciprocity among membersof the IC.
Congress should require the DNI to develop an implementation plan for security clearance
reform for clearances at the Top Secret and above level including detailed timelines andmetrics.
Congress should require the DNI and the directors of the major intelligence services toregularly report on progress to the oversight committees.
SEC. ___.—IMPLEMENTATION PLAN FOR SECURITY CLEARANCE REFORM.—(a) PLAN REQUIRED.—Not later than 180 days after the date of the enactmentof this Act, the Director of National Intelligence shall develop an implementation planfor security clearance reform for clearances at the Top Secret level and above. The
implementation plan shall include, at a minimum:(1) detailed implementation metrics and timelines;(2) steps to be taken to collaborate with the private sector and academia
to develop data-informed behavioral approaches to understanding risk factors
and security clearance adjudication; and(3) steps to be taken to reform identity management and ensure seamlesssecurity clearance reciprocity across the intelligence community (including any
enforcement mechanisms that may be needed to ensure such reciprocity).
(b) REPORTS REQUIRED.—Not later than 270 days after the date of the enactmentof this Act and annually for five years thereafter, the Director of National Intelligence shallreport to the congressional intelligence committees on the implementation of the planrequired by subsection (a) and the progress that has been made toward security clearance
reform.
CHAPTER 6: TECHNICAL TALENT IN GOVERNMENT
Blueprint for Action
Recommendation: Create a National Reserve Digital Corps.
NATIONAL RESERVE DIGITAL CORPS ACT OF 2021
SECTION. 1.—SHORT TITLE.—This Act may be cited as the “National Reserve Digital
Corps Act of 2021”.

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SEC. 2.—ESTABLISHMENT OF NATIONAL RESERVE DIGITAL CORPS.—(a) IN GENERAL.—Subpart I of part III of title 5, United States Code, is amended
by inserting after chapter 102 the following new chapter:
CHAPTER 103—NATIONAL RESERVE DIGITAL CORPS
SEC. 10301. Establishment.
SEC. 10302. Definitions.
SEC. 10303. Organization.
SEC. 10304. Work on Behalf of Federal Agencies.
SEC. 10305. Digital Corps Scholarship Program.
SEC. 10306. Duration of Pilot Program.
SEC. 10307. Authorization of Appropriation.
SEC. 10301. ESTABLISHMENT.—For the purposes of attracting, recruiting, and training
a corps of world-class digital talent to serve the national interest and enable the Federal
Government to become a digitally proficient enterprise, there is established within the
Office of Management and Budget a pilot program for a civilian National Reserve Digital
Corps, whose members shall serve as special government employees, working not fewer
than 30 days per year as short-term advisors, instructors, or developers in the Federal
Government.
SEC. 10302. DEFINITIONS.—(a) DIRECTOR.—The term “Director” means the Director of the Office ofManagement and Budget.
(b) NODE.—The term “node” means a group of persons or team organized under
the direction of a node leader to provide digital service to one or more Federal agencies
pursuant to an agreement between the Office of Management Budget and each other
Federal agency.
(c) NODE LEADER.—The term “node leader” means a full time government
employee, as defined by section 2105 of title 5, United States Code, selected under thisAct to lead one or more nodes, who reports to the Director or the Director’s designee.
(d) NODE MEMBER.—The term “node member” means a special government
employee, as defined by section 202 of title 18, United States Code, selected under thisAct to work at least 38 days per fiscal year and report to a node leader in furtherance ofthe mission of a specified node.
SEC. 10303. ORGANIZATION.—(a) NODES AND NODE LEADERS.—The National Reserve Digital Corps shall beorganized into nodes, each of which shall be under the supervision of a node leader .
(b) ADMINISTRATIVE SUPPORT.—The National Reserve Digital Corps shallreceive funding and administrative support from the Office of Management and Budget,p677

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which shall be responsible for selecting node leaders, establishing standards, ensuringthat nodes meet government client requirements, maintaining security clearances,establishing access to an agile development environment and tools, and facilitating
appropriate technical exchange meetings.
(c) HIRING AUTHORITY.—(1) Direct Hiring Authority of Node Members.—The Director of the Officeof Management and Budget, on the recommendation of a node leader, may
appoint, without regard to the provisions of subchapter I of chapter 33 (other thansections 3303 and 3328 of such chapter), a qualified candidate to a position in
the competitive service in the Office of Management and Budget to serve as anode member. This provision shall not preclude the Director from hiring additional
employees, including full time government employees, as defined by section 2105
of title 5, United States Code.
(2) Term and Temporary Appointments of Node Members.—The Directorof the Office of Management and Budget, on the recommendation of a nodeleader, may make a noncompetitive temporary appointment or term appointment
for a period of not more than 18 months, of a qualified candidate to serve as anode member in a position in the competitive service for which a critical hiring
need exists, as determined under section 3304 of title 5, United States Code,
without regard to sections 3327 and 3330 of such title.
SEC. 10304. WORK ON BEHALF OF FEDERAL AGENCIES.—(a) PURPOSE.—Each node shall undertake projects to assist Federal agencies by
providing digital education and training, performing data triage and providing acquisition
assistance, helping guide digital projects and frame technical solutions, helping build
bridges between public needs and private sector capabilities, and related tasks.
(b) AUTHORITIES.—Projects may be undertaken—(1) on behalf of a Federal agency—(A) by direct agreement between the Office of Management andBudget and the Federal agency; or(B) at the direction of the Office of Management and Budget at therequest of the Federal agency; or(2) to address a digital service need encompassing more than one Federal
agency—p
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APPENDIX D(A) at the direction of the Office of Management and Budget; or(B) on the initiative of a node leader.
SEC. 10305. DIGITAL CORPS SCHOLARSHIP PROGRAM.—(a) IN GENERAL.—The Director shall establish a National Reserve Digital Corps
scholarship program to provide full scholarships to competitively selected students who
commit to study specific disciplines related to national security digital technology .
(b) SERVICE OBLIGATION.—Each student, prior to commencing the Digital Corps
Scholarship Program, shall sign an agreement with respect to the student’s commitment to
the United States. The agreement shall provide that the student agree to the following:(1) a commitment to serve as an intern in a Federal agency for at least six
weeks during each of the summers before their junior and senior years; and(2) a commitment to serve in the National Reserve Digital Corps for six
years after graduation.
(c) PROGRAM ELEMENTS.—In establishing the program, the Director shalldetermine the following—(1) Eligibility standards for program participation;(2) Criteria for establishing the dollar amount of a scholarship, including
tuition, room and board;(3) Repayment requirements for students who fail to complete their service
obligation;(4) An approach to ensuring that qualified graduates of the program arepromptly hired and assigned to node leaders; and(5) Resources required for the implementation of the program.
(d) CONTINUING EDUCATION.—The Director shall establish a training andcontinuing education program to fund educational opportunities for members of the
National Digital Reserve Corps, including conferences, seminars, degree and certificate
granting programs, and other training opportunities that are expected to increase thedigital competencies of the participants.
(e) IMPLEMENTATION.—

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D R A F T L E G I S L AT I V E L A N G U A G E(1) Not later than six months after the date of the enactment of this Act, theDirector shall establish the administrative support function and issue guidance for
the National Reserve Digital Corps, which shall include the identification of pointsof contact for node leaders at Federal agencies.
(2) Not later than one year after the date of the enactment of this Act,
the Director shall appoint not fewer than five node leaders under the National
Reserve Digital Corps program and authorize the node leaders to begin recruiting
reservists and undertaking projects for Federal agencies.
(3) Beginning two years after the date of the enactment of this Act, theDirector shall report annually to Congress on the progress of the National Reserve
Digital Corps. The Director’s report shall address, at a minimum, the following
measures of success:(A) The number of technologists who participate in the National
Reserve Digital Corps annually;(B) Identification of the Federal agencies that submitted work
requests, the nature of the work requests, which work requests wereassigned a node, and which work requests were completed or remain inprogress;(C) Evaluations of results of National Reserve Digital Corps
projects by Federal agencies; and(D) Evaluations of results of National Reserve Digital Corps
projects by reservists.
SEC. 10306. DURATION OF PILOT PROGRAM.—The pilot program under this Act shallterminate no earlier than six years after its commencement.
SEC. 10307. AUTHORIZATION OF APPROPRIATION.—There is authorized to beappropriated $16,000,000 to remain available until fiscal year 2023 the initial administrative
cost, including for the salaries and expenses scholarship and education benefits, for the
National Digital Reserve Corps.
Recommendation: Create Digital Talent Recruiting Offices Aligned with Digital Corps.
SEC. ___.—DIGITAL TALENT RECRUITING OFFICES.—(a) DIGITAL TALENT RECRUITING FOR THE DEPARTMENT OF DEFENSE.—

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APPENDIX D(1) Not later than 270 days after the date of the enactment of this Act, theSecretary of Defense shall designate a chief digital recruiting officer within theoffice of the Under Secretary of Defense for Personnel and Readiness to oversee
a digital recruiting office to carry out the responsibilities set forth in paragraph (2).
(2) The chief digital recruiting officer shall be responsible for—(A) identifying Department of Defense needs for specific types ofdigital talent;(B) recruiting technologists, in partnership with the military
services and defense components, including by attending conferences
and career fairs, and actively recruiting on university campuses and fromthe private sector;(C) integrating Federal scholarship for service programs into
civilian recruiting;(D) offering recruitment and referral bonuses; and(E) partnering with human resource teams in the military services
and defense components to use direct-hire authorities to accelerate hiring.
(3) The Secretary of Defense shall ensure that the chief digital recruiting
officer is provided with personnel and resources sufficient to maintain an officeand to carry out the duties set forth in paragraph (2).
(b) DIGITAL TALENT RECRUITING FOR THE INTELLIGENCE COMMUNITY.—(1) Not later than 270 days after the date of the enactment of this Act, theDirector of National Intelligence shall designate a chief digital recruiting officer
to oversee a digital recruiting office to carry out the responsibilities set forth inparagraph (2).
(2) The chief digital recruiting officer shall be responsible for—(A) identifying intelligence community needs for specific types ofdigital talent;(B) recruiting technologists, in partnership with components of theintelligence community, by attending conferences and career fairs, andactively recruiting on college campuses;p681

D R A F T L E G I S L AT I V E L A N G U A G E(C) integrating Federal scholarship for service programs into
intelligence community recruiting;(D) offering recruitment and referral bonuses; and(E) partnering with human resource teams in the componentsof the intelligence community to use direct-hire authorities to accelerate
hiring.
(3) The Director of National Intelligence shall ensure that the chief digital
recruiting officer is provided with personnel and resources sufficient to maintain
an office and to carry out the duties set forth in paragraph (2).
(c) DIGITAL TALENT RECRUITING FOR THE DEPARTMENT OF HOMELAND
SECURITY.—(1) Not later than 270 days after the date of the enactment of this Act, theSecretary of Homeland Security shall designate a chief digital recruiting officer
to oversee a digital recruiting office to carry out the responsibilities set forth inparagraph (2).
(2) The chief digital recruiting officer shall be responsible for—(A) identifying Department of Homeland Security needs for
specific types of digital talent;(B) recruiting technologists, in partnership with components of the
Department of Homeland Security, by attending conferences and career
fairs, and actively recruiting on college campuses;(C) integrating Federal scholarship for service programs into
civilian recruiting;(D) offering recruitment and referral bonuses; and(E) partnering with human resource teams in the componentsof the Department of Homeland Security to use direct-hire authorities toaccelerate hiring.
(3) The Secretary of Homeland Security shall ensure that the chief digital
recruiting officer is provided with personnel and resources sufficient to maintain
an office and to carry out the duties set forth in paragraph (2).

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APPENDIX D(d) DIGITAL TALENT RECRUITING FOR THE DEPARTMENT OF ENERGY.—(1) Not later than 270 days after the date of the enactment of this Act, theSecretary of Energy shall designate a chief digital recruiting officer to oversee a
digital recruiting office to carry out the responsibilities set forth in paragraph (2).
(2) The chief digital recruiting officer shall be responsible for—(A) identifying Department of Energy needs for specific types ofdigital talent;(B) recruiting technologists, in partnership with Department ofEnergy programs, by attending conferences and career fairs, and actively
recruiting on college campuses;(C) integrating Federal scholarship for service programs into
civilian recruiting;(D) offering recruitment and referral bonuses; and(E) partnering with human resource teams in Department ofEnergy programs to use direct-hire authorities to accelerate hiring.
(3) The Secretary of Energy shall ensure that the chief digital recruiting
officer is provided with personnel and resources sufficient to maintain an officeand to carry out the duties set forth in paragraph (2).
Recommendation: Grant exemption from OPM General Schedule Qualification Policies forSpecific Billets and Position Descriptions.
SEC. ___.—WAIVER OF QUALIFICATION STANDARDS FOR GENERAL SCHEDULE
POSITIONS IN ARTIFICIAL INTELLIGENCE.—(a) DEPARTMENT OF DEFENSE.—Two-star and above commands and theircivilian equivalents are authorized to waive any General Schedule qualification standard
established by the Office of Personnel Management in the case of any applicant for aposition in artificial intelligence who is determined by a hiring manager, in consultation withsubject matter experts, to be the best qualified candidate for the position.
(b) OTHER NATIONAL SECURITY AGENCIES.—The Director of the Office of
Personnel Management shall establish a process by which the the Attorney General, theSecretary of Homeland Security, the Secretary of State, the Secretary of Commerce, theDirector of National Intelligence, and the head of any element of the Intelligence Community
may request an exception to any General Schedule qualification standard in any case inp683

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which the agency head determines that national security needs would best be met byhiring managers making an independent judgment about qualifications and pay grades
for a position in artificial intelligence with the advice of subject matter experts. The process
shall provide for requests to be made for individual billets, for position descriptions, or for
categories of individual billets or position descriptions at the discretion of the agency head.
Recommendation: Expand the CyberCorps: Scholarship for Service.
SEC. ___.—AMENDMENT TO THE FEDERAL CYBER SCHOLARSHIP-FOR- SERVICEPROGRAM.—(a) AMENDMENTS TO TITLE 15, UNITED STATES CODE.—Section 7442 of title
15, United States Code, is amended—(1) By amending the title to read: “Federal Cyber and Artificial Intelligence
Scholarship-for-Service Program”;(2) in subsection (a), by striking “industrial control system” and all that follows
and inserting in lieu thereof “digital engineers, artificial intelligence practitioners,
data engineers, data analysts, data scientists, industrial control system security
professionals, security managers, and cybersecurity course instructors to meetthe needs of the cybersecurity and artificial intelligence missions for Federal,
State, local, tribal, and territorial governments.”;(3) in subsection (b), by—(A) striking “and” at the end of paragraph (3);(B) striking the period at the end of paragraph (4) and inserting inlieu thereof “; and”; and(C) adding a new paragraph (5), as follows:“(5) provide an opportunity for scholarship recipients toinitiate the security clearance process at least one year before
their planned graduation date.”; and(4) in subsection (c), by striking “3 years” and inserting “4 years”.
(b) SAVINGS PROVISION.—Nothing in this section, or an amendment made bythis section, shall affect any agreement, scholarship, loan, or repayment under section 302
of the Cybersecurity Enhancement Act of 2014 (15 U.S.C. 7442), in effect on the day before
the date of the enactment of this section.

