THE NATIONAL 

ARTIFICIAL INTELLIGENCE 

RESEARCH AND DEVELOPMENT 

STRATEGIC PLAN: 2019 UPDATE

A Report by the

SELECT COMMITTEE ON ARTIFICIAL INTELLIGENCE

of the

NATIONAL SCIENCE & TECHNOLOGY COUNCIL

JUNE 2019
Dear Colleagues,

In his State of the Union address on February 5, 2019, President Trump stressed the importance of 

ensuring American leadership in the development of emerging technologies, including artificial 

intelligence (AI), that make up the Industries of the Future. Reflecting this importance, on February 11, 

2019, President Trump signed Executive Order 13859, which established the American Artificial 

Intelligence Initiative. This Initiative is a whole-of-government approach for maintaining American 

leadership in AI and ensuring that AI benefits the American people and reflects our Nation’s values. The 

first directive in this Executive Order is for Federal agencies to prioritize AI research and development 

(R&D) in their annual budgeting and planning process. The attached National AI R&D Strategic Plan: 

2019 Update highlights the key priorities for Federal investment in AI R&D.

Artificial intelligence presents tremendous opportunities that are leading to breakthroughs in 

improved healthcare, safer and more efficient transportation, personalized education, significant 

scientific discoveries, improved manufacturing, increased agricultural crop yields, better weather 

forecasting, and much more. These benefits are largely due to decades of long-term Federal 

investments in fundamental AI R&D, which have led to new theories and approaches for AI systems, as 

well as applied research that allows the translation of AI into practical applications.

The landscape for AI R&D is becoming increasingly complex, due to the significant investments that are 

being made by industry, academia, and nonprofit organizations. Additionally, AI advancements are 

progressing rapidly. The Federal Government must therefore continually reevaluate its priorities for AI 

R&D investments, to ensure that investments continue to advance the cutting edge of the field and are 

not unnecessarily duplicative of industry investments.

In August of 2018, the Administration directed the Select Committee on AI to refresh the 2016 National 

AI R&D Strategic Plan. This process began with the issuance of a Request for Information to solicit public 

input on ways that the strategy should be revised or improved. The responses to this RFI, as well as an 

independent agency review, informed this update to the Strategic Plan.

In this Strategic Plan, eight strategic priorities have been identified. The first seven strategies continue 

from the 2016 Plan, reflecting the reaffirmation of the importance of these strategies by multiple 

respondents from the public and government, with no calls to remove any of the strategies. The eighth 

strategy is new and focuses on the increasing importance of effective partnerships between the Federal 

Government and academia, industry, other non-Federal entities, and international allies to generate 

technological breakthroughs in AI and to rapidly transition those breakthroughs into capabilities. 

While this Plan does not define specific research agendas for Federal agency investments, it does 

provide an expectation for the overall portfolio for Federal AI R&D investments. This coordinated 

Federal strategy for AI R&D will help the United States continue to lead the world in cutting-edge 

advances in AI that will grow our economy, increase our national security, and improve quality of life. 

Sincerely,

Michael Kratsios

Deputy Assistant to the President for Technology Policy

June 21, 2019
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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Table of Contents

Executive Summary ...........................................................................................................................iii

Introduction to the 2019 National AI R&D Strategic Plan ........................................................................ 1

AI R&D Strategy .................................................................................................................................. 5

Strategy 1: Make Long-Term Investments in AI Research........................................................................... 7

2019 Update: Sustaining long-term investments in fundamental AI research................................................... 7

Advancing data-focused methodologies for knowledge discovery ................................................................ 9

Enhancing the perceptual capabilities of AI systems....................................................................................... 9

Understanding theoretical capabilities and limitations of AI........................................................................10

Pursuing research on general-purpose artificial intelligence........................................................................10

Developing scalable AI systems ......................................................................................................................11

Fostering research on human-like AI..............................................................................................................11

Developing more capable and reliable robots ...............................................................................................11

Advancing hardware for improved AI .............................................................................................................12

Creating AI for improved hardware ................................................................................................................12

Strategy 2: Develop Effective Methods for Human-AI Collaboration ........................................................ 14

2019 Update: Developing AI systems that complement and augment human capabilities,with increasing 

focus on the future of work .................................................................................................................................14

Seeking new algorithms for human-aware AI ................................................................................................17

Developing AI techniques for human augmentation .....................................................................................17

Developing techniques for visualization and human-AI interfaces...............................................................18