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APPENDIX D

Recommendation: Create a United States Digital Service Academy.
UNITED STATES DIGITAL SERVICE ACADEMY ACT OF 2021
SECTION. 1.—SHORT TITLE.—This Act may be cited as the “United States Digital
Service Academy Act of 2021”.
SEC. 2.—ESTABLISHMENT OF ACADEMY.—(a) ESTABLISHMENT.—There is established as an independent entity within
the Federal Government a United States Digital Service Academy (hereafter referred
to as the “ACADEMY”), at a location to be determined, to serve as a federally-funded,accredited, degree-granting university for the instruction of selected individuals in digitaltechnical fields and the preparation of selected individuals for civil service with the Federal
Government.
(b) DIGITAL TECHNICAL FIELDS DEFINED.—The term “digital technical fields”
includes artificial intelligence, software engineering, electrical science and engineering,
computer science, molecular biology, computational biology, biological engineering,
cybersecurity, data science, mathematics, physics, human-computer interaction, robotics,
and design and any additional fields specified in regulations by the Board.
SEC. 3.—ORGANIZATION.—(a) BOARD OF REGENTS.—The business of the Academy shall be conducted bya Board of Regents (hereafter referred to as the “Board”).
(1) COMPOSITION.—The Board shall consist of nine voting members andex officio members, as set forth in this subsection.
(2) VOTING MEMBERS.—The President shall appoint, by and with theconsent of the Senate, nine persons from civilian life who have demonstrated
achievement in one or more digital technical fields, higher education administration,
or Federal civilian service, to serve as voting members on the Board. Appointmentof the first voting members shall be made not later than 180 days after enactmentof this Act.
(3) EX OFFICIO MEMBERS.—Ex officio members shall include—(A) The Secretary of State;(B) The Secretary of Defense;(C) The Attorney General;p685

D R A F T L E G I S L AT I V E L A N G U A G E(D) The Secretary of Commerce;(E) The Secretary of Energy;(F) The Secretary of Homeland Security;(G) The Director of National Intelligence;(H) The Director of the Office of Personnel Management; and(I) such other Federal Government officials as determined by the
President.
(2) TERM OF VOTING MEMBERS.—The term of office of each voting
member of the Board shall be six years, except that initial terms shall be staggeredat two year intervals and any member appointed to fill a vacancy occurring before
the expiration of a term shall be appointed for the remainder of such term.
(3) PRESIDENT OF THE BOARD.—One of the members (other than an ex
officio member) shall be designated by the President as Chairman and shall be
the presiding officer of the Board.
(b) KEY POSITIONS.—There shall be at the Academy the following:(1) A Superintendent;(2) A Dean of the Academic Board, who is a permanent professor;(3) A Director of Admissions; and(4) A Director of Placement.
(c) SUPERINTENDENT.—The Board shall appoint a Superintendent of the
Academy, who shall serve for a term of six years. The Superintendent, acting pursuant to
the oversight and direction of the Board, shall be responsible for the day-to-day operationsof the Academy and the welfare of the students and the staff of the Academy. The Board
shall select the first Superintendent of the Academy no later than 60 days after the Board
is established.
(d) ADVISORY BOARD.—The Board of Regents and the Superintendent shall be
assisted by an Advisory Board, composed of commercial and academic leaders in digitaltechnical fields and higher education. The Advisory Board shall adhere to the requirementsof the Federal Advisory Committee Act, Pub.L. 92–463.

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APPENDIX D(e) INTERAGENCY WORKING GROUP.—(1) ESTABLISHMENT.—The Office of Personnel Management shallestablish and lead an interagency working group to annually assess and report to
the Academy the need for civil servants at agencies in digital technical fields forthe purposes of informing Academy student field of study and agency placement.
(2) RESPONSIBILITIES.—The interagency working group shall be
responsible for—(A) establishing a range of Academy graduates needed during
the ensuing five-year period, by agency and digital technical field; and(B) undertaking necessary steps to enable each agency identified
to hire Academy graduates into full-time positions in the civil service.
(3) COMPOSITION.—The interagency working group shall consist of thefollowing officials or their designees:(A) The Secretary of State;(B) The Secretary of Defense;(C) The Attorney General;(D) The Secretary of Commerce;(E) The Secretary of Energy;(F) The Secretary of Homeland Security;(G) The Director of National Intelligence;(H) The Director of the Office of Personnel Management; and(I) such other Federal Government officials as determined by the
Director of the Office of Personnel Management.
SEC. 4.—FACULTY.—(a) NUMBER OF FACULTY.—The Superintendent of the Academy may employ
as many professors, instructors, and lecturers at the Academy as the Superintendentconsiders necessary to achieve academic excellence.

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D R A F T L E G I S L AT I V E L A N G U A G E(b) FACULTY COMPENSATION.—The Superintendent may prescribe thecompensation of persons employed under this section. Compensation and benefits forfaculty members of the Academy shall be sufficiently competitive to achieve academic
excellence, as determined by the Superintendent.
(c) FACULTY EXPECTATIONS.—Faculty members shall—(1) possess academic expertise and teaching prowess;(2) exemplify high standards of conduct and performance;(3) be expected to participate in the full spectrum of academy programs,
including providing leadership for the curricular and extracurricular activities ofstudents;(4) comply with the standards of conduct and performance established by
the Superintendent; and(5) participate actively in the development of the students through theenforcement of standards of behavior and conduct, to be established in the
Academy’s rules and regulations.
(d) DEPARTMENT TITLES.—The Superintendent may prescribe the titles of eachof the departments of instruction and the professors of the Academy.
SEC. 5.—STUDENT QUALIFICATIONS AND REQUIREMENTS FOR ADMISSION.—(a) ADMISSIONS REQUIREMENTS.—A student wishing to be admitted to the
Academy shall fulfill admission requirements to be determined by the Superintendent andapproved by the Board of Regents.
(b) HONOR CODE.—A student wishing to be admitted to the Academy shall signan Honor Code developed by the Superintendent of the Academy and approved by the
Board of Regents. A violation of the honor code may constitute a basis for dismissal fromthe Academy.
SEC. 6.—APPOINTMENT OF STUDENTS.—(a) NOMINATIONS PROCESS.—Prospective applicants to the Academy for seats
described in paragraphs (1) and (2) of subsection (b) shall follow a nomination processestablished by the Director of Admissions of the Academy that is similar to the processused for admission to the military academies of the United States Armed Forces.

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APPENDIX D(b) APPOINTMENTS.—(1) NOMINEES FOR CONGRESSIONAL SEATS.—Each member of the
Senate or the House of Representatives may nominate candidates from the State
that the member represents for each incoming first-year class of the Academy .
(2) EXECUTIVE BRANCH NOMINEES.—The President may nominate amaximum of 75 candidates to compete for the executive branch seats.
SEC. 7.—ACADEMIC FOCUS OF THE UNITED STATES DIGITAL SERVICE ACADEMY.—(a) CURRICULUM.—Each Academy student shall follow a structured curriculum
according to the program of study approved by the Board of Regents centered on digitaltechnical fields and incorporating additional core curriculum coursework in history,
government, English language arts including composition, and ethics.
(b) DEGREES CONFERRED UPON GRADUATION.—Under such conditions as
the Board of Regents may prescribe, once the Academy is accredited, the Superintendentof the Academy may confer a baccalaureate of science or baccalaureate of arts degree
upon a graduate of the Academy.
(c) MAJORS AND AREAS OF CONCENTRATION.—Under such conditions as
the Board of Regents may prescribe, the Superintendent of the Academy may prescribe
requirements for majors and concentrations and requirements for declaring a major orconcentration during the course of study.
(d) ADDITIONAL DIGITAL SERVICE OF CIVIL SERVICE PROGRAMMING.—
Under such conditions as the Board of Regents may prescribe, the Superintendent of the
Academy may prescribe requirements for each Academy student to participate in noncurricular programing during Academy terms and during the summer, which may include
internships, summer learning programs, and project-based learning activities.
SEC. 8.—CIVIL SERVICE REQUIREMENTS FOLLOWING GRADUATION.—(a) CIVIL SERVICE AGREEMENT.—Each Academy student, prior to commencing
the third year of coursework, shall sign an agreement with respect to the student’s lengthof civil service to the United States. The agreement shall provide that the student agrees
to the following:(1) The student will complete the course of instruction at the Academy,
culminating in graduation from the Academy.
(2) Unless the student pursues graduate education under subsection (f),upon graduation from the Academy, the student agrees to serve in the Federal civil
service for not less than five years following graduation from the Academy .

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D R A F T L E G I S L AT I V E L A N G U A G E(b) FAILURE TO GRADUATE.—(1) IN GENERAL.—An Academy student who has completed a minimumof four semesters at the Academy but fails to fulfill the Academy’s requirements forgraduation shall be—(A) dismissed from the Academy; and(B) obligated to repay the Academy for the cost of the delinquent
student’s education in the amount described in paragraph (2).
(2) AMOUNT OF REPAYMENT.—A student who fails to graduate shall havefinancial responsibility for certain costs relating to each semester that the student
was officially enrolled in the Academy as prescribed by the Superintendent.
(c) FAILURE TO ACCEPT OR COMPLETE ASSIGNED CIVIL SERVICE.—(1) IN GENERAL.—A student who graduates from the Academy but fails
to complete the full term of required civil service shall be obligated to repay the
Academy for a portion of the cost of the graduate’s education as determined byAcademy as set forth in this subsection.
(2) AMOUNT OF REPAYMENT.—In the case of a delinquent graduate
who fails to complete all years of public service required under subsection (a)(2)(including any additional years required for graduate education under subsection(f)), the delinquent graduate shall be financially responsible for the cost of thedelinquent graduate’s education (including the costs of any graduate education),
except that the amount of financial responsibility under this paragraph shall be
reduced by 20 percent for each year of civil service under subsection (a)(2) thatthe delinquent graduate did complete.
(d) EXCEPTIONS.—The Superintendent may provide for the partial or total waiveror suspension of any civil service or payment obligation by an individual under this section
whenever compliance by the individual with the obligation is impossible or deemed to
involve extreme hardship to the individual, or if enforcement of such obligation with respect
to the individual would be unconscionable.
(e) STUDENT SALARIES AND BENEFITS.—The Academy shall not be responsible
for the salaries and benefits of graduates of the Academy while the graduates are fulfillingthe civilian service assignment under this section. All salaries and benefits shall be paidby the employer with whom the Academy graduate is placed.
(f) GRADUATE EDUCATIONS.—An Academy student and the Superintendent
may modify the agreement under subsection (a) to provide that—p690

APPENDIX D(1) the Academy shall—(A) subsidize an Academy student’s graduate education; and(B) postpone the public service assignment required undersubsection (a)(2).
(2) the student shall—(A) accept a civil service assignment under subsection (g) upon
the student’s completion of the graduate program; and(B) add two additional years to the student’s civil service
commitment required under the agreement described in subsection (a)for every year of subsidized graduate education.
SEC. 9.—IMPLEMENTATION PLAN.—(a) Not later than 180 days after the enactment of this Act, the Superintendent,
in consultation with the Advisory Board, shall develop a detailed plan to implement the
Academy that complies with the requirements of this section. Upon approval by the Boardof Regents, the Superintendent shall present the implementation plan to Congress.
(b) CONTENTS OF PLAN.—The implementation plan described in section (a) shallprovide, a minimum, the following:(1) Identification and securement of an appropriate site for initial Academy
build-out with room for future expansion, to include a construction plan andtemporary site plan, if necessary;(2) Identification of gaps in the government’s current and envisioned
digital workforce by the interagency working group under the Office of Personnel
Management as established by section (3)(e);(3) Establishment of student qualifications and requirements for admission;(4) Establishment of the student appointment and nomination process;(5) Establishment of student honor and conduct code to include a plan forstudent noncompletion of requirements and obligations;(6) Establishment of the student curriculum;(7) Establishment of a mechanism for students to select fields of study
and annually select agencies and career fields within the limits prescribed byp691

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the interagency working group under the Office of Personnel Management asestablished by section (3)(e);(8) Establishment of a mechanism for graduates to transition from theAcademy to civil service employment by selected individual agencies;(9) Determination of the initial Academy departments and faculty needs;(10) Establishment of faculty and staff requirements and compensation;(11) Determination of non-academic staff required;(12) Recruitment and hiring of faculty, including tenure-track faculty,
adjunct faculty, part-time faculty and visiting faculty, and other staff as needed;(13) Identification of nonprofit and private sector partners;(14) Procurement of outside funds and gifts from individuals andcorporations for startup, administrative, maintenance, and infrastructure costs;(15) Establishment of the process to meet statutory and regulatory
requirements for establishing the Academy as an academic institution with degreegranting approval and for applying for degree program specific accreditation andensuring that the Academy obtains, no later than two years after enactment of thisAct, status as an accreditation candidate, as defined by a nationally recognizedaccrediting agency or association as determined by the Secretary of Education inaccordance with section 1099b in title 10, United States Code, before commencing
academic operations;(16) A plan commencing the Academy with an initial class of 500 students
three years after enactment of this Act;
(17) Procedures for incorporating accreditation assessments to facilitate
ongoing improvements to the Academy; and,(18) Procedures for assessing the size of the Academy and potential
expansion of student enrollment.
SEC. 10.—ADMINISTRATIVE MATTERS.—(a) FULLY-SUBSIDIZED EDUCATION.—Each Academy student’s tuition and roomand board shall be fully subsidized provided that the student completes the requirements of
the Academy and fulfills the civil service commitment as determined by the implementation
plan in section 9.

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APPENDIX D(b) GIFT AUTHORITY.—The Board of Regents may accept, hold, administer, andspend any gift, devise, or bequest of real property, personal property, or money madeon the condition that the gift, devise, or bequest be used for the benefit, or in connection
with, the establishment, operation, or maintenance, of the Academy. The Board of Regents
may accept a gift of services, which includes activities that benefit the education, morale,
welfare, or recreation of students, faculty or staff, for the Academy.
(1) LIMITATIONS AND PROHIBITIONS.—(A) IN GENERAL.—The Board of Regents may not accept a gift
under this subsection if the acceptance of the gift would reflect unfavorablyon the ability of any agency of the Federal Government to carry out any
responsibility or duty in a fair and objective manner, or would compromise
the integrity or appearance of integrity of any program of the Federal
Government or any officer or employee of the Federal Government who is
involved in any such program.
(B) FOREIGN GIFTS.—The Board of Regents may not accept a giftof services from a foreign government or international organization under
this subsection. A gift of real property, personal property, or money from aforeign government or international organization may be accepted under
this subsection only if the gift is not designated for a specific individual.
(C) APPLICABLE LAW.—No gift under this section may be
accepted with attached conditions inconsistent with applicable law or
regulation.
(D) MISSION.—No gift under this section may be accepted with
attached conditions inconsistent with the mission of the Academy .
(E) NAMING RIGHTS.—The Board of Regents may issue
regulations governing the circumstances under which gifts conditioned
on naming rights may be accepted, appropriate naming conventions, andsuitable display standards.
(2) TREATMENT OF GIFTS.—(A) Gifts and bequests of money, and the proceeds of the sale ofproperty, received under subsection shall be deposited in the Treasury in
the account of the Academy as no year money and may be expended inconnection with the activities of the Academy as determined by the Boardof Regents.