Developing more effective language processing systems .............................................................................18

Strategy 3: Understand and Address the Ethical, Legal, and Societal Implications of AI........................... 19

2019 Update: Addressing ethical, legal, and societal considerations in AI ......................................................19

Improving fairness, transparency, and accountability by design ................................................................. 21

Building ethical AI............................................................................................................................................21

Designing architectures for ethical AI.............................................................................................................21

Strategy 4: Ensure the Safety and Security of AI Systems ........................................................................ 23

2019 Update: Creating robust and trustworthy AI systems..............................................................................23

Improving explainability and transparency ...................................................................................................25

Building trust...................................................................................................................................................25

Enhancing verification and validation............................................................................................................25

Securing against attacks ................................................................................................................................. 26

Achieving long-term AI safety and value-alignment......................................................................................26

Strategy 5: Develop Shared Public Datasets and Environments for AI Training and Testing..................... 27

2019 Update: Increasing access to datasets and associated challenges ........................................................27

Developing and making accessible a wide variety of datasets to meet the needs of a diverse spectrum 

of AI interests and applications ......................................................................................................................29

Making training and testing resources responsive to commercial and public interests ..............................30

Developing open-source software libraries and toolkits...............................................................................30

Strategy 6: Measure and Evaluate AI Technologies through Standards and Benchmarks ......................... 32

2019 Update: Supporting development of AI technical standards and related tools ......................................32

Developing a broad spectrum of AI standards ...............................................................................................33

Establishing AI technology benchmarks ........................................................................................................34

Increasing the availability of AI testbeds........................................................................................................34

Engaging the AI community in standards and benchmarks ..........................................................................35

Strategy 7: Better Understand the National AI R&D Workforce Needs ...................................................... 37

2019 Update: Advancing the AI R&D workforce, including those working on AI systems and those working 

alongside them, to sustain U.S. leadership......................................................................................................37

Strategy 8: Expand Public–Private Partnerships to Accelerate Advances in AI............................................ 40

Abbreviations ...................................................................................................................................43
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Executive Summary

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including 

the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 

2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial 

Intelligence.

1 This order launched the American AI Initiative, a concerted effort to promote and protect 

AI technology and innovation in the United States. The Initiative implements a whole-of-government 

strategy in collaboration and engagement with the private sector, academia, the public, and likeminded international partners. Among other actions, key directives in the Initiative call for Federal 

agencies to prioritize AI research and development (R&D) investments, enhance access to high-quality 

cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards 

for AI, and provide education and training opportunities to prepare the American workforce for the new 

era of AI.

In support of the American AI Initiative, this National AI R&D Strategic Plan: 2019 Update defines the 

priority areas for Federal investments in AI R&D. This 2019 update builds upon the first National AI R&D 

Strategic Plan released in 2016, accounting for new research, technical innovations, and other 

considerations that have emerged over the past three years. This update has been developed by 

leading AI researchers and research administrators from across the Federal Government, with input 

from the broader civil society, including from many of America’s leading academic research 

institutions, nonprofit organizations, and private sector technology companies. Feedback from these 

key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also 

calling for greater attention to making AI trustworthy, to partnering with the private sector, and other 

imperatives. 

The National AI R&D Strategic Plan: 2019 Update establishes a set of objectives for Federally funded AI 

research, identifying the following eight strategic priorities:

Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of 

AI that will drive discovery and insight and enable the United States to remain a world leader in AI. 

Strategy 2: Develop effective methods for human-AI collaboration. Increase understanding of how to 

create AI systems that effectively complement and augment human capabilities.

Strategy 3: Understand and address the ethical, legal, and societal implications of AI. Research AI 

systems that incorporate ethical, legal, and societal concerns through technical mechanisms.

Strategy 4: Ensure the safety and security of AI systems. Advance knowledge of how to design AI systems 

that are reliable, dependable, safe, and trustworthy. 

Strategy 5: Develop shared public datasets and environments for AI training and testing. Develop and 

enable access to high-quality datasets and environments, as well as to testing and training resources. 

Strategy 6: Measure and evaluate AI technologies through standards and benchmarks.Develop a broad 

spectrum of evaluative techniques for AI, including technical standards and benchmarks.

Strategy 7: Better understand the national AI R&D workforce needs. Improve opportunities for R&D 

workforce development to strategically foster an AI-ready workforce.

Strategy 8: Expand public-private partnerships to accelerate advances in AI. Promote opportunities for 

sustained investment in AI R&D and for transitioning advances into practical capabilities, in 

collaboration with academia, industry, international partners, and other non-Federal entities.

 1 https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/
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Introduction to the 2019 National AI R&D Strategic Plan

Artificial intelligence enables computers and other automated systems to perform tasks that have 

historically required human cognition and what we typically consider human decision-making abilities. 

Over the past several decades, AI has advanced tremendously and today promises better, more 

accurate healthcare; enhanced national security; improved transportation; and more effective 

education, to name just a few benefits. Increased computing power, the availability of large datasets 

and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI 

development to create new sectors of the economy and revitalize industries. As more industries adopt 

AI’s fundamental technologies, the field will continue to drive profound economic impact and qualityof-life improvements worldwide. 

These advancements have been driven 

primarily by Federal investments in AI R&D, 

the expertise of America’s unsurpassed R&D 

institutions, and the collective creativity of 

many of America’s most visionary 

technology companies and entrepreneurs.

In 2016 the Federal Government published 

first National AI R&D Strategic Plan,

recognizing AI’s tremendous promise and 

need for continued advancement. It was 

developed to guide the Nation in our AI R&D 

investments, provide a strategic framework 

for improving and leveraging America’s AI 

capabilities, and ensure that those 

capabilities produce prosperity, security, 

and improved quality of life for the 

American people for years to come.

The Plan defined several key areas of 

priority focus for the Federal agencies that 

invest in AI. These focus areas, or strategies, 

include: continued long-term investments 

in AI; effective methods for human-AI 

collaboration; understanding and addressing

the ethical, legal, and societal implications 

for AI; ensuring the safety and security of AI; 

developing shared public datasets and 

environments for AI training and testing; 

measuring and evaluating AI technologies 

through standards and benchmarks; and 

better understanding the Nation’s AI R&D 

 2 https://www.nitrd.gov/news/RFI-National-AI-Strategic-Plan.aspx

3 https://www.nitrd.gov/nitrdgroups/index.php?title=AI-RFI-Responses-2018

2019 

Update

RFI responses inform the 

2019 National AI R&D Strategic Plan

In September 2018, the National Coordination Office for 

Networking and Information Technology Research and 

Development issued a Request for Information (RFI)2 on 

behalf of the Select Committee on Artificial Intelligence, 

requesting input from all interested parties on the 2016 

National Artificial Intelligence Research and 

Development Strategic Plan. Nearly 50 responses were 

submitted by researchers, research organizations, 

professional societies, civil society organizations, and 

individuals; these responses are available online.3

Many of the responses reaffirmed the analysis, 

organization, and approach outlined in the 2016

National AI R&D Strategic Plan. A significant number of 

responses noted the importance of investing in the 

application of AI in areas such as manufacturing and 

supply chains; healthcare; medical imaging; 

meteorology, hydrology, climatology, and related 

areas; cybersecurity; education; data-intensive physical 

sciences such as high-energy physics; and 

transportation. This interest in translational 

applications of AI technologies has certainly increased 

since the release of the 2016 National AI R&D Strategic 

Plan. Other common themes echoed in the RFI 

responses were the importance of developing 

trustworthy AI systems, including fairness, ethics, 

accountability, and transparency of AI systems; curated 

and accessible datasets; workforce considerations; and 

public-private partnerships for furthering AI R&D.
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workforce needs. That work was prescient: today, countries around the world have followed suit and

have issued their own versions of this plan. 

In the three years since the National AI R&D Strategic Plan was produced, new research, technical 

innovations, and real-world deployments have progressed rapidly. The Administration initiated this 

2019 update to the National AI R&D Strategic Plan to address these advancements, including a rapidly 

evolving international AI landscape.

Notably, this 2019 Update to the National AI R&D Strategic Plan is, by design, solely concerned with 

addressing the research and development priorities associated with advancing AI technologies. It does 

not describe or recommend policy or regulatory actions related to the governance or deployment of AI, 

although AI R&D will certainly inform the development of reasonable policy and regulatory frameworks.

AI as an Administration Priority

Since 2017, the Administration has addressed the importance of AI R&D by emphasizing its role for 

America’s future across multiple major policy documents, including the National Security Strategy,

4 the 

National Defense Strategy,

5 and the FY 2020 R&D Budget Priorities Memo.6

In May 2018, the Office of Science and Technology Policy (OSTP) hosted the White House Summit on 

Artificial Intelligence for American Industry to begin discussing the promise of AI and the policies 

needed to realize that promise for the American people and maintain U.S. leadership in the age of AI. 