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D R A F T L E G I S L AT I V E L A N G U A G E(B) The Board of Regents may pay all necessary expenses inconnection with the conveyance or transfer of a gift, devise, or bequest
accepted under this section.
(C) For the purposes of Federal income, estate, and gift taxes,
any property, money, or services accepted under this subsection shallbe considered as a gift, devise, or bequest to or for the use of the UnitedStates.
(D) The Comptroller General shall make periodic audits of gifts,
devises, and bequests accepted under this section at such intervals as
the Comptroller General determines to be warranted. The Comptroller
General shall submit to Congress a report on the results of each suchaudit.
SEC. 11.—INITIAL APPROPRIATION.—There are authorized to be appropriated$40,000,000 to remain available until expended for the Academy’s initial administrative
cost and salaries and expenses.
Recommendation: Establish Career Fields for Government Civilians in Software
Development, Software Engineering, Data Science, Knowledge Management, and Artificial
Intelligence.
SEC.___.—NEW OCCUPATIONAL SERIES FOR DIGITAL CAREER FIELDS.—Not later
than 270 days after the date of the enactment of this Act, the Director of the Office of
Personnel Management shall exercise its authority under section 5105 of title 5, UnitedStates Code, to establish one or more new occupational series and associated policies
covering Federal Government positions in the fields of software development, software
engineering, data science, and knowledge management.
SEC.___.—NEW OCCUPATIONAL SERIES FOR ARTIFICIAL INTELLIGENCE.—Notlater than 270 days after the date of the enactment of this Act, the Director of the Officeof Personnel Management shall exercise its authority under section 5105 of title 5, UnitedStates Code, to establish a new occupational series and associated policies covering
Federal Government positions in the field of artificial intelligence.
Recommendation: Establish Digital Career Fields for Military Personnel.
SEC.___.—MILITARY CAREER FIELDS FOR SOFTWARE DEVELOPMENT, DATASCIENCE, AND ARTIFICIAL INTELLIGENCE.—Section 230 of the National Defense
Authorization Act for Fiscal Year 2020 is amended by adding the following new subsection:“(d) Not later than 270 days after the date of the enactment of this subsection, the Chiefof Staff of the Army, the Chief of Naval Operations, the Chief of Staff of the Air Force, andp
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APPENDIX D

the Commandant of the Marine Corps (collectively, the Service Chiefs) shall each establish
new military career fields for software development, data science, and artificial intelligencethat are open to commissioned officers, enlisted personnel and, as appropriate, warrant
officers. The Service Chiefs shall utilize the authority provided in sections 605 and 649a to
649k of title 10, United States Code, to ensure that military personnel in these career fields
who choose to specialize and focus on technical skill sets rather than pursue leadership
positions are not required to move outside their specialties or into management positions
to continue to promote.
CHAPTER 8: UPHOLDING DEMOCRATIC VALUES: PRIVACY, CIVIL LIBERTIES, AND
CIVIL RIGHTS IN USES OF AI FOR NATIONAL SECURITY
Blueprint for Action
Recommendation Set 1: Increase Public Transparency about AI Use through Improved
Reporting.
For AI systems that involve U.S. persons, require AI Risk Assessment Reports and AI
Impact Assessments to assess the privacy, civil liberties and civil rights implications foreach new qualifying AI system or significant system refresh.
SEC.___—PRIVACY, CIVIL RIGHTS AND CIVIL LIBERTIES RISK AND IMPACTASSESSMENTS FOR ARTIFICIAL INTELLIGENCE SYSTEMS.—(a) IN GENERAL.—The head of a covered agency shall conduct risk and impact
assessments of the privacy, civil rights, and civil liberties risks and potential implicationsof any covered artificial intelligence system utilized by the covered agency and takeappropriate steps to mitigate risks and adverse impact of any such system on the privacy,
civil rights, and civil liberties of U.S. persons.
(b) DEFINITIONS.—For purposes of this section—(1) COVERED ARTIFICIAL INTELLIGENCE SYSTEM.—A “covered artificial
intelligence system” means a qualified artificial intelligence system or a significant
artificial intelligence system refresh as determined by the task force established in
section [XX] of this Act that is—(A) designed to collect, process, maintain, or use information on
U.S. persons;(B) may inadvertently process, maintain, or use information on
U.S. persons; or(C) has a direct impact on U.S. persons.

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D R A F T L E G I S L AT I V E L A N G U A G E(2) COVERED AGENCY.—A “covered agency” includes—(A) the Department of Homeland Security;(B) the Federal Bureau of Investigation; and(C) each element of the Intelligence Community, as defined in
section 3003(4) of title 50, United States Code.
(3) HEAD OF A COVERED AGENCY.—The “head of a covered agency”
shall mean the Secretary of Homeland Security, the Director of the Federal Bureau
of Investigation and, for the Intelligence Community, the Director of National
Intelligence.
(c) REPORTS REQUIRED.—(1) ARTIFICIAL INTELLIGENCE SYSTEM RISK ASSESSMENT.—Before
acquiring or fielding a covered artificial intelligence system, each covered
agency shall conduct an Artificial Intelligence System Risk Assessment (“Risk
Assessment”). The Risk Assessment shall—(A) assess the potential implications of the covered artificial
intelligence system on freedom of expression, equal protection, privacy,
and due process;(B) account for the environment in which the covered artificial
intelligence system will be deployed, including its interactions with otherartificial intelligence tools, programs, and systems that collect personallyidentifiable information; and(C) include steps to mitigate and track any risks identified in theassessment.
(2) ARTIFICIAL INTELLIGENCE SYSTEM IMPACT ASSESSMENT.—Eachcovered agency shall conduct an Artificial Intelligence System Impact Assessment(“Impact Assessment”), no less than once per year, to assess the degree to whicha covered artificial intelligence system remains compliant with the constraints andmetrics established in the Risk Assessment. The Impact Assessment shall bebased on outcomes, impacts, and metrics collected during system use, and shalldetermine if the existing validation processes should be improved.
(d) NOTICE OF DISCONTINUATION.—Within one year of discontinuing use of
any non-public or classified covered artificial intelligence system, a covered agency shallp696

APPENDIX D

consider providing notice to the public that the covered artificial intelligence system has
been discontinued.
(e) REPORT TO CONGRESS.—The head of each covered agency shall, within
90 days of the date of this Act, submit to Congress a report identifying any additional
resources, including staff, needed to carry out the requirements of this section.
This section should be cross-referenced with the recommendation to create a task force
to assess the privacy and civil rights and civil liberties implications of AI and emerging
technologies, as the definition of a “covered artificial intelligence system” relies on the workof the task force.
Recommendation Set 2: Develop & Test Systems per Goals of Privacy Preservation and
Fairness.
Establish third-party testing center(s) to allow independent, third-party testing of national
security-related AI systems that could impact U.S. persons.
Require the Department of Justice (DOJ), in consultation with the Privacy and Civil Liberties
Oversight Board (PCLOB), to develop binding guidance for the use of third-party testing(e.g., thresholds for high-consequence systems or unprecedented factors) of AI systems.
SEC.___.—THIRD PARTY TESTING OF ARTIFICIAL INTELLIGENCE SYSTEMS.—(a) IN GENERAL.—Not later than one year after the date of enactment of thisAct, the Director of the National Institute of Standards and Technology shall establish
an accreditation program for Third Party Independent Artificial Intelligence Testing
Laboratories, as set forth in this section, to conduct independent testing of artificial
intelligence systems for covered agencies to assess potential privacy, civil rights, and civil
liberties impacts of such systems on U.S. persons.
(b) ARTIFICIAL INTELLIGENCE SYSTEMS REQUIRING TESTING.—The Privacy
and Civil Liberties Oversight Board and the Department of Justice shall, in consultation
with Privacy and Civil Liberties officers of the covered agencies, propose criteria for when
an artificial intelligence system warrants third-party testing for privacy, civil liberties, andcivil rights implications for U.S. Persons. Covered agencies shall adopt this criteria, as
described in subsection (e).
(c) COVERED AGENCIES.—For the purposes of this section, covered agencies
are the elements of the Intelligence Community (as defined in section 3003(4) of title 50,
United States Code, and coordinated by the Office of the Director of National Intelligence),
the Department of Homeland Security, and the Federal Bureau of Investigation.

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D R A F T L E G I S L AT I V E L A N G U A G E(d) ACCREDITATION OF THIRD PARTY ARTIFICIAL INTELLIGENCE TESTINGLABORATORIES.—Accreditation of Third Party Artificial Intelligence Testing Laboratories
shall be done through the National Institute of Standards and Technology’s National
Voluntary Laboratory Accreditation Program (“NVLAP”). In accordance with current
NVLAP processes, the National Institute of Standards and Technology shall determine
and maintain the authoritative list for approved Third Party Artificial Intelligence Testing
Laboratories.
(e) INDEPENDENT TESTING REQUIRED.—Upon the approval of Third Party
Artificial Intelligence Testing Laboratories as outlined in subsection (d), a covered agency,
prior to procuring or fielding an artificial intelligence system requiring testing, shall instituteindependent third party testing of the system to assess performance of the system
according to attributes listed in section 22A of the National Institute of Standards andTechnology Act.
(f) SCOPE OF TESTING.—Each independent Third Party Artificial Intelligence
Testing Laboratory accredited pursuant to subsection (d) shall—(1) utilize metrics relevant to the mission and authorities of the agency that
intends to field the artificial intelligence system;(2) develop approaches to test—(A) the software product, as installed in a test facility; and(B) relevant cloud-based services.
(3) establish binding data agreements that enable the agency and otherstakeholders to share confidential and proprietary data with the testing entity
without fear of inappropriate disclosure; and(4) collaborate with the covered agency that is seeking testing to reachconsensus on appropriate protocols and approaches for handling test data, test
results, and analyses.
Recommendation Set 4: Strengthen Oversight and Governance Mechanisms to Address
Current and Evolving Concerns.
Strengthen the Privacy and Civil Liberties Oversight Board’s (PCLOB) ability to provide
meaningful oversight and advice to the federal government’s use of AI-enabled technologies
for counterterrorism purposes.
SEC.___.—OVERSIGHT OF FEDERAL GOVERNMENT USE OF ARTIFICIALINTELLIGENCE-ENABLED SYSTEMS FOR COUNTERTERRORISM PURPOSES.—p
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APPENDIX D(a) AMENDMENTS TO AUTHORITIES AND RESPONSIBILITIES OF THE PRIVACY
AND CIVIL LIBERTIES OVERSIGHT BOARD.—Section 2000ee of title 42, United States
Code, is amended—(1) in paragraph (2) of subsection (d), by—(A) striking “and” at the end of subparagraph (B);(B) redesignating subparagraph (C) as subparagraph (D); and(C) adding a new subparagraph (C), as follows:“(C) the development and use of artificial intelligence-enabledtechnologies for counterterrorism purposes; and”; and(2) in subparagraph (1)(A) of subsection (g), by striking the semicolonand adding the following: “and information about artificial intelligence-enabledtechnologies proposed to be acquired or fielded in the Federal Government(such as documentation of data collection, disclosure and consent processes for
artificial intelligence-enabled tools and programs, documentation of models usedand supporting training and testing, and any repurposing);”(b) AMENDMENTS TO AUTHORITIES AND RESPONSIBILITIES OF PRIVACY
AND CIVIL LIBERTIES OFFICERS.—Section 2000ee-1 of title 42, United States Code, isamended—(1) in subsection (a), by—(5); and(A) redesignating paragraphs (3) and (4) as paragraphs (4) and(B) inserting a new paragraph (3), as follows:“(3) provide prior notice to the Privacy and Civil Liberties Oversight
Board of the fielding or repurposing of an artificial intelligence-enabled
system (including a classified system) that could have an impact on
privacy, civil liberties, or civil rights, and provide access to associatedimpact statements, including System of Record Notices, Privacy Impact
Assessments, and Civil Rights and Civil Liberties Impact Assessments;”and(2) in subsection (d), by striking the semicolon in paragraph (1) andinserting the following: “(including information described in paragraph (a)(3));”.

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D R A F T L E G I S L AT I V E L A N G U A G E(c) SELF-ASSESSMENT BY PRIVACY AND CIVIL LIBERTIES OVERSIGHT
BOARD.—Not later than 270 days after the date of the enactment of this act, the Privacy andCivil Liberties Oversight Board shall conduct and provide to Congress a self-assessmentof any change in resources and organizational structure that may be required to carry out
the artificial intelligence-related mission required by this section.
Empower DHS Offices of Privacy and Civil Rights and Civil Liberties.
SEC.___.—ENHANCED OVERSIGHT OF ARTIFICIAL INTELLIGENCE-ENABLED
SYSTEMS AT THE DEPARTMENT OF HOMELAND SECURITY.—(a) AMENDMENT TO DUTIES AND RESPONSIBILITIES OF CIVIL RIGHTS AND
CIVIL LIBERTIES OFFICER.—Section 345 of title 6, United States Code, is amended inparagraph (a)(5), by—(1) striking the final “and” in subparagraph (A);(2) redesignating subparagraph (B) as subparagraph (C); and(3) adding a new subparagraph (B), as follows:“(B) ensure that the legal and approval processes for the
procurement and use of artificial intelligence-enabled systems, including
associated data of machine learning systems, provide appropriate
consideration to the privacy, civil rights, and civil liberties impacts of such
systems; and”.
(b) AMENDMENT TO DUTIES AND RESPONSIBILITIES OF CHIEF PRIVACYOFFICER.—Section 142 of title 6, United States Code, is amended in paragraph (a)(5),by—(1) striking the final “and” in subparagraph (A);(2) redesignating subparagraph (B) as subparagraph (C); and(3) adding a new subparagraph (B), as follows:“(B) ensure that the legal and approval processes for the
procurement and use of artificial intelligence-enabled systems, including
associated data of machine learning systems, provide appropriate
consideration to the privacy, civil rights, and civil liberties impacts of such
systems; and”.

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APPENDIX D(c) ENHANCED PROCEDURES FOR CONSIDERATION OF PRIVACY AND CIVIL
LIBERTIES ISSUES.—Not later than 270 days after the date of the enactment of this Act—
(1) the Secretary of Homeland Security shall revise the legal and approval
processes for the procurement and use of artificial intelligence-enabled systems,
including associated data of machine learning systems, to ensure that fullconsideration is given, with the participation of the Department’s Chief Privacy
Officer and the Officer for Civil Rights and Civil Liberties, to the privacy, civil rights,
and civil liberties impacts of such systems; and(2) the Department’s Chief Privacy Officer and the Officer for Civil Rights
and Civil Liberties shall report to Congress on any additional staffing or funding
resources that may be required to carry out the requirements of this section.
Establish a task force to assess the privacy and civil rights and civil liberties implicationsof AI and emerging technologies.
SEC.___.—TASK FORCE ON ORGANIZATIONAL STRUCTURE FOR ARTIFICIALINTELLIGENCE GOVERNANCE AND OVERSIGHT.—(a) ESTABLISHMENT.—Not later than 90 days after the date of the enactmentof this Act, the President shall appoint a task force to assess the privacy, civil rights, andcivil liberties implications of artificial intelligence and emerging technologies. This includes
identifying policy and legal gaps and making recommendations to ensure that uses ofartificial intelligence and associated data in U.S. government operations comport withfreedom of expression, equal protection, privacy, and due process. The task force shall—(1) assess existing policy and legal gaps for current AI applications andemerging technologies, and make recommendations for—(A) legislative and regulatory reforms on the development andfielding of AI and emerging technologies; and(B) institutional changes to ensure sustained assessment
and recurring guidance on privacy and civil liberties implications of AI
applications and emerging technologies.
(b) MEMBERSHIP OF TASK FORCE.—(1) The task force shall include—(A) the Attorney General or his or her designee;(B) the Director of the Office of Management and Budget or his or
her designee;p701

D R A F T L E G I S L AT I V E L A N G U A G E(C) the Director of the National Institute of Standards andTechnology or his or her designee;(D) the Comptroller General or his or her designee;(E) the Inspectors General for the following agencies:(i) the Department of State;(ii) the Department of the Treasury;(iii) the Department of Defense;(iv) the Department of Justice;(v) the Department of Health and Human Services;(vii) the Department of Homeland Security;(viii) the Office of the Director of National Intelligence; and(ix) the Central Intelligence Agency.
(F) the chief privacy and civil liberties officers of each agency
described in subparagraph (E);(G) the Chair of the Privacy and Civil Liberties Oversight Board;(H) the Chair of the National Artificial Intelligence Advisory
Committee’s Subcommittee on Artificial Intelligence and Law Enforcement;
and(I) representatives from civil society, including organizational
leaders with expertise in technology, privacy, civil liberties, and civil rights,
representatives from industry, and representatives from academia, as
appointed by the President.
(2) TASK FORCE CHAIR AND VICE CHAIR.—The President shalldesignate a Chair and Vice Chair of the task force from among its members.
(c) RESPONSIBILITIES OF TASK FORCE.—The task force established pursuant tosubsection (a) shall—p702