The Summit convened over 100 senior government officials, technical experts from top academic 

institutions, heads of industrial research laboratories, and American business leaders.

In his State of the Union address on February 5, 2019, President Trump stressed the importance of 

ensuring American leadership in the development of emerging technologies, including AI, that make up 

the Industries of the Future.

On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in 

Artificial Intelligence.

7 This order launched the American AI Initiative, a concerted effort to promote and 

protect AI technology and innovation in the United States. The Initiative implements a whole-ofgovernment strategy in collaboration and engagement with the private sector, academia, the public,

and like-minded international partners. Among other actions, key directives in the Initiative call for 

Federal agencies to prioritize AI R&D investments, enhance access to high-quality cyberinfrastructure 

and data, ensure that the Nation leads in the development of technical standards for AI, and provide 

education and training opportunities to prepare the American workforce for the new era of AI.

Development of the 2019 Update to the National AI R&D Strategic Plan

The 2016 National AI R&D Strategic Plan recommended that the many Federal agencies tasked with 

advancing or adopting AI collaborate to identify critical R&D opportunities and support effective 

coordination of Federal AI R&D activities, both intramural and extramural research. Reflecting the 

Administration’s prioritization of AI, the National Science and Technology Council (NSTC) has 

established a new framework to implement this recommendation, consisting of three unique NSTC 

subgroups made up of members from across the Federal R&D agencies to cover (1) senior leadership 

4 https://www.whitehouse.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf 5 https://dod.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf 6 https://www.whitehouse.gov/wp-content/uploads/2018/07/M-18-22.pdf

7 https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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and strategic vision, (2) operational planning and tactical implementation, and (3) research and 

technical expertise. These subgroups are:

• The Select Committee on AI,

8 consisting of the heads of departments and agencies principally 

responsible for the government’s AI R&D, advises the Administration on interagency AI R&D 

priorities; considers the creation of Federal partnerships with industry and academia; establishes

structures to improve government planning and coordination of AI R&D; identifies opportunities 

to leverage Federal data and computational resources to support our national AI R&D ecosystem; 

and supports the growth of a technical, national AI workforce.

• The NSTC Subcommittee on Machine Learning and Artificial Intelligence (MLAI), consisting of 

agency AI leaders and administrators, serves as the operational and implementation arm of the 

Select Committee, responsible for fulfilling tasking from the Select Committee; creating and 

maintaining the National AI R&D Strategic Plan; identifying and addressing important policy issues 

related to AI research, testing, standards, education, implementation, outreach, and related 

areas; and related activities.

• The AI R&D Interagency Working Group, operating under the NSTC’s Networking and Information 

Technology R&D (NITRD) Subcommittee and consisting of research program managers and 

technical experts from across the Federal Government, reports to the MLAI Subcommittee; helps 

coordinate interagency AI R&D programmatic efforts; serves as the interagency AI R&D community 

of practice; and reports government-wide AI R&D spending through the NITRD Subcommittee’s 

annual Supplement to the President’s Budget.

In September 2018, the Select Committee initiated an update to the 2016 Strategic Plan, beginning with 

an RFI seeking broad community input on whether and how the seven strategies of the 2016 National AI 

R&D Strategic Plan merited revision or replacement (see sidebar). Independently, Federal departments 

and agencies performing or funding AI R&D undertook their own assessments.

An Overview of the 2019 Update to the 2016 National AI R&D Strategic Plan

Together, the Select Committee on AI, the NSTC Subcommittee on Machine Learning and AI, and the AI 

R&D Interagency Working Group of NITRD reviewed the input regarding the National AI R&D Strategic 

Plan. Each of the original seven focus areas or strategies of the 2016 Plan was reaffirmed by multiple 

respondents from the public and government, with no calls to remove any one strategy. These 

strategies, updated in this 2019 Update to the Strategic Plan to reflect the current state of the art, are:

Strategy 1: Make long-term investments in AI research;

Strategy 2: Develop effective methods for human-AI collaboration;

Strategy 3: Understand and address the ethical, legal, and societal implications of AI;

Strategy 4: Ensure the safety and security of AI systems;

Strategy 5: Develop shared public datasets and environments for AI training and testing;

Strategy 6: Measure and evaluate AI technologies through standards and benchmarks; and

Strategy 7: Better understand the national AI R&D workforce needs.

 8 https://www.whitehouse.gov/wp-content/uploads/2018/05/Summary-Report-of-White-House-AI-Summit.pdf
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Many responses to the RFI called for greater Federal Government R&D engagement with the private 

sector, given the fast rise of privately funded AI R&D, and the rapid adoption of AI by industry. As a result, 

the 2019 Update incorporates a new, eighth strategy: 

Strategy 8: Expand public-private partnerships to accelerate advances in AI.

Feedback from the public and Federal agencies identified a number of specific challenges to further AI 

development and adoption. These challenges, many of which cut across multiple agencies, provide 

enhanced insight into ways that this National AI R&D Strategic Plan can guide the course of AI R&D in 

America, and many closely relate to the themes addressed in the 2019 Executive Order on Maintaining 

American Leadership in Artificial Intelligence. Examples include the following: 

• Research at the frontiers. Even though machine learning has brought phenomenal new

capabilities in the past several years, continued research is needed to further push the frontiers of 

ML, as well as to develop additional approaches to the tough technical challenges of AI (Strategy 1).

• Positive impact. As AI capabilities grow, the United States must place increased emphasis on

developing new methods to ensure that AI’s impacts are robustly positive into the future

(Strategies 1, 3, and 4).

• Trust and explainability. Truly trustworthy AI requires explainable AI, especially as AI systems grow 

in scale and complexity; this requires a comprehensive understanding of the AI system by the

human user and the human designer (Strategies 1, 2, 3, 4, and 6).

• Safety and security. Researchers must devise methods to keep AI systems and the data they use

secure so that the Nation can leverage the opportunities afforded by this technology while also

maintaining confidentiality and safety (Strategies 4, 5, and 6).

• Technical standards. As the Nation develops techniques to expand both AI abilities and assurance, 

it must test and benchmark them; when the techniques are ready, they should be turned into

technical standards for the world (Strategy 6).

• Workforce capability. Accomplishing these goals will require growing a skilled AI R&D workforce that 

is currently limited and in high demand; the United States must be creative and bold in training and

acquiring the skilled workforce it needs to lead the world in AI research and applications (Strategy 7).

• Partnerships. Advances in AI R&D increasingly require effective partnerships between the Federal

Government and academia, industry, and other non-Federal entities to generate technological

breakthroughs in AI and to rapidly transition those breakthroughs into capabilities (Strategy 8).

• Cooperation with allies. Additionally, the Plan recognizes the importance of international

cooperation for successful implementation of these goals, while protecting the American AI R&D

enterprise from strategic competitors and adversarial nations.

Structure of this 2019 Update to the 2016 National AI R&D Strategic Plan

This updated National AI R&D Strategic Plan incorporates the original text from the 2016 version, 

including the following section on R&D Strategy (except for minor edits) and the original 2016 wording 

of the first seven strategies. For each strategy, 2019 updates to the 2016 National R&D Strategic Plan are 

provided in shaded boxes at the top of the original seven strategies; these highlight updated imperatives 

and/or new focus areas for the strategies. Text below the shaded boxes is as it originally appeared in the 

2016 National AI R&D Strategic Plan, providing observations and context that remain important today

(note that some of the original details may have become out of date in the intervening period). In 

addition, as noted previously, a new eighth strategy is added in this 2019 Update, on expanding publicprivate partnerships in AI R&D.
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AI R&D Strategy

The research priorities outlined in this AI R&D Strategic Plan focus on areas that industry is unlikely to 

address on their own, and thus, areas that are most likely to benefit from Federal investment. These 

priorities cut across all of AI to include needs common to the AI sub-fields of perception, automated 

reasoning/planning, cognitive systems, machine learning, natural language processing, robotics, and 

related fields. Because of the breadth of AI, these priorities span the entire field, rather than only 

focusing on individual research challenges specific to each sub-domain. To implement the plan, 

detailed roadmaps should be developed that address the capability gaps consistent with the plan. 

One of the most important Federal research priorities, outlined in Strategy 1, is for sustained long-term 

research in AI to drive discovery and insight. Many of the investments by the U.S. Federal Government 

in high-risk, high-reward9 fundamental research have led to revolutionary technological advances we

depend on today, including the Internet, GPS, smartphone speech recognition, heart monitors, solar 

panels, advanced batteries, cancer therapies, and much, much more. The promise of AI touches nearly 

every aspect of society and has the potential for significant positive societal and economic benefits. 

Thus, to maintain a world leadership position in this area, the United States must focus its investments 

on high-priority fundamental and long-term AI research.