APPENDIX D(1) conduct an assessment and make recommendations to Congress andto the President to ensure that the development and fielding of artificial intelligenceand other emerging technologies by the Federal Government provides protections
for the privacy, civil liberties, and civil rights of U.S. persons as appropriatelybalanced against critical law enforcement and national security needs;(2) issue criteria for identifying qualified artificial intelligence systems
and significant system refreshes requiring Artificial Intelligence Risk Assessment
Reports and Artificial Intelligence Impact Assessments, under section [XX] of thisAct;(3) recommend baseline standards for Federal Government use ofbiometric identification technologies, including, but not limited to, facial recognition,
voiceprint, gait recognition, and keyboard entry technologies;(4) recommend proposals to address any gaps in Federal law or regulation
with respect to facial recognition technologies in order to enhance protections ofprivacy, civil liberties, and civil rights of U.S. persons;(5) recommend best practices and contractual requirements to strengthen
protections for privacy, information security, fairness, non-discrimination,
auditability, and accountability in artificial intelligence systems and technologies
and associated data procured by the federal government;(6) consider updates to and reforms of government data privacy andretention requirements to address implications to privacy, civil liberties, and civilrights;(7) assess ongoing efforts to regulate commercial development and fieldingof artificial intelligence and associated data in light of privacy, civil liberties, andcivil rights implications, and as appropriate, consider and recommend institutionalor organizational changes to facilitate applicable regulation; and(8) assess the utility of establishing a new organization within the Federal
Government to provide ongoing governance for and oversight over the fieldingof artificial intelligence technologies by Federal agencies as technologicalcapabilities evolve over time.
(d) ORGANIZATIONAL CONSIDERATIONS.—In conducting the assessment
required by subsection (c)(7), the task force shall consider—(1) the organizational placement, structure, composition, authorities, andresources that a new organization would require to provide ongoing guidance andbaseline standards for—p703

D R A F T L E G I S L AT I V E L A N G U A G E(A) the Federal Government’s development, acquisition, andfielding of artificial intelligence systems to ensure they comport withprivacy, civil liberties, and civil rights and civil liberties law, to include
guardrails for their use and to disallow outcomes to be incorporated inpolicy and embedded in system development; and(B) providing transparency to oversight entities and the publicregarding the Federal Government’s use of artificial systems and theperformance of those systems. 						(2) the existing interagency and intra-agency efforts to address AI
oversight;(3) the need for and scope of national security carve outs, and any
limitations or protections that should be built into any such carve outs; and(4) the research, development, and application of new technologies tomitigate privacy and civil liberties risks inherent in artificial intelligence systems.
(e) REPORTING.—(1) Not later than 180 days of establishment, the task force shall issue
a report to Congress and the President with its legislative and regulatory
recommendations. The task force shall provide periodic updates to the President
and the Congress.
(2) Within a year of its establishment, the task force shall issue a report to the
President and the Congress with its assessment on organizational considerations,to include any recommendations for organizational changes.
CHAPTER 10: THE TALENT COMPETITION
Blueprint for Action
Recommendation: Pass a National Security Immigration Act.
1) Grant Green Cards to All Students Graduating with STEM PhDs from Accredited
American Universities.
2) Double the Number of Employment Based Green Cards.
3) Create an Entrepreneur Visa.
4) Create an Emerging and Disruptive Technology Visa.
NATIONAL SECURITY IMMIGRATION ACT OF 2021
SECTION. 1.—SHORT TITLE.—This Act may be cited as the “National Security
Immigration Act of 2021.”

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SEC. 2.—GREEN CARDS FOR STUDENTS GRADUATING FROM ACCREDITEDAMERICAN UNIVERSITIES WITH DOCTORATES IN THE FIELDS OF SCIENCE,TECHNOLOGY, ENGINEERING, AND MATHEMATICS.—Section 1151 of title 8, UnitedStates Code, is amended in subsection (b)(1), by adding a new subparagraph (F), as
follows:“(F) Aliens who have been awarded doctoral degrees in the fields of science,technology, engineering, and mathematics by accredited universities in the United States.”
SEC.
3.—INCREASEDAUTHORIZATIONFOREMPLOYMENT-BASEDIMMIGRATION.—Section 1151 of title 8, United States Code, as amended by section 2,
is further amended in subsection (d)(1)(A) by striking “140,000” and inserting “280,000”.
SEC. 4.—ENTREPRENEUR VISAS FOR HIGH PRIORITY SCIENCE AND TECHNOLOGYFIELDS AS DETERMINED BY NATIONAL SCIENCE FOUNDATION.—Section 1153 of
title 8, United States Code, is amended in subsection (b)(5)—(1) By redesignating subparagraphs (C) and (D) as subparagraphs (D) and (E);and(2) By adding a new subparagraph (C), as follows:		“(C) PRIORITY FOR ENTREPRENEURS IN CERTAIN SCIENCEAND TECHNOLOGY FIELDS.—		“(i) Priority under this section shall be given to qualified immigrants
who engage in new commercial enterprises in high priority science and technology
fields, including artificial intelligence-enabled technology fields, as determined by
the National Science Foundation.
“(ii) A qualified immigrant under this paragraph section shall not be
required to meet the capital investment requirement in clause (A)(i) if the qualified
immigrant is one of the principal organizers and operators of a new commercial
enterprise described in clause (i).”SEC. 5.—VISA FOR EMERGING AND DISRUPTIVE TECHNOLOGIES.—Section 1151
of title 8, United States Code, as amended by Sections 2 and 3, is further amended insubsection (b)(1), by adding a new clause (G), as follows:“(G) Aliens who are students, researchers, entrepreneurs, and technologists incritical emerging and disruptive technology fields, as determined by the National Science
Foundation.”
SEC. 6.—DETERMINATIONS BY THE NATIONAL SCIENCE FOUNDATION.—Not later
than 180 days after the date of the enactment of this Act, and every three years thereafter,
the National Science Foundation shall publish a list of—p
705

D R A F T L E G I S L AT I V E L A N G U A G E(1) high priority science and technology fields in which qualified immigrants will
be eligible for consideration for entrepreneur visas under section 1153(b)(5)(C) of title 8,
United States Code, as amended; and(2) critical emerging and disruptive technology fields in which qualified immigrants
will be eligible for consideration for student, researcher, and entrepreneur visas under
section 1151(b)(1)(G) of title 8, United States Code, as amended.
CHAPTER 11: ACCELERATING AI INNOVATION
Blueprint for Action
Recommendation: Scale and Coordinate Federal AI R&D Funding.
Component 1: Establish a National Technology Foundation.
THE NATIONAL TECHNOLOGY FOUNDATION ACT OF 2021
SECTION 1.—SHORT TITLE.—This Act may be cited as the “National Technology
Foundation Act of 2021.”
SEC. 2.—ESTABLISHMENT OF NATIONAL TECHNOLOGY FOUNDATION.—There isestablished in the executive branch of the Government an independent agency to be
known as the National Technology Foundation (hereinafter referred to as the “Foundation”).
The Foundation shall consist of a National Technology Board (hereinafter referred to as the“Board”) and a Director of the Foundation (hereinafter referred to as the “Director”).
SEC. 3.—NATIONAL TECHNOLOGY BOARD.—(a) The Board shall consist of twenty-four members to be appointed by the President
and of the Director ex officio. In addition to any powers and functions otherwise granted
to it by this chapter, the Board shall establish the policies of the Foundation, within theframework of applicable national policies as set forth by the President and the Congress.
(b) The term of office of each member of the Board shall be six years; exceptthat any member appointed to fill a vacancy occurring prior to the expiration of the term
for which his predecessor was appointed shall be appointed for the remainder of suchterm. Any person, other than the Director, who has been a member of the Board for twelve
consecutive years shall thereafter be ineligible for appointment during the two-year period
following the expiration of such twelfth year.
SEC. 4.—DIRECTOR OF THE FOUNDATION.—The Director shall be appointed by the
President, by and with the advice and consent of the Senate. Before any person is appointed
as Director, the President shall afford the Board an opportunity to make recommendations
to the President with respect to such appointment. The Director shall receive basic payat the rate provided for level II of the Executive Schedule under Section 5313 of title 5,p
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United States Code, and shall serve for a term of six years unless sooner removed by the
President.
SEC. 5.—DEPUTY DIRECTOR OF THE FOUNDATION.—The Deputy Director (hereinafterreferred to as the “Deputy Director”) shall be appointed by the President, by and with theadvice and consent of the Senate. Before any person is appointed as a Deputy Director, thePresident shall afford the Board and the Director an opportunity to make recommendations
to the President with respect to such appointment. The Deputy Director shall receive basic
pay at the rate provided for level III of the Executive Schedule under section 5314 of title
5, United States Code, and shall perform such duties and exercise such powers as the
Director may prescribe. The Deputy Director shall act for, and exercise the powers of, theDirector during the absence or disability of the Director, or in the event of a vacancy in theoffice of Director.
SEC. 6.—GENERAL AUTHORITY OF THE FOUNDATION.—(a) The Foundation shall have the authority, within the limits of available
appropriations, to do all things necessary to carry out the provisions of this chapter,
including, but without being limited thereto, to—(1) distribute other payments for research and development in priority
technology areas through grants, cooperative agreements, and contracts awarded
to academic and private sector researchers, nonprofits, and consortia throughcompetitive processes without regard to the provisions of sections 3324(a) and (b)of title 31, United States Code;(2) establish an innovation unit in which independent program managers,
brought into the Foundation on the basis of term appointments, fund proposals
from both industry and academia to advance solutions to forward-looking researchquestions in priority technology areas;(3) organize prize competitions to catalyze research around significant
technology challenge problems;(4) manage national technology resources, infrastructure, and initiatives
that are assigned to the Foundation by statute or executive order;(5) promote the commercialization of new technologies in priority
technology areas and the transfer of such technologies to Federal, State and local
government entities; and(6) serve as a focal point for international research and development
collaboration and standards-setting dialogues in priority technology areas.

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SEC. 7.—PRIORITY TECHNOLOGY AREAS.—(a) CORE DIRECTORATES.—The Foundation shall be organized into a set of coredirectorates, each dedicated to advancing fundamental research into a priority technology
area.
(b) PRIORITY TECHNOLOGY AREAS.—Priority technology areas shall include—(1) artificial intelligence;(2) biotechnology;(3) quantum computing;(4) semiconductors and advanced hardware;(5) robotics and autonomy;(6) fifth-generation and advanced networking;(7) advanced manufacturing;(8) energy technology; and(9) any other technology area designated by the Congress or the Board.
(c) REVIEW OF KEY TECHNOLOGY FOCUS AREAS AND SUBSEQUENT LISTS.—(1) ADDING OR DELETING KEY TECHNOLOGY FOCUS AREAS.—
Beginning on the date that is four years after the date of enactment of this Act andevery four years thereafter, the Director, acting through the Deputy Director shall—(A) review the list of key technology focus areas, in consultation
with the Board; and(B) as part of that review, may add or delete key technology focus
areas if the competitive threats to the United States have shifted andwhether the United States or other nations have advanced or fallen behindin a technological area.
(2) LIMIT ON KEY TECHNOLOGY FOCUS AREAS.—Not more than ten
key technology focus areas shall be included on the list of key technology focus
areas at any time.

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APPENDIX D(3) UPDATING FOCUS AREAS AND DISTRIBUTION.—Upon thecompletion of each review under this subsection, the Director shall make the listof key technology focus areas readily available and publish the list in the Federal
Register, even if no changes have been made to the prior list.
SEC. 8.—ADMINISTRATIVE MATTERS.—(a) HIRING AUTHORITY.—(1) PRIORITY TECHNOLOGY EXPERTS.—The Director shall have theauthority to carry out a program of personnel management authority for theFoundation in the same manner, and subject to the same requirements, as the
program of personnel management authority authorized for the Director of the
Defense Advanced Research Projects Agency under section 1599h(a)(2) of title
10, United States Code, for the Defense Advanced Research Projects Agency.
(2) HIGHLY QUALIFIED EXPERTS.—In addition to the authority provided
under subsection (A), the Director shall have the authority to carry out a programof personnel management authority for the Foundation in the same manner, andsubject to the same requirements, as the program to attract highly qualified experts
carried out by the Secretary of Defense under section 9903 of title 5, United States
Code.
(3) ADDITIONAL HIRING AUTHORITY.—To the extent needed to carryout the duties of the Foundation, the Director shall utilize hiring authorities under
section 3372 of title 5, United States Code, to staff the Foundation with employees
from other Federal agencies, State and local governments, Indian tribes andtribal organizations, institutions of higher education, and other organizations, as
described in that section, in the same manner and subject to the same conditions.
(b) EMPLOYMENT AND COMPENSATION OF CERTAIN PERSONNEL.—(1) PROGRAM MANAGERS.—The employees of the Foundation may
include program managers, who shall perform a role similar to program managers
employed by the Defense Advanced Research Projects Agency, for the oversightand selection of programs supported by the Foundation.
(2) COMPENSATION OF MEMBERS OF BOARD.—The members of theBoard shall be entitled to receive compensation for each day engaged in thebusiness of the Foundation at a rate fixed by the Chairman but not exceeding
the maximum rate payable under section 5376 of title 5, United States Code, andshall be allowed travel expenses as authorized by 5703 of title 5, United States
Code. For the purposes of determining the payment of compensation under thissubsection, the time spent in travel by any member of the Board shall be deemed
as time engaged in the business of the Foundation. Members of the Board andp709

D R A F T L E G I S L AT I V E L A N G U A G E

members of special commissions may waive compensation and reimbursement
for traveling expenses.
SEC. 9.—INTERNATIONAL COOPERATION.—(a) INTERNATIONAL AUTHORITY.—The Foundation is authorized to cooperate inany international technology activities consistent with the purposes of this Act and to expend
for such international technology activities such sums within the limit of appropriated funds
as the Foundation may deem appropriate.
(b) CONTRACTS AND ARRANGEMENTS.—(1) The authority to enter into contracts or other arrangements withorganizations or individuals in foreign countries and with agencies of foreigncountries, as provided in section 1870(c) of title 42, United States Code, andthe authority to cooperate in international scientific or engineering activities asprovided in subsection (a) of this section, shall be exercised only with the approvalof the Secretary of State, to the end that such authority shall be exercised in suchmanner as is consistent with the foreign policy objectives of the United States.
(2) If, in the exercise of the authority referred to in paragraph (1) of thissubsection, negotiation with foreign countries or agencies thereof becomes
necessary, such negotiation shall be carried on by the Secretary of State in
consultation with the Director.
SEC. 10.—SECURITY PROVISIONS.—(a) RESEARCH RELATED TO NUCLEAR ENERGY.— The Foundation shall notsupport any research or development activity in the field of nuclear energy, nor shall it
exercise any authority pursuant to section 1870(e) of title 42, United States Code, in respect
to that field, without first having obtained the concurrence of the Secretary of Energy thatsuch activity will not adversely affect the common defense and security. To the extent
that such activity involves restricted data as defined in the Atomic Energy Act of 1954,
the provisions of that Act regarding the control of the dissemination of restricted data andthe security clearance of those individuals to be given access to restricted data shall beapplicable. Nothing in this chapter shall supersede or modify any provision of the Atomic
Energy Act of 1954.
(b) RESEARCH RELATION TO NATIONAL DEFENSE.—(1) In the case of priority technology area research activities under thisAct in connection with matters relating to the national defense, the Secretary of
Defense shall establish such security requirements and safeguards, including
restrictions with respect to access to information and property, as the Secretary of
Defense deems necessary.