Many AI technologies will work with and alongside humans, thus leading to important challenges in 

how to best create AI systems that work with people in intuitive and helpful ways.10 The walls between 

humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing 

human capabilities. Fundamental research is needed to develop effective methods for human-AI 

interaction and collaboration, as outlined in Strategy 2. 

AI advancements are providing many positive benefits to society and are increasing U.S. national 

competitiveness.11 However, as with most transformative technologies, AI presents some societal risks 

in several areas, from jobs and the economy to safety, ethical, and legal questions. Thus, as AI science 

and technology develop, the Federal Government must also invest in research to better understand 

what the implications are for AI for all these realms, and to address these implications by developing AI 

systems that align with ethical, legal, and societal goals, as outlined in Strategy 3.

A critical gap in current AI technology is a lack of methodologies to ensure the safety and predictable 

performance of AI systems. Ensuring the safety of AI systems is a challenge because of the unusual 

complexity and evolving nature of these systems. Several research priorities address this safety 

challenge. First, Strategy 4 emphasizes the need for explainable and transparent systems that are 

trusted by their users, perform in a manner that is acceptable to the users, and can be guaranteed to 

act as the user intended. The potential capabilities and complexity of AI systems, combined with the 

wealth of possible interactions with human users and the environment, makes it critically important to 

invest in research that increases the security and control of AI technologies. Strategy 5 calls on the 

Federal Government to invest in shared public datasets for AI training and testing to advance the

progress of AI research and to enable a more effective comparison of alternative solutions.

Strategy 6 discusses how standards and benchmarks can focus R&D to define progress, close gaps, and

drive innovative solutions for specific problems and challenges. Standards and benchmarks are 

 9 “High-risk, high-reward” research refers to visionary research that is intellectually challenging but has the potential 

to make deeply positive, transformative impacts on the field of study. 10 See 2016 Report of the One Hundred Year Study on Artificial Intelligence, which focuses on the anticipated uses and 

impacts of AI in the year 2030; https://ai100.stanford.edu/2016-report. 11 J. Furman, “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence,” Council of Economic 

Advisors remarks, New York University: AI Now Symposium, July 7, 2016.
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essential for measuring and evaluating AI systems and ensuring that AI technologies meet critical 

objectives for functionality and interoperability.

Finally, the growing prevalence of AI technologies across all sectors of society creates new pressures 

for AI R&D experts. Opportunities abound for core AI scientists and engineers with a deep 

understanding of the technology who can generate new ideas for advancing the boundaries of 

knowledge in the field. The Nation should take action to ensure a sufficient pipeline of AI-capable 

talent. Strategy 7 addresses this challenge.

Figure 1 (updated in this 2019 version of the Plan) provides a graphical illustration of the overall 

organization of this AI R&D Strategic Plan. Across the bottom row of boxes are the crosscutting, 

underlying foundations that affect the development of all AI systems; these foundations are described 

in Strategies 3-7 and the new Strategy 8. The next layer higher (middle row of boxes) includes many 

areas of research that are needed to advance AI. These R&D areas (including use-inspired basic 

research) are outlined in Strategies 1-2.12 Across the top row of boxes in the graphic are examples of 

applications that are expected to benefit from advances in AI. Together, these components of the AI 

R&D Strategic Plan define a high-level framework for Federal investments that can lead to impactful 

advances in the field and positive societal benefits.

Figure 1. Organization of the AI R&D Strategic Plan (2019 update,to include Strategy 8). A combination of 

crosscutting R&D foundations (in the lower row) are important for all AI research. Many AI R&D areas (in the 

middle row) can build upon these crosscutting foundations to impact a wide array of societal applications 

(in the top row). The numbers in brackets indicate the number of the Strategy in this plan that further 

develops each topic. The ordering of these strategies does not indicate a priority of importance.

 12 Throughout this document, “basic research” includes both pure basic research and use-inspired basic research—

the so-called Pasteur’s Quadrant defined by Donald Stokes in his 1997 book of the same name—referring to basic 

research that has use for society in mind. For example, the fundamental NIH investments in IT are often called useinspired basic research.
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Strategy 1: Make Long-Term Investments in AI Research

2019 

Update Sustaining long-term investments in fundamental AI research

Since the release of the 2016 National 

AI R&D Strategic Plan, powerful new 

capabilities, primarily ML applications 

to well-defined tasks, have continued 

to emerge. These capabilities have 

demonstrated impacts in a diverse 

array of applications, such as 

classifying genetic sequences,20,21

managing limited wireless spectrum 

resources,22 interpreting medical 

images,23 and grading cancers.24 These 

rapid advances required decades of 

research for the technologies and 

applications to mature.25 To maintain 

this progress in ML to achieve 

advancements in other areas of AI, and 

to strive toward the long-term goal of

general-purpose AI, the Federal

Government must continue to foster 

long-term, fundamental research in ML 

and AI. This research will give rise to 

transformational technologies and, in 

turn, breakthroughs across all sectors 

of society.

Much of the current progress in the 

field has been in specialized, welldefined tasks often driven by 

statistical ML, such as classification, 

recognition, and regression (i.e., 

“narrow AI systems”). Surveys of the 

Long-term, fundamental AI research: 

Recent agency R&D programs

Since the release of the 2016 National AI R&DStrategic Plan, 

a number of agencies have initiated AI R&D programs for 

Strategy 1:

 NSF has continued to fund foundational research in AI, 

spanning ML, reasoning and representation, computer 

vision, computational neuroscience, speech and 

language, robotics, and multi-agent systems. NSF has 

launched new joint funding opportunities with other 

agencies—notably with DARPA in the area of highperformance, energy-efficient hardware for real-time 

ML13 and with USDA-NIFA on AI for agricultural 

science14—and with industry.15,16 In addition, NSF’s 

Harnessing the Data Revolution Big Idea17 supports 

research on the foundations of data science, which will 

serve as a driver of future ML and AI systems. 

 DARPA announced in September 2018 a multiyear 

investment in new and existing programs called the “AI 

Next” campaign.18 Key campaign areas include 

improving the robustness and reliability of AI systems; 

enhancing the security and resiliency of ML/AI

technologies; reducing power, data, and performance 

inefficiencies; and pioneering the next generation of AI 

algorithms and applications, such as explainability and 

commonsense reasoning.

 The NIH Strategic Plan for Data Science19 of September 

2018 aims to advance access to data science technology 

and ML/AI capability for the biomedical research 

community toward data-driven healthcare research.

 13 https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505640&org=NSF 14 https://www.nsf.gov/pubs/2019/nsf19051/nsf19051.jsp 15 https://www.nsf.gov/pubs/2019/nsf19018/nsf19018.jsp 16 https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505651 17 https://www.nsf.gov/cise/harnessingdata/ 18 https://www.darpa.mil/work-with-us/ai-next-campaign 19 https://datascience.nih.gov/strategicplan 20 https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html 21 https://irp.nih.gov/catalyst/v26i4/machine-learning 22 https://www.spectrumcollaborationchallenge.com/ 23 https://news-medical.net/news/20190417/Workshop-explores-the-future-of-artificial-intelligence-in-medical-imaging.aspx 24 https://www.nature.com/articles/nature21056 25 https://www.nitrd.gov/rfi/ai/2018/AI-RFI-Response-2018-Yolanda-Gil-AAAI.pdf
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field have noted that long-term investments in fundamental research are needed to continue 

building on these advances in ML. Further, parallel sustained efforts are required to fully realize the 

vision of “general-purpose AI”—systems that exhibit the flexibility and versatility of human 

intelligence in a broad range of cognitive domains.26,27,28,29

Emphasis is needed on the development of further ML capabilities to interactively and persistently 

learn, the connection between perception and attention, and the incorporation of learned models 

into comprehensive reasoning architectures.30 Beyond ML, critical research is also needed in other 

core areas of AI, including in commonsense reasoning and problem solving, probabilistic reasoning, 

combinatorial optimization, knowledge representation, planning and scheduling, natural language 

processing, decision making, and human-machine interaction. Advances in these areas will in turn 

enable collaborative robotics and shared and fully autonomous systems (see Strategy 2). The grand 

challenge of understanding human intelligence requires significant investments in shared resources 

and infrastructure.

25 Broad consensus exists for foundational investments in drivers of ML and AI as 

well, including data provenance and quality, novel software and hardware paradigms and platforms, 

and the security of AI systems.31,32 For example, as AI software performs increasingly complex 

functions in all aspects of daily life and all sectors of the economy, existing software development 

paradigms will need to evolve to meet software productivity, quality, and sustainability requirements.