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APPENDIX D(2) Any agency of the Government exercising investigatory functions
otherwise within its jurisdiction is authorized to make such investigations andreports as may be requested by the Foundation in connection with the enforcementof security requirements and safeguards, including restrictions with respect
to access to information and property, established under paragraph (1) of thissubsection.
SEC. 11.—REPORTS.—(a) INITIAL REPORT.—Not later than one year after the date of enactment of thisAct, the Director shall transmit to the Committee on Commerce, Science, and Transportationof the Senate and the Committee on Science, Space, and Technology of the House ofRepresentatives a report regarding the establishment of the Foundation. The report shall
include an assessment of the priority technology focus areas as defined in this Act and of
authorities that conflict with the National Science Foundation.
(b) ANNUAL REPORTS.—(1) The Board shall submit to the President and the Congress no later thanJanuary 15 of each even numbered year, a report on indicators of the state of thepriority technology areas in the United States, as defined in this Act.
(2) The Board shall render to the President and the Congress reports
on specific, individual policy matters within the authority of the Foundation (orotherwise as requested by the Congress or the President) related to priority
technology areas, as the Board, the President, or the Congress determines theneed for such reports.
SEC. 12.—AUTHORIZATION OF APPROPRIATIONS.—(a) INITIAL APPROPRIATION.—To enable the Foundation to carry out its powers
and duties, including the establishment of a physical location, there is authorized to beappropriated to the Foundation $30,000,000 for the first fiscal year following the enactmentof this Act. Appropriations made pursuant to the authority provided in this subsection
shall remain available for obligation, for expenditure, or for obligation and expenditure until
expended for the Foundation’s initial administrative costs and salaries and expenses.
(b) ANNUAL APPROPRIATION.—There are authorized to be appropriated for theFoundation, in addition to the appropriation provided in subsection (a) of this section andany other funds made available to the Foundation, a total of $51,000,000,000 for fiscal
years 2022 through 2026, of which—(A) $1,000,000,000 is authorized for fiscal year 2022;(B) $5,000,000,000 is authorized for fiscal year 2023;

p
711

D R A F T L E G I S L AT I V E L A N G U A G E(C) $10,000,000,000 is authorized for fiscal year 2024;(D) $15,000,000,000 is authorized for fiscal year 2025; and(E) $20,000,000,000 is authorized for fiscal year 2026.
The Commission acknowledges additional authorities may be required to establish the
NTF, including administrative, financial, and educational authorities mirroring those of
the National Science Foundation, and that amendments to the NSF’s statutory authorities
may be required to alleviate duplication of duties. The Commission is ready to work with
Congress to address such provisions.
Component 4: Invest in Talent that Will Transform the Field.
Direct and fund establishment of an AI Innovator Award.
Direct and fund establishment of a team-based AI research award.
SEC. ___.—ARTIFICIAL INTELLIGENCE AWARD PROGRAM.—(a) ARTIFICIAL INTELLIGENCE INNOVATOR AWARD.—(1) IN GENERAL.—The Director of the National Science Foundation shallpartner with a nonprofit organization as described in subsection (c) to establish
an Artificial Intelligence Innovator Award program to recognize and support the
research of leaders in the field of artificial intelligence.
(2) ARTIFICIAL INTELLIGENCE INNOVATOR AWARD RECIPIENTS.—
The Artificial Intelligence Award Selection Committee as described in subsection(d) shall select no fewer than 10 and no more than 20 award recipients each year.
Recipients shall be selected for five-year, renewable award terms, based on a
proven track record of prior innovation, a proposed general research program,a commitment to spend 75 percent of the recipients’ time on research, and thecommittee’s assessment of the potential of the research to generate breakthroughsin the area of artificial intelligence. Award amounts shall be determined by theselection committee with the objective of covering the full salary and benefits of
the researcher and the cost of associated support staff and research equipment.
(b) ARTIFICIAL INTELLIGENCE TEAM AWARD.—(1) IN GENERAL.—The Director of the National Science Foundation shallpartner with a nonprofit organization as described in subsection (c) to establish
an Artificial Intelligence Team Award program to support interdisciplinary research
directed at applying artificial intelligence to solve complex problems or pursuinguse-inspired basic research efforts to advance a fundamental understanding of
the science of artificial intelligence in a manner that provides a significant benefit
to society.
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APPENDIX D(2) ARTIFICIAL INTELLIGENCE TEAM AWARD RECIPIENTS.—The
Artificial Intelligence Innovator Awards Selection Committee as described inparagraph (d) shall select no fewer than five and no more than 10 team recipientseach year. Recipients shall be selected for five-year, nonrenewable terms,
based on team qualifications, commitment to multi-disciplinary approaches,
and innovative research proposals. Award amounts shall be determined by theselection committee with the objective of covering the cost of carrying out the
proposed research proposal.
(c) NONPROFIT ORGANIZATION PARTNER.—The National Science Foundation
shall partner with a nonprofit organization active in the field of computer science andartificial intelligence that maintains the requisite expertise and connections to the artificial
intelligence research community to identify promising talent and invest in innovative ideas
and to manage the award programs described in subsections (a) and (b), including toadminister the programs and arrange the annual meeting.
(d) ARTIFICIAL INTELLIGENCE AWARD SELECTION COMMITTEE.—Recipientsof the Artificial Intelligence Innovator Award and the Artificial Intelligence Team Award shallbe selected by a rotating committee of artificial intelligence experts known as the Artificial
Intelligence Award Selection Committee. The Committee shall consist of members chosen
for their first-hand experience in artificial intelligence research and their familiarity withthe frontiers of the field. Committee member selection shall be made by the nonprofit
organization partner identified under subsection (c), in consultation with the Director of the
National Science Foundation or designee.
(e) ANNUAL MEETING.—The Director of the National Science Foundation shallsponsor an annual meeting of recipients of the Artificial Intelligence Innovator Award andthe Artificial Intelligence Team Award, at which the award recipients shall share informationon the progress of their work.
(f) OTHER SOURCES OF FUNDING.—Nothing in this section shall be interpreted topreclude a recipient of an Artificial Intelligence Innovator Award or an Artificial Intelligence
Team Award from pursuing supplemental government research grant or other researchsupport provided by individuals, nonprofits and corporations, provided that such additional
funding does not interfere with the recipient’s commitment to the research program or
require the assignment of ownership of intellectual property in a manner that would beinconsistent with the provisions of the Bayh-Dole Act, Public Law 96-517.
(g) INDEPENDENT REVIEW.—The Director of the National Science Foundation
shall engage an independent entity to conduct a review to assess the successes andfailures of the awards program authorized by this section, evaluate the impact of the funding
level and award term on the research conducted by participants, and recommend any
needed changes to the program (including any expansion or contraction in the number ofp713

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awards). The findings of the independent review shall be delivered to Congress not later
than seven years after the commencement of the program.
(h) AUTHORIZATION OF APPROPRIATION.—(1) There is authorized to be appropriated for each of the fiscal years 2022
through 2028 $125,000,000 for the Artificial Intelligence Innovator Award.
(2) There is authorized to be appropriated for the Artificial Intelligence
Team Award—(A) $50,000,000 for fiscal year 2022;(B) $100,000,000 for fiscal year 2023;(C) $150,000,000 for fiscal year 2024;(D) $200,000,000 for fiscal year 2025; and(E) $250,000,000 for fiscal years 2026 through 2028.
Recommendation: Leverage Both Sides of the Public-Private Partnership.
Component 2: Form a Network of Regional Innovation Clusters Focused on Strategic
Emerging Technologies.
SEC. ___.—ESTABLISHMENT OF A NATIONAL NETWORK FOR REGIONALINNOVATION IN EMERGING TECHNOLOGIES.—(a) ESTABLISHMENT OF NATIONAL PROGRAM OFFICE.—The Secretary ofCommerce shall establish, within the National Institute of Standards and Technology, a
National Program Office for Regional Innovation in Emerging Technologies (referred to inthis section as the ‘National Program Office’).
(b) DUTIES AND RESPONSIBILITIES.—The National Program Office, incoordination with representatives of Federal agencies with experience in and missions
related to emerging technologies, shall—(1) oversee the planning, development, management, and coordination of
a National Network for Regional Innovation in Emerging Technologies (referred toin this section as the “National Network”);(2) develop, not later than one year after the date of enactment, and update
not less frequently than once every three years thereafter, a strategic plan toguide the development of the National Network to include identification of priority
emerging technologies critical to national security or national competitiveness;p714

APPENDIX D(3) use a competitive process to designate and provide financial assistanceto regional innovation clusters that enable United States leadership in emerging
technologies and support regional economic development throughout the UnitedStates;(4) establish within each regional innovation cluster in the National Networka Technology Research Center for the purpose of facilitating collaboration among
regional innovation cluster participants;(5) establish such procedures, processes, and criteria as may benecessary and appropriate to coordinate the activities of the National Network andto maximize participation in and coordination with the National Network by Federal
agencies that field or operate systems that incorporate emerging technologies;(6) establish a clearinghouse of public information related to the activitiesof the National Network; and(7) act as a convener of the National Network.
(c) DESIGNATION OF AND FINANCIAL ASSISTANCE IN SUPPORT OF REGIONALINNOVATION CLUSTERS.—The National Program Office shall use a competitive process
to designate and provide financial assistance to regional innovation clusters based on thefollowing criteria:(1) the equitable distribution of regional innovation clusters throughout theUnited States, taking into account factors such as proximity to the research anddevelopment facilities of Federal agencies, the level of support from state andlocal governments, the presence of and value proposition for leading firms andresearch institutions in relevant fields, and the size and education level of the local
workforce;(2) the capacity of regional innovation clusters to support the research,development, and commercialization of specific emerging technologies in areasthat are critical to United States national competitiveness; and(3) the clear potential for future development of regional innovation clusters
that are not yet established technology hubs.
(d) TECHNOLOGY RESEARCH CENTERS.—The National Program Office shallestablish within each regional innovation cluster in the National Network a Technology
Research Center for the purpose of facilitating collaboration between regional innovation
cluster participants. The Technology Research Centers shall—(1) form sustained partnerships with anchor institutions in the region;p715

D R A F T L E G I S L AT I V E L A N G U A G E(2) host researchers on temporary assignments from Federal agencies,
establish talent exchanges with local firms and research institutions, and fundmulti-year, post-doctoral fellowships for the commercialization of research;(3) host program managers from Federal agencies responsible fortransitioning basic research into commercially viable technologies, identifying
national security use cases and end users within the Federal Government, andinitiating new Federal Government contracts to support technology transition;(4) facilitate low cost access by regional innovation cluster participants
to computing resources, curated datasets, testing infrastructure and ranges,
and other research and development facilities owned or operated by the Federal
government;(5) establish intellectual property sharing agreements with regionalinnovation cluster participants to encourage Federal government adoption ofcommercial technologies; and(6) when appropriate, provide for the publication of research in the opensource domain to encourage advances in the science and technology community
more broadly.
(e) OTHER MATTERS.—(1) RECOMMENDATIONS.—In developing and updating the strategic planunder subsection (b)(2), the National Program Office shall solicit recommendations
and advice from a wide range of stakeholders, including industry, small andmedium-sized enterprises, research universities, community colleges, state andlocal elected officials, and other relevant organizations and institutions on an
ongoing basis.
(2) REPORT TO CONGRESS.—Upon completion of the strategic planrequired by subsection (b)(2) or an update thereof, the National Program Officeshall transmit the strategic plan to the Committee on Commerce, Science,
and Transportation of the Senate and the Committee on Science, Space, andTechnology of the House of Representatives.
(3) DETAILEES.—Any Federal Government employee may be detailed to
the National Program Office without reimbursement. Such detail shall be without
interruption or loss of civil service status or privilege.

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APPENDIX D(f) DEFINITIONS.—(1) REGIONAL INNOVATION CLUSTER.—The term “regional innovation
cluster” means a geographically bounded network of similar, synergistic, orcomplementary entities that —(A) are engaged in or with a particular industry sector and its
related sectors;(B) have active channels for business transactions andcommunication;and(C) share specialized infrastructure, labor markets, and services;(D) leverage the region’s unique competitive strengths to stimulateinnovation and create jobs.
(2) EMERGING TECHNOLOGIES.—For the purposes of this section
the term “emerging technologies” may include such technologies as artificial
intelligence, microelectronics, quantum computing, biotechnology, any associated,enabling or successor technologies, or any technologies identified by the National
Program Office to be critical to national security or national competitiveness.
(g) AUTHORIZATION OF APPROPRIATION.—There is authorized to beappropriated to the Secretary of Commerce to carry out this section $5,000,000 for fiscal
year 2022.
CHAPTER 14: TECHNOLOGY PROTECTION
Blueprint for Action
Recommendation: Reform CFIUS for Emerging Technology Competition.
Amend CFIUS’ authorizing legislation to require competitors to disclose investments in“sensitive technologies” to CFIUS.
SEC. ___. REVIEW OF SENSITIVE TRANSACTIONS INVOLVING COUNTRIES OF
SPECIAL CONCERN.
(a) TECHNICAL AMENDMENTS.—Section 721(a) of the Defense Production Act
of 1950 (50 USC 4565(a)) is amended by redesignating paragraphs (4), (5), (6), (7), (8), (9),(10), (11), (12), and (13) as paragraphs (5), (6), (7), (9), (10), (11), (12), (13), (15), and (16),respectively.

p717

D R A F T L E G I S L AT I V E L A N G U A G E(b) DEFINITION OF COUNTRY OF SPECIAL CONCERN.—Section 721(a) of the
Defense Production Act of 1950 (50 USC 4565(a)) is amended by inserting after paragraph(3) the following:“(4) COUNTRY OF SPECIAL CONCERN.—The term “country of special
concern” means any country that is—“(A) subject to export restrictions pursuant to section 744.21 of
title 15, Code of Federal Regulations;“(B) determined by the Secretary of State to be a state sponsor of
terrorism; or“(C) determined by the Committee to have a demonstrated or
declared strategic goal of acquiring a type of technology or infrastructurethat would have an adverse impact on United States leadership in areas
related to national security, and is specified in regulations prescribed by
the Committee.”(c) DEFINITION OF SENSITIVE TECHNOLOGY.—Section 721(a) of the Defense
Production Act of 1950 (50 USC 4565(a)) is amended by inserting after redesignatedparagraph (7) the following:“(8) SENSITIVE TECHNOLOGY.—The term ‘sensitive technology’ means
any technology that is determined by the Committee to be necessary for maintaining
or increasing the technological advantage of the United States over countries of
special concern with respect to national defense, intelligence, or other areas ofnational security, or gaining such an advantage over such countries with respect
to national defense, intelligence, or other areas of national security in areas wheresuch an advantage may not exist, and is not a critical technology as defined inparagraph (7) of this subsection, and is specified in regulations prescribed by theCommittee.
(d) DEFINITION OF SENSITIVE TRANSACTION INVOLVING A COUNTRY OF
SPECIAL CONCERN.—Section 721(a) of the Defense Production Act of 1950 (50 USC
4565(a)) is amended by inserting after redesignated paragraph (13) the following:“(14) SENSITIVE TRANSACTION INVOLVING A COUNTRY OF SPECIAL
CONCERN.—The term ‘sensitive transaction involving a country of special
concern’ means any investment in an unaffiliated United States business by aforeign person that—“(A) is—p718

APPENDIX D“(i) a national or a government of, or a foreign entity organizedunder the laws of, a country of special concern; or“(ii) a foreign entity—“(I) over which control is exercised or exercisable by anational or a government of, or by a foreign entity organized under
the laws of, a country of special concern; or“(II) in which the government of a country of special
concern has a substantial interest; and“(B) as a result of the transaction, could achieve–—“(i) influence, other than through voting of shares, on substantive
decision making of the United States business regarding the use,development, acquisition, or release of sensitive technologies, as definedin this section; or—“(ii) access to material nonpublic technical information related tosensitive technologies, as defined in this section, in the possession of theUnited States business.”(e) DEFINITION OF COVERED TRANSACTIONS.—Section 721(a) of the Defense
Production Act of 1950 (50 USC 4565(a)) is amended—(1) in redesignated paragraph (5)(B)—(A) in clause (iv)(I), by striking “or”;(B) in clause (iv)(II), by striking the period and inserting “; or”; and(C) by adding at the end the following:“(III) a sensitive transaction involving a country of special
concern.”(2) by redesignating clause (v) as clause (vi) and inserting after clause (iv)the following:“(v) Any sensitive transaction involving a country of special
concern.”p719

D R A F T L E G I S L AT I V E L A N G U A G E(f) INFORMATION REQUIRED IN ANNUAL REPORT TO CONGRESS.—Section
721(m)(2) of the Defense Production Act of 1950 (50 USC 4565(m)(2)) is amended byadding at the end the following:“(L) Identification of each country designated as a country of
special concern along with an explanation of the rationale for suchdesignation.
“(M) Identification of each technology designated as a sensitivetechnology along with an explanation of the rationale for such designation.”(g) MANDATORY DECLARATIONS.—Section 721(b)(1)(C)(v)(IV)(bb)(AA) of the
Defense Production Act of 1950 (50 USC 4565(b)(1)(C)(v)(IV)(bb)(AA)) is amended byinserting before the period “or is a sensitive transaction involving a country of special
concern”.
(h) CONFORMING AMENDMENTS.—Title 50, United States Code, is amended—(1) in section 4817(a)(1)(B) by striking “section 4565(a)(6)(A)” and inserting“section 4565(a)(7)(A)”;(2) in section 4565(b)(4)(B)(ii) (section 721(b)(4)(B)(ii) of the Defense
Production Act of 1950) by striking “subsection (a)(4)(B)(ii)” and inserting“subsection (a)(5)(B)(ii)”;(3) in section 4565(b)(1)(c)(v)(III)(bb)(AA) (section 721(b)(1)(c)(v)(III)(bb)(AA) of the Defense Production Act of 1950) by striking “subsection (a)(4)(B) (iii)”
and inserting “subsection (a)(5)(B)(iii)”;(4) in section 4565(b)(1)(c)(v)(III)(bb)(BB) (section 721(b)(1)(c)(v)(III)(bb)(BB) of the Defense Production Act of 1950) by striking “subsection (a)(4)(B)(iii)”
and inserting “subsection (a)(5)(B)(iii)”;(5) in section 4565(b)(1)(c)(v)(III)(cc) (section 721(b)(1)(c)(v)(III)(bb) (BB) of
the Defense Production Act of 1950) by striking “subsection (a)(4)(B)(iii)(II)” andinserting “subsection (a)(5)(B)(iii)(II)”.
Recommendation: Build Capacity to Protect the Integrity of the U.S. Research Environment.
Establish a government-sponsored independent entity focused on research integrity.
SEC. ___.—Establishment of University Affiliated Research Center Focused on
Research Integrity.—(a) AGREEMENT AUTHORIZED.—Not later than 180 days after the date of thep
720

APPENDIX D

enactment of this Act, the Secretary of Defense, acting through the Under Secretary of
Defense for Research and Engineering and in consultation with the Director of the Officeof Science and Technology Policy and other appropriate members of the Federal researchcommunity, shall enter into an agreement with a college or university to establish a
University Affiliated Research Center to act as a center of excellence on research integrityand provide information and advice on research security.
(b) RESEARCH PURPOSES.—The University Affiliated Research Centerestablished pursuant to subsection (a) shall—(1) Maintain open source materials to serve university vetting of international
engagement and risk management, including databases and risk assessment
tools;(2) Provide tailored guidance to research organizations for decision
support on matters related to research security and integrity;(3) Conduct comprehensive studies and regular reports on the state offoreign influence on U.S. research;(4) Undertake independent investigations on research integrity;(5) Develop education materials and tools for U.S. universities to build
annual training and compliance initiatives; and(6) Manage dialogue with stakeholder communities and provide a venue
for information sharing among research organizations and Federal agencies.
Recommendation: Counter Foreign Talent Recruitment Programs.
Mandate and resource compliance operations.
SEC. ___.—Enhanced Review of Risk Posed by Applicants for Federal Grants.—(a) ENHANCED REVIEW REQUIRED.—Not later than 180 days after the date of
the enactment of this Act, the Director of the Office of Management and Budget shallrevise section 200.206 of Part 2 of the Code of Federal Regulations to ensure that Federal
grant-making agencies maintain compliance operations to guard against malign foreign
talent recruitment programs and to prescribe standardized disclosure and accountability
measures to support such compliance operations.
(b) DEFINITION.—For the purposes of this section, a “malign foreign talent
recruitment program” is an effort directly or indirectly organized, managed, or funded by aforeign government to recruit science and technology professionals or students (regardlessof citizenship or national origin) engaged in research funded by a federal agency to share
information with or otherwise act on behalf of such foreign government.

p721

D R A F T L E G I S L AT I V E L A N G U A G E

Amend the Foreign Agents Registration Act.
SEC. ___.—AMENDMENT TO FOREIGN AGENTS REGISTRATION ACT. —Section 611
of title 22, United States Code, is amended in paragraph (1) of subsection (c) by—(1) Striking “and” at the end of clause (iv); and(2) Inserting at the end a new clause (v), as follows:“(v) directly or indirectly organizes, manages, or funds an effort to recruit
science and technology professionals or students (regardless of citizenshipor national origin) engaged in research funded by a Federal agency to share
information with or otherwise act on behalf of a foreign government; and”.
CHAPTER 15: A FAVORABLE INTERNATIONAL TECHNOLOGY ORDER
Blueprint for Action
Recommendation: Develop and Implement a Comprehensive U.S. National Plan to Support
International Technology Efforts.
Core Goal #1: Shape International Technical Standards.
Establish a grant program to enable small- and medium-sized U.S. AI companies toparticipate in international standardization efforts.
SEC. ___.—SUPPORT FOR INDUSTRY PARTICIPATION IN INTERNATIONAL
STANDARDS ORGANIZATIONS.—(a) IN GENERAL.—Not later than 180 days after the date of the enactment of
this Act, the Administrator of the Small Business Administration shall establish a program
to support participation by small business concerns in meetings and proceedings ofinternational standards organizations in the development of voluntary technical standards.
(b) GRANTS AUTHORIZED.—In carrying out the program authorized bysubsection (a), the Administrator shall award competitive, merit-reviewed grants, to small
business concerns to cover the reasonable costs, up to a specified ceiling, of participationof employees of such businesses in meetings and proceedings of international standards
organizations. Participation may include regularly attending meetings, contributing
expertise and research, proposing new work items, volunteering for leadership roles suchas convenors and editors, and being early adopters of emerging standards. Recipients ofawards under this subsection shall not be required to provide a matching contribution.
(c) AWARD CRITERIA.—The Administrator may provide under this section a grantaward to covered entities that:(1) demonstrate deep technical expertise in key emerging technologies,
including Artificial Intelligence and related technologies;p722

APPENDIX D(2) commit personnel with such expertise to regular participation ininternational bodies responsible for setting standards for such technologies overthe period of the grant; and(3) agree to participate in efforts to coordinate between the U.S.
government and industry to ensure protection of national security interests in thesetting of international standards.
(d) EVALUATION.—In issuing awards under this section, the Administrator shallcoordinate with the Director of the National Institute of Standards and Technology who
shall provide support in the assessment of technical expertise in emerging technologies
and standards setting needs.
(e) DEFINITIONS.—In this section:(1) ADMINISTRATOR.—The term “Administrator” means the Administratorof the Small Business Administration.
(2) COVERED ENTITY.—The term “covered entity” means a small businessconcern that is incorporated in and maintains a primary place of business in the
United States.
(3) SMALL BUSINESS CONCERN.—The term “small business concern”
has the same definition as set out in section 632 of title 15, United States Code.
(f) AUTHORIZATION OF APPROPRIATION.—There is authorized to beappropriated for fiscal year 2022 and each fiscal year thereafter $1,000,000 to carry out
the program authorized in this section.
Core Goal #2: Implement a Coordinated U.S. National Policy for the IDDI.
Create an allocated Emerging Technology Fund for foreign operations and related programsof USAID and the Department of State.
SEC. ___.—EMERGING TECHNOLOGY FUND.—(a) ESTABLISHMENT.—There is established within the Department of Statean Emerging Technology Fund (“Fund”) to facilitate holistic planning of digital foreignassistance, digital development projects, emerging technology programs, and otherrelated initiatives of the Department of State and the United States Agency for International
Development and to ensure the efficient management, coordination, operation, andutilization of such resources.
(b) FUNDING.—Funds otherwise available for the purposes of subsection (a) may
be deposited in such Fund.

p723

D R A F T L E G I S L AT I V E L A N G U A G E(c) AVAILABILITY.—-Amounts deposited into the Fund shall remain available until
expended.
(d) EXPENDITURES FROM FUND.—Amounts deposited in the Fund shall beavailable for the purposes of subsection (a).
(e) TRANSFER AUTHORITY.—Amounts available in the Fund may be transferred
to any account of the Department of State or the United States Agency for International
Development authorized by the Secretary of State for the purposes of carrying out aprogram described in subsection (a). Any amount so transferred shall be credited to theaccount to which it is transferred. The transfer authority provided in this subsection is inaddition to any other transfer authority available to the Department of State.
								
Recommendation: Enhance the United States’ Position as an International Digital Research
Hub.
Component #2: Establish the Multilateral AI Research Institute (MAIRI).
SEC. ___.—MULTILATERAL ARTIFICIAL INTELLIGENCE RESEARCH INSTITUTE.—(a) ESTABLISHMENT.—Not later than 180 days after the date of the enactmentof this Act, the Director of the National Science Foundation (“Director”) shall establish a
Multilateral Artificial Intelligence Research Institute (“MAIRI”) that leverages the National
Artificial Intelligence Research Institutes as well as contributions from international partners,
U.S. Government agencies, and non-governmental partners to facilitate international
collaborative research and development initiatives involving artificial intelligence (“AI”).
MAIRI shall have both a physical center located in the United States and a virtual presence.
(b) PURPOSE.—The purpose of MAIRI shall be to facilitate collaboration ofinternational artificial intelligence research, foster international artificial intelligenceinnovation, and develop the next generation global artificial intelligence workforce ina manner that comports with democratic values and helps to preserve free and open
societies.
(c) INTERNATIONAL PARTNERS.—As authorized by section 1872 of title 42,
United States Code, the Director, in coordination with the Secretary of State, shall seek todevelop partnerships with foreign governments that have existing research agreements
and collaborative relationships with the United States. The Director of MAIRI shall provide
for international partners to collaborate in the governance of MAIRI, contingent upon
appropriate contributions of financial support.
(d) OTHER PARTNERS.—To further the goals of MAIRI, the Director shall seek,
as necessary, partnerships with other U.S. Federal departments and agencies, and theirnational laboratories, and non-governmental partners, such as from industry, academia,
research institutions, and philanthropies on a project-by-project basis.

p724

APPENDIX D(e) FACILITATION.—The Director, in coordination with the Secretary of State, shall
facilitate the operations of MAIRI by creating a trusted learning cloud and associatedcompute capacity to facilitate international collaborative research by enabling access toneeded resources, compute, and data for shared innovation, research, and development(f) RESEARCH AGENDA.—MAIRI shall work with international partners, as well as
U.S. Government partners, as needed, to—(1) develop principles for multilateral artificial intelligence research,which address the importance of research integrity, the need for transparency,
the necessity of open data and data sharing, the development of risk-benefit
frameworks, and the use of merit-based competition reviews for research
proposals; and(2) develop research priorities that leverage members’ capabilities andmay include the development of—(A) shared, secure compute resources, including joint
benchmarking projects and data sharing, pooling, and storing initiatives
founded on commonly agreed principles that ensure trust, privacy andsecurity;(B) privacy-preserving artificial intelligence and machine learningtechnologies, including technologies like federated learning and on-device
prediction that enable remote execution, encrypted computation throughmulti-party computation and homomorphic encryption, and differential
privacy; and(C) smart city technologies, aligned with democratic values, that
promote sustainability as well as norms that should guide standards
development at bodies like the ITU and technical standards bodies.
(g) SOLICITATION AUTHORIZED.—The Director is authorized to issue one ormore solicitations to create a physical facility to support the establishment of MAIRI. Anysuch solicitation shall provide for the selection of an awardee on a competitive, meritreviewed basis.
(h) FINANCIAL ASSISTANCE TO ESTABLISH AND SUPPORT MAIRI.—Subject tothe availability of funds appropriated for this purpose, the Director, the Secretary of Energy,
the Secretary of State, the Secretary of Commerce, and other Federal agency heads may
award financial assistance, as determined by an agency head, to establish and support
MAIRI and associated research.

p725

D R A F T L E G I S L AT I V E L A N G U A G E(i) AUTHORIZATION OF APPROPRIATION.—There is authorized to be appropriatedfor fiscal years 2022 through 2027, in such funds as may be required, for the purpose of—States;(1) establishing and maintaining a physical center for MAIRI in the United(2) carrying out MAIRI research initiatives in cooperation with the National
Science Foundation, the Department of Energy, the Department of State, andother appropriate federal agencies;(3) creating a trusted learning cloud and associated compute capacity tofacilitate international collaborative research;(4) U.S. researchers’ travel and associated expenses to participate in
MAIRI workshops, conferences, and similar events; and(5) the establishment of an endowment fund in cooperation withinternational partners.
Recommendation: Reorient U.S. Foreign Policy and the Department of State for GreatPower Competition in the Digital Age.
Expedite necessary reorganization of the Department of State by passing legislation tocreate an Under Secretary for Science, Research and Technology (Q).
SEC. ___.—UNDER SECRETARY OF STATE FOR SCIENCE, RESEARCH ANDTECHNOLOGY.—(a) POSITION ESTABLISHED.—Subsection (b) of section 2651a of title 22, UnitedStates Code, is amended—(1) in paragraph (1), by striking “6” and inserting “7”;(2) by redesignating paragraph (4) as paragraph (5); and(3) by inserting before redesignated paragraph (5) the following newparagraph:“(4) UNDER SECRETARY FOR SCIENCE, RESEARCH AND TECHNOLOGY.
There shall be in the Department of State, among the Under Secretaries authorizedby paragraph (1), an Under Secretary for Science, Research and Technology, whoshall have primary responsibility to assist the Secretary and the Deputy Secretaryon matters related to international science and technology policy.”(b) REORGANIZATION REQUIRED.—Not later than 180 days after the date of
the enactment of this Act, the Secretary of State shall develop a plan to consolidate thep726

APPENDIX D

science and technology policy functions of the Department in a single division under theleadership of the Under Secretary for Science, Research and Technology.
CHAPTER 16: ASSOCIATED TECHNOLOGIESBlueprint for Action
Recommendation: Foster a Vibrant Domestic Quantum Fabrication Ecosystem.
Enact a package of provisions that incentivizes the domestic design and manufacturing ofquantum computers and their constituent materials.
SEC. ___.—TAX CREDIT FOR DOMESTIC DESIGN AND MANUFACTURING OF
QUANTUM COMPUTERS AND CONSTITUENT MATERIALS.—Section 41(d) of title 26,
United States Code, is amended by adding at the end a new paragraph (5), as follows—“(5) SPECIAL RULE FOR DOMESTIC DESIGN AND MANUFACTURING OF
QUANTUM COMPUTERS AND CONSTITUENT MATERIALS.—“(A) With regard to domestic design and manufacturing of qualified
quantum computers and constituent materials, the term ‘qualified research’ shall
include, in addition to research described in paragraph (1)—“(i) the development and production of qualified quantum
computers and constituent materials in the United States; and“(ii) the training of United States persons with regard to thedevelopment and production of qualified quantum computers andconstituent materials.
“(B) In this paragraph, the term ‘qualified quantum computers andconstituent materials’ means—“(i) any computers have been identified by the Secretary, in
consultation with the Secretary of Commerce, as quantum computers;and“(ii) any components or constituent parts of such computers thathave been identified by the Secretary, in consultation with the Secretary ofCommerce, as critical to the operation of such computers.”
General Note: Should Congress establish a National Technology Foundation pursuant to
the Commission’s Chapter 11 recommendation, Congress should also review conflicting
National Science Foundation authorities and delegating appropriate authorities to the NTF.

p727

D R A F T L E G I S L AT I V E L A N G U A G E

p728

APPENDIX E

Appendix E: Funding
Recommendation Table
Funding Recommendation Table

Chapter

Chapter 1– Emerging
Threats in the
AI Erap729

CabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail1Create a Foreign
Malign Influence
Response Joint
Interagency Task
Force (JIATF).

Office of the
Director of National
Intelligence$30 million-

2

Increase DARPA
funding formedia media
authentication,
disinformation
detection,
attribution, anddisruption.

Department of
Defense:USD(R&E) DARPA$60 million to$80 million-

3

Fund a machine
speed AI-enabledcyber defense
acceleration study.

Department ofHomeland Security$10 million-

4

Increase DARPA
funding for AIenabled cyberdefense research.

Department of
Defense:USD(R&E) DARPA”$20 million-

5

Increase National
Institute ofStandards andTechnology AI
testbed funding.

National Instituteof Standards andTechnology$25 million-

6

Provide funding fora SolarWinds threat
review.

Cyberspace
Solarium
Commission$6.5 million-

Recommendation

F U N D I N G R E C O M M E N D AT I O N TA B L E

Chapter

Chapter 2 –Foundations of
Future Defense

Recommendation

CabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail1

Establish a
dedicated AI Fund.

Department of
Defense:USD(R&E)$200 million-

2

Increase
investments in AI
R&D.

Department of
Defense$8 billion-

3

Establish a fundto to accelerate
procurement
and integrationof commercial
AI solutions
for businessapplications.

Department of
Defense:
Joint Artificial
Intelligence Center$100 million-

4

Provide funding tobuild enterprise data
sets.

Department of
Defense:
Office of the Chief
Data Officer$125 million-

5

Provide funding fortechnology scouting
tools, data, and atechnology fellows
program.

Departmentof Defense,
USD(R&E)$10 million-

1

Develop innovative
operational
concepts that
integrate new
warfighting
capabilities
with emerging
technologies.

Department of
Defense:USD(R&E)$5 million-

2

Incentivize
experimentation
with AI-enabledapplications throughthe Warfighting
Lab Incentive Fund(WLIF).

Department of
Defense:USD(R&E)$10 million-

3

Encourage a cultureof “Thinking Red.”

Department of
Defense:
Joint Warfighting
Analysis Center$2.5 million-

Chapter 3 – AI
and Warfarep730

APPENDIX E

ChapterCabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail4

Direct the military
services, incoordination with
the Under Secretaryof Defense (forAcquisition andSustainment),
the Joint Staff,
and the Defense
Logistics Agency,
and enabled byenterprise services
and expertise at the
JAIC, to prioritize
integration of
AI into logistics
and sustainment
systems wherever
possible.

Department of
Defense:
Office of the
Deputy Secretaryof Defense$100 million-

5

Define a joint
warfighting networkarchitecture by theend of 2021.

“Department of
Defense:
Office of the Chief
Information Officer”$5 million-

1Work with theintelligencecommunity toestablish a 10-year,$1 billion, Programof Record toprovide long-term,
predictable funding
for technologies
identified in thetechnology annex
to the National
Intelligence
Strategy.

Office of the
Director of National
Intelligence$1 billion
annually for FYs
2022-2032-

1

Congress should
create a National
Reserve Digital
Corps.

Office ofManagement andBudget$16 million-

2

Congress should
establish a STEM
Corps.

Department of
Defense$5 million for
FY 2022 &$5 million for
FY 2023-3

Congress should
create a UnitedStates Digital
Service Academy.

New Entity$40 million initialappropriation-

Recommendation

Chapter 3 – AI
and Warfare

Chapter 5 – AI
and the Futureof National
Intelligence

Chapter 6– TechnicalTalent inGovernmentp731

F U N D I N G R E C O M M E N D AT I O N TA B L E

Chapter

AmountApproprations Detail

1

Appoint responsible
AI leads andsupporting staff ineach agency criticalto national security.

Department of
Defense;
Office of the
Director of National
Intelligence;
Department ofHomeland Security;
Federal Bureau of
Investigation;
Department ofState;
Department ofEnergy; &Department of
Health and Human
Services$21.5 million

This funding supports
one responsible AI leadand two supporting staff.
Additionally, the funding
includes responsible
AI leads for each of thearmed services in the
Department of Defense
and each of the agenciesof the Intelligence
Community.

Chapter 8 –Upholding
Democratic
Values1

Congress should
establish third-party
testing center(s) toallow independent,
third-party testing of
national securityrelated AI systems
that could impact
U.S. persons.

National Instituteof Standards andTechnology$1.2 million-

Chapter 9 – A
Strategy forCompetition
andCooperation

1

Create a Technology
CompetitivenessCouncil.

The White House:
Executive Office of
the President$2 million-

Chapter 10– The TalentCompetition

1

Congress shouldpass a new National
Defense Education
Act.

Department of
Education;
National Science
Foundation

One time
apprporiation of$8.2 billion-

New Entity$30 million initialappropriation
for start-up
expenses;$1 billion for FY
2022;$5 billion for FY
2023;$10 billion for
FY 2024;$15 billion for
FY 2025; &$20 billion for
FY 2026-

Chapter 7– Establishing Justified
Confidence in
AI Systems

Chapter 11 –Accelerating AI
Innovation

Recommendation

CabinetDepartments,
Major Agencies,
and Program
Offices1

Establish a
National Techology
Foundation.

p
732

APPENDIX E

Chapter

Recommendation

1

Chapter 11 –Accelerating AI
Innovationp
733

Increase federal
funding of NonDefense AI R&D atcompounding levels.

CabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations DetailMultiple agencies,
including:the NSCAI
proposed National
Technology
Foundation;
National Science
Foundation;
Department ofEnergy;National Instituteof Standards andTechnology;
National Institutesof Health; &National
Aeronauticaland Space
Administration$2 billion for
FY 2022;$4 billion for
FY 2023;$8 billion for
FY 2024;$16 billion for
FY 2025; &$32 billion for
FY 2026--

2

Expand the Networkof AI ResearchInstitutes.

National Science
Foundation$200 million
for FY 2022;$200 million
for FY 2023;& $200 million
for FY 2024

3

Establish an AI
Innovator Award.

National Science
Foundation$125 million

-

-

4

Establish a teambased AI Award.

National Science
Foundation$50 million
for FY 2022;$100 million
for FY 2023;$150 million
for FY 2024;$200 million
for FY 2025;& $250 million
annually for FYs
2026-2028

5

Implement the
NAIRR Roadmap.

National Science
Foundation$30 million-

6

Fund an AI Data
Program.

Department ofEnergy$25 million-

7

Sponsor an
Open Knowledge
Network.

National Science
Foundation$25 million-

8Form a network ofRegional Innovation
Clusters.

National Instituteof Standards andTechnology$200 million for
FYs 2022-2026

Funding recommended
at $20 million perRegional Innovation
ClusterF U N D I N G R E C O M M E N D AT I O N TA B L E

Chapter

Chapter 13 –Microelectronics

CabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail1

Increase federal
grants for
microelectronics
manufacturing.

Department ofCommerce$15 billion total$3 billion per project on
average2

Increase funding for
DARPA’s Electronics
Resurgence
Initiative (ERI).

Department of
Defense:USD(R&E) DARPA$400 million
for FY 2022 &$5 billion total
for FYs 20222026

These funding levels
should ramp up on
an annual basis as
absorptive capacity
increases

3

Increase funding
for National
Science Foundation
semiconductor
research.

National Science
Foundation$300 million
for FY 2022 &$2.5 billion total
for FYs 20222026

These funding levels
should ramp up on
an annual basis as
absorptive capacity
increases

4

Increase funding
for Departmentof Energy
semiconductor
research.

Department ofEnergy$400 million
for FY 2022 &$4.5 billion total
for FYs 20222026

These funding levels
should ramp up on
an annual basis as
absorptive capacity
increases

5

Establish theAdvanced
Packaging National
Manufacturing
Program.

National Instituteof Standards andTechnology$1 billion for
FY 2022 &$5 billlion total
for FYs 20222026-

6

Establish
the National
Semiconductor
Technology Center.

Department ofCommerce incollaboration with
the Departmentof Defense andDepartment ofEnergy$100 million
FY 2022 &$2 billlion total
for FYs 20222026-

Recommendationp734

APPENDIX E

ChapterCabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail

1

Provide funding for
U.S. International
Development
Finance Corporation
to executedevelopment
financing fortechnology
infrastructure
projects.

U.S. International
Development
Finance
Corporation$1 billion-

2

Provide funding
to support U.S.
International
Development
Finance Corporation
development
financing initiatives.

Department ofState;
U.S. Agency forInternational
Development$200 million-

3

Provide funding
for U.S. Agency
for International
Development Digital
Strategy.

U.S. Agency forInternational
Development:
Bureau of
Democracy,
Development, andInnovation$200 million-

4

Provide funding
for an Interagency
AI Standards
team to support
National Instituteof Standards
and Technology
AI Standards
Coordinator andfund travel andother administrativeneeds.

National Instituteof Standards andTechnology;
Department of
Defense;
Department ofState;
Office of the
Director of National
Intelligence;
Department ofEnergy;Department ofHomeland Security;
U.S. Agency forInternational
Development$3.3 million

Funding includes fivefull-time employee (FTE)from National Institute ofStandards and Technology and one FTE fromeach of the following departments and agencies:
Department of Defense,
Department of State,
Office of the Director of
National Intelligence,
Department of Energy,
Department of Homeland Security, and U.S.
Agency for International
Development.

5

Provide funding tosupport grants forsmall- and mediumsized businesses
to participate ininternational data
and technicalstandards efforts.

Small Business
Administration$1 million-

Recommendation

Chapter 15 –A Favorable
International AI
Order

p
735

F U N D I N G R E C O M M E N D AT I O N TA B L E

Chapter

Recommendation

CabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail

National Science
Foundation$1 million-$12.15 million
annually for FYs
2022-2027$10 million to National
Science Foundation/
Department of State/
Department of Energy for
research and personnel;$2M to National
Science Foundation
for infrastructure;$150,000 to National
Science Foundation foradministrative costs.

6

Funding foradministrative costs
associated withestablishing an U.S.
Center of Expertise
relationship with
GPAI/OECD.

7

Funding for theMultilateral AI
Research Initiative(MAIRI), including
establishing andmaintaining physicalcenter; supporting
research initiatives;
created a trusted
learning cloud
resource; andsupporting U.S.
researchers’ travel
and involvement
in workshops,
conferences, andevents.

National Science
Foundation;
Department ofState;
Department ofEnergy8

Provide funding
for trusted learning
cloud to facilitate
collaborative R&D
with allies andpartners (envisioned
as a component of
MAIRI).

National Science
Foundation;
Department ofState$11.3 million

Funding includes
underlying infrastructure,
data storage and sharing
capacity, grants forresearchers, foreignassistance grants.

9Provide funding
to support grants
for scholars andresearchers toparticipate ininternational data
and technicalstandards efforts.

Department ofState$5 million-

Chapter 15 –A Favorable
International
AI Orderp736

APPENDIX E

Chapter

Recommendation

737

AmountApproprations Detail

10

Provide funding
for immediate
augmentation andtraining of U.S.
diplomatic corps
for efforts related
to AI and emerging
technology (funding
does not include
future fundingneeds which
we recommend
be determinedby a focusedplanning effort tobe undertaken
by Department ofState).

Department ofState$8 million$550,000 - STAS;$550,000 - Office ofCommunication andInformation Policy;$400,000 - Office of
Science and Technology
Cooperation; $3.8 million- Regional Technology
Officers (12 locations);$1.25 million - Officeof the Special
Representative to Silicon
Valley; $450,000 - FSI
training.

11

Provide funding
for the Bureau
of Cyberspace
Security andEmerging
Technologies.

Department ofState$20 million-

12

Provide funding for
public diplomacy
and engagement
activities on AI
innovation anddemocratic values.

Department ofState$5.5 million-

13

Provide funding
for AI exchange
programs to support
U.S. values andfund participation
by developing
countries in
multilateral AI
activities.

Department ofState$8.5 million-

14

Provide funding forefforts to promote
U.S. innovation andvalues and support
American Spaces,
Tech Camps, Maker
Spaces, Speakers
Program, and otherinitiatives.

Department ofState$3 million-

Chapter 15 –A Favorable
International
AI OrderpCabinetDepartments,
Major Agencies,
and Program
OfficesF U N D I N G R E C O M M E N D AT I O N TA B L E

ChapterCabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail

15

Provide funding
for tracking
and analysis of
public opinion tomeasure impact ofengagement efforts
and guide strategicplanning.

Department ofState$1 million-

16

Provide funding
for U.S. Science
Envoys andEmbassy Science
Fellows programs.

Department ofState$1 million-

Recommendation

17

Provide funding
to support U.S.
leadership in AI
through Emerging
Technology
Coalition andinternal programs.

Department ofState:
Office of the Under
Secretary for
Economic Growth,
Energy, and theEnvironment (E)$5.5 million

Funding includes ETC
support, creation of anadvisory committee on
emerging technology,
private sector
engagement, multilateral
R&D efforts, techoriented diplomaticefforts, innovation
enhancements.

18“Funding tosupport promotionof human rights
and fundamental
freedoms in AI
context through civilsociety initiatives,promoting AI
and emerging
tech to countercensorship,and supporting
research andawareness
campaigns”

Department ofState: Office of theUnder Secretary for
Civilian Security,
Democracy, andHuman Rights (J):
Bureau of
Democracy, Human
Rights, and Labor(DRL)$1.5 million-

19

Provide funding tosupport use of AI
for national security/
military applications
through cooperation
with allies andpartners, to include
joint exercises,
grants, fellowships,
and other activities.

“Departmentof State: Officeof the Under
Secretary of State
for Arms Control
and International
Security (T)”

$3 million-

Chapter 15 –A Favorable
International
AI Orderp738

APPENDIX E

ChapterCabinetDepartments,
Major Agencies,
and Program
OfficesAmountApproprations Detail

20

Provide funds tosupport building
technical capacityin emerging
democracies andmarket economies
to counter maligninfluence.

Department ofState$3 million-

21

Provide funds tosupport research
grants on maligninfluence in AI
ecosystems.

Department ofState$2 million-

22

Provide funds tosupport public
diplomacy intiatives
on international
AI standards
and tracking
and reporting of
impact on public
engagement.

Department ofState$2 million-

23

Provide funds tosupport US Global
Innovation throughScience andTechnology (GIST)
Initiative.

Department ofState$1 million-

24

Provide additional
funding to support
foreign assistanceactivities aroundemerging tech anddigital infrastructure,
to include planning,assessments,
and provision ofassistance. Funds
would support
targeted, digitalprograms in several
areas, including
rule of law (INL),
democracy andhuman rights (DRL),
security cooperation(AVC/PM/ISN), andtechnical assistance(EB, STAS, others).

Department ofState$230 million-

Recommendation

Chapter 15 –A Favorable
International
AI Order*Unless otherwise noted funding is annual beginning in Fiscal Year 2022.
**All funding figures should be considered initial estimates for consideration by Congress and the
Executive Branch.

p739

F U N D I N G R E C O M M E N D AT I O N TA B L E

p740

APPENDIX F

Appendix F:
Commissioner Bios
Dr. Eric Schmidt, Chair

Dr. Eric Schmidt is an accomplished technologist,
entrepreneur, and philanthropist. He joined Google
in 2001 and helped grow the company from a
Silicon Valley startup to a global leader in technology
alongside founders Sergey Brin and Larry Page.
Schmidt served as Google’s Chief Executive
Officer and Chairman from 2001 to 2011, as well
as Executive Chairman and Technical Advisor.
Under his leadership, Google dramatically scaled
its infrastructure and diversified its product offerings
while maintaining a strong culture of innovation.
In 2017, he co-founded Schmidt Futures, a philanthropic initiative that bets early on
exceptional people making the world better. Schmidt is the host of “Reimagine with Eric
Schmidt,” a podcast series of conversations with leaders to explore how society can build
a brighter future after the COVID-19 pandemic.

The Honorable Robert Work, Vice Chair

Robert Work was the 32nd Deputy Secretary of
Defense, serving alongside three Secretaries of
Defense from May 2014 to July 2017. In 2001, heretired as a Colonel in the United States Marine
Corps after spending 27 years on active duty. He
subsequently served as Senior Fellow and Vice
President and Director of Studies at the Center for
Strategic and Budgetary Assessments. In January
2009, he was asked to join the Obama administration
as the 31st Under Secretary of the Navy, and wasconfirmed in that role in May 2009. Work stepped
down as the Under Secretary in March 2013 to become the Chief Executive Officer for theCenter for a New American Security (CNAS). He remained in that position until he assumed
the role of Deputy Secretary of Defense in May 2014. He currently is the President andOwner of TeamWork, LLC, which specializes in defense strategy and policy, programmingand budgeting, military-technical competitions, revolutions in war, and the future of war.

p
741

COMMISSIONER BIOS

Safra Catz

Safra A. Catz has served as chief executive officer ofOracle Corporation since 2014 and a member of thecompany’s board of directors since 2001. She joined
Oracle in 1999 and held various positions within thecompany, including President and Chief Financial
Officer, prior to being named CEO. Catz currently
serves as a director of The Walt Disney Company andpreviously served as a director of HSBC Holdings plc.

Dr. Steve Chien

Dr. Steve Chien is a Technical Fellow, Senior Research
Scientist, and the Technical Group Supervisor of the
Artificial Intelligence Group at the Jet Propulsion
Laboratory, California Institute of Technology. Chien
has led the deployment of AI software to a wide
range of missions. He is currently supporting thedevelopment of onboard and ground automatedscheduling for the Mars 2020 rover mission, as
well as scheduling technologies for the ECOsystem
Spaceborne Thermal Radiometer Experiment on
Space Station (ECOSTRESS) and Orbiting Carbon
Observatory 3 (OCO-3). Chien has received numerous awards for these efforts, to include
Lew Allen Award for Excellence, JPLs highest award recognizing outstanding technicalachievements by JPL personnel in the early years of their careers. He has been recognized
four times in the NASA Software of the Year competition and has received four NASA
medals for his work in AI for space. In 2011, Chien was awarded the inaugural American
Institute of Aeronautics and Astronautics Intelligent Systems Award for his contributions tospacecraft autonomy.

The Honorable Mignon Clyburn

Mignon L. Clyburn served as Commissioner on theFederal Communications Commission (FCC) from2009 to 2018, and acting chair from May to Novemberof 2013. During her nearly nine years at the FCC,
Mignon was committed to closing persistent digitaland opportunities divides that continue to challengerural, Native, and low wealth communities. Previously,
Clyburn served for 11 years on the South Carolina
Public Service Commission. Prior to that, she was the
publisher and general manager of the Coastal Times,p742

APPENDIX F

a family-founded, Charleston-based weekly newspaper focusing on issues affecting theAfrican American community. Clyburn is currently the principal of MLC Strategies, LLC.

Christopher Darby

Christopher Darby has served as President andCEO of In-Q-Tel since September 2006. He is alsoa member of its Board of Trustees. Prior to joining
In-Q-Tel, Darby was a Vice President and General
Manager at Intel, where he oversaw the Middleware
Products Division. He joined Intel in August 2005 withthe acquisition of Sarvega, a venture-backed supplierof XML networking and security products, where
he served as President and CEO. Prior to Sarvega,
Darby was the Chairman and CEO of @stake, anInternet security consulting firm ultimately acquired by
Symantec. Before that, Darby served as President and CEO of Interpath Communications,which was later acquired by US Internetworking. Earlier in his career, he held several
executive positions at Digital Equipment Corporation (now Hewlett-Packard) and Northern
Telecom (now Nortel Networks). Chris began his career at Bell Northern Research.

Dr. Kenneth Ford

Dr. Kenneth Ford is Founder and CEO of the Institutefor Human & Machine Cognition. His research interests
include AI, human-centered computing, and humanperformance and resilience. Ford is a Fellow of the
Association for the Advancement of AI (AAAI), and acharter Fellow of the National Academy of Inventors.
He has received many awards and honors including
the Doctor Honoris Causas from the University of
Bordeaux in 2005, the 2008 Robert Englemore Awardfor his work in AI, and the AAAI Distinguished Service
Award in 2015. In 2015, he was elected as Fellow of
the American Association for the Advancement of Science and in 2017 was inducted into
the Florida Inventors Hall of Fame. Ford has served on the National Science Board, the Air
Force Science Advisory Board, and the Defense Science Board. In 2008, he was named
as Chairman of the NASA Advisory Council – a capacity in which he served through 2011.
In 2010, Ken was awarded NASA’s Distinguished Public Service Medal – the highest honor
the agency confers.

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COMMISSIONER BIOS

Dr. José-Marie Griffiths

Dr. José-Marie Griffiths is president of Dakota State
University in Madison, South Dakota. Griffiths has
spent her career in research, teaching, public service,
corporate leadership, economic development, andhigher education administration. She has served inpresidential appointments to the National Science
Board, the U.S. President’s Information Technology
Advisory Committee, and the U.S. National
Commission on Libraries and Information. Griffiths
has led projects for more than 28 U.S. federal
agencies such as the National Science Foundation
and NASA, and more than 20 major corporations including, AT&T Bell Laboratories and IBM,in more than 35 countries. She also has worked with seven major international organizations,
including NATO and the United Nations. She has received over 20 significant awards in science,technology, teaching, and the advancement of women in these fields.

Dr. Eric Horvitz

Dr. Eric Horvitz is a technical fellow at Microsoft,where he serves as the company’s first Chief
Scientific Officer. Horvitz provides crosscompany leadership and perspectives on
advances and trends on scientific matters,
and on issues and opportunities arising at theintersection of technology, people, and society.
He is recognized for his research on challenges
and opportunities with the uses of AI technologies
amidst the complexities of the open world. Horvitz
is the recipient of the Feigenbaum Prize and the
Allen Newell Prize for contributions to AI.

Andrew Jassy

Andy Jassy is the founder and CEO of
Amazon Web Services (AWS), the world’s mostcomprehensive and broadly adopted cloud
platform. Jassy launched AWS in 2006 andhas managed an inventive and nimble teamthat has delivered more than 165 services forcompute, storage, networking, databases,
analytics, mobile, Internet of Things, Artificial
Intelligence, security, hybrid, and enterpriseapplications. Prior to founding AWS, Jassy heldp744

APPENDIX F

several leadership positions across Amazon. Shortly after joining the company in 1997,
he authored the business plan for Amazon’s Music business and served as its Directorof Product Management and General Manager. Jassy also started the Amazon Customer
Relationship Management team, led marketing for Amazon, and was Technical Advisor(shadow) to Amazon Founder and CEO Jeff Bezos.

Gilman Louie

Gilman Louie is Co-Founder and Partner of Alsop
Louie Partners, an early-stage technology venture
capital firm founded in 2006. From 1999 until 2006,
Louie was the first CEO of In-Q-Tel. Prior to In-Q-Tel,
Louie built a career as a pioneer in the interactive
entertainment industry, during which he founded
and ran a publicly traded company called Spectrum
HoloByte, and served as Chief Creative Officer ofHasbro Interactive. He serves as a member of theBoard of Directors for the Markle Foundation, Maxar
Technologies, Niantic, Lookingglass Cyber Solutions,
Aurora Insights and various other private companies and non-profit foundations. He is also
Chairman of the Board of the Federation of American Scientists. Louie has served as amember of the Technical Advisory Group of the United States Senate Select Committee onIntelligence, and as a Commissioner of the National Commission for Review of Researchand Development Programs of the United States Intelligence Community. He has received
dozens of awards for his achievements, including from the NGA, CIA, and DNI, and in 2002
was named as one of fifty scientific visionaries by Scientific American.

Dr. William Mark

Dr. William Mark leads SRI International’s Information
and Computing Sciences division, creating newtechnology in machine learning, virtual personal
assistance, trusted systems, and speech andvision analytics. The group also commercializes
technology, licensing to corporations and creating
spinoff companies such as Siri, Kasisto, CurieAI,
and LatentAI. Prior to joining SRI International, Markheaded research groups at National Semiconductor,
Lockheed Martin, and the University of Southern
California Information Sciences Institute.

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COMMISSIONER BIOS

Dr. Jason MathenyDr. Jason Matheny is the founding director ofGeorgetown University’s Center for Security andEmerging Technology (CSET). Previously he served
as Assistant Director of National Intelligence, andDirector of IARPA, responsible for the developmentof breakthrough technologies for the U.S. intelligencecommunity. Before IARPA, he worked at Oxford
University, the World Bank, the Johns Hopkins
University Applied Physics Laboratory, the Center for
Biosecurity, and Princeton University, and was the cofounder of two biotechnology companies.

The Honorable Katharina McFarland

Katharina McFarland serves as Chairman of theBoard of Army Research and Development at the
National Academies of Science, and as a Director
on the Boards of SAIC, Exyn Technologies, and
the Procurement Round Table. With more than 30
years of government service, McFarland is widely
recognized as a leading subject matter expert
on government procurement. She also serves as
an advisor to Raytheon Missile Systems Division
Senior Advisory Board, Cypress International Senior
Strategy Group, Transunion Corporation Advisory
Board, and Sehlke, Inc. Senior Advisory Board. From 2012 to 2017, McFarland served
as the Assistant Secretary of Defense for Acquisition and as acting Assistant Secretaryof the Army (Acquisition, Logistics & Technology) from 2016-2017. She was President of
the Defense Acquisition University from 2010 to 2012, and the Director of Acquisition at
the Missile Defense Agency from 2006 to 2010. She has received an Honorary Doctoralof Engineering from the University of Cranfield in the United Kingdom, the Presidential
Meritorious Executive Rank Award, the Secretary of Defense Medal for Meritorious Civilian
Service Award, the Department of the Navy Civilian Tester of the Year Award, and the Navyand United States Marine Corps Commendation Medal for Meritorious Civilian Service.

p746

APPENDIX F

Dr. Andrew Moore

Dr. Andrew W. Moore is a distinguished computerscientist with expertise in machine learning androbotics. He became the head of Google Cloud
Artificial Intelligence division in January 2019. Moore
previously worked at Google from 2006 to 2014 andwas the founding director of Google’s Pittsburgh
engineering office in 2006. He then spent a fouryear hiatus at Carnegie Mellon University as thedean of the School of Computer Science. Moore’s
research interests encompass the field of “big data”— applying statistical methods and mathematicalformulas to massive quantities of information, ranging from web searches to astronomy to
medical records, in order to identify patterns and extract meaning from that information. His
past research has included improving the ability of robots and other automated systems tosense the world around them and respond appropriately.

p
747

COMMISSIONER BIOSp
748

APPENDIX G

Appendix G: Commission Staff
and Contributors
EXECUTIVE STAFF
Yll Bajraktari,
Executive Director

Michael J. Lueptow,
General Counsel

Michael L. Gable,
Chief of Staff

Tara M. Rigler,
Director of Strategy,
Communications & Engagements

Angela A. Ponmakha,
Director of Operations,
Designated Federal Officer

LEGISLATIVE AFFAIRS
Brandon McKee

Jenilee Keefe Singer

SENIOR ADVISORS
Dr. Seth Center		Robert Nelson

RESEARCH AND ANALYSIS
Courtney Barno
Dr. Ryan Carpenter
Matthew Cordova
Caroline Danauy
Raina Davis
Tess deBlanc-Knowles
Rama Elluru
Michael Garrisp749

Matthew Gentzel
Charles Howell
LTC Michael Jackson, USA
Rebekah Kennel
Jeffrey Kojac
David Kumashiro
CAPT Lance Lantier, USN
Christie LawrenceC O M M I S S I O N S TA F F A N D C O N T R I B U T O R SPaul Lekas
Dr. Margaret Lentz
Quinn Lorenz
Justin Lynch
Col Paul “P.J.” Maykish, USAF
Kevin McGinnis
Christopher McGuirePaul Rhodes
Dr. Christopher RiceJoe Wang
Parker Wild
Jessica Young
Olivia Zetter

OPERATIONS AND LEGAL TEAM
Chelsea Holt
Sarah Johnson
Brent Myles

Jennifer SheehanAngela Stacks
Jamie Tomberlin

INTERNS
Richard Altieri
Madeline Blanchard
Sabrina Broderick
Shaantam Chawla
Devin Davidson
Nickie Deahl
Hudson Dizon
Dylan Halpern
Courtney Lange
Alexander Mann
Nikhil Marda
M. Marin Ruelas Mendoza

Ariana Orne
Sultan Seraj
Katie Stolarczyk
Jaide Tarwid
Christopher Tonelli
Claire Trotter
Samuel Trotter
Aristotle Vainikos
Jackson Valen
Zoe Weinberg
Kate Yeagerp
750

APPENDIX G

The Commission would like to thank Members of Congress, Congressional Staff,
government personnel, industry professionals, academia, members of the public, and allothers who participated in, advised on, or commented on our work. The unified effort ofeveryone involved made this document possible. Additionally, the Commission thanks allof our outside contributors whose hard work resulted in this product.

OUTSIDE CONTRIBUTORSJohn Bansemer
Susanna Blume
Dr. Anne Bowser
Scott Britt
Kristy ColbertMark Cohen
Dr. David Danks
Jeffrey Ding
Dr. Kathleen Fisher
Amanda Foley
Dr. Kristin Gilkes
Dr. Bryce Goodman
Gregory Grant
Erin Hahn
Orin Hoffman
Dajonte Holsey
Dr. Michael Horowitz
Dr. Andrew Imbrie
Taylor Lineberger
Dr. Albana Shehaj
Dr. Paul Scharre
Dr. William Scherlis
Raj Shah
John “Jack” Shanahan
Dr. Bernadette Johnson

Elsa Kania
Dr. Christopher Kirchhoff
Zachary Kuehn
Thomas KalilPeter LevineFrank Long
Michael “Brendan” McCord
Michael McNerney
Tariq Mehmood
Paul Michel
Dr. Nadia Schadlow Murphy
Adam Mossoff
Geoffrey Odlum
Scott Padgett
Jared C. Ponmakha
Douglas Rand
Dr. Heather Roff
Craig Smith
Michael Soos
Dean Souleles
Dr. Barbara Stephenson
Francoise von Trapp
German Wegbrait
Darren Wright
Dr. Amy Zegart

A special thanks to Lirijon Kadriu for designing the Commission’s logo.

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