Recent Federal investments have prioritized these areas of fundamental ML and AI research (see 

sidebar) as well as the use of ML and AI across numerous application sectors, including defense, 

security, energy, transportation, health, agriculture, and telecommunications. Ultimately, AI 

technologies are critical for addressing a range of long-term challenges, such as constructing

advanced healthcare systems, a robust intelligent transportation system, and resilient energy and 

telecommunication networks.

For AI applications to become widespread, they must be explainable and understandable (see 

Strategy 3). These challenges are particularly salient for fostering collaborative human-AI 

relationships (see Strategy 2). Today, the ability to understand and analyze the decisions of AI 

systems and measure their accuracy, reliability, and reproducibility is limited. Sustained R&D 

investments are needed to advance trust in AI systems to ensure they meet society’s needs and 

adequately address requirements for robustness, fairness, explainability, and security.

A long-term commitment to AI R&D is essential to continue and expand current technical advances 

and more broadly ensure that AI enriches the human experience. Indeed, the 2019 Executive Order 

on Maintaining American Leadership in Artificial Intelligence notes:

Heads of implementing agencies that also perform or fund R&D (AI R&D agencies), shall 

consider AI as an agency R&D priority, as appropriate to their respective agencies’ 

missions... Heads of such agencies shall take this priority into account when developing 

budget proposals and planning for the use of funds in Fiscal Year 2020 and in future years. 

Heads of these agencies shall also consider appropriate administrative actions to increase 

focus on AI for 2019.

 26 https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai100report10032016fnl_singles.pdf 27 http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf 28 https://cra.org/ccc/visioning/visioning-activities/2018-activities/artificial-intelligence-roadmap/ 29 https://www.microsoft.com/en-us/research/research-area/artificial-intelligence/ 30 https://cra.org/ccc/events/artificial-intelligence-roadmap-workshop-3-learning-and-robotics/ 31 https://cra.org/ccc/wp-content/uploads/sites/2/2016/04/AI-for-Social-Good-Workshop-Report.pdf 32 https://openai.com/blog/ai-and-compute/
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AI research investments are needed in areas with potential long-term payoffs. While an important 

component of long-term research is incremental research with predictable outcomes, long-term 

sustained investments in high-risk research can lead to high-reward payoffs. These payoffs can be seen 

in 5 years, 10 years, or more. A 2012 National Research Council report emphasizes the critical role of 

Federal investments in long-term research, noting “the long, unpredictable incubation period—

requiring steady work and funding—between initial exploration and commercial deployment.”33 It 

further notes that “the time from first concept to successful market is often measured in decades.”Welldocumented examples of sustained fundamental research efforts that led to high-reward payoffs 

include the World Wide Web and deep learning. In both cases, the basic foundations began in the 1960s; 

it was only after 30+ years of continued research efforts that these ideas materialized into the 

transformative technologies witnessed today in many categories of AI. 

The following subsections highlight some of these areas. Additional categories of important AI research 

are discussed in Strategies 2 through 6.

Advancing data-focused methodologies for knowledge discovery

As discussed in the 2016 Federal Big Data Research and Development Strategic Plan,

34 many 

fundamental new tools and technologies are needed to achieve intelligent data understanding and 

knowledge discovery. Further progress is needed in the development of more advanced machine 

learning algorithms that can identify all the useful information hidden in big data. Many open research 

questions revolve around the creation and use of data, including its veracity and appropriateness for AI 

system training. The veracity of data is particularly challenging when dealing with vast amounts of data, 

making it difficult for humans to assess and extract knowledge from it. While much research has dealt 

with veracity through data quality assurance methods to perform data cleaning and knowledge 

discovery, further study is needed to improve the efficiency of data cleaning techniques, to create 

methods for discovering inconsistencies and anomalies in the data, and to develop approaches for 

incorporating human feedback. Researchers need to explore new methods to enable data and 

associated metadata to be mined simultaneously. 

Many AI applications are interdisciplinary in nature and make use of heterogeneous data. Further 

investigation of multimodality machine learning is needed to enable knowledge discovery from a wide 

variety of different types of data (e.g., discrete, continuous, text, spatial, temporal, spatio-temporal, 

graphs). AI investigators must determine the amount of data needed for training and to properly 

address large-scale versus long-tail data needs. They must also determine how to identify and process 

rare events beyond purely statistical approaches; to work with knowledge sources (i.e., any type of 

information that explains the world, such as knowledge of the law of gravity or of social norms) as well 

as data sources, integrating models and ontologies in the learning process; and to obtain effective 

learning performance with little data when big data sources may not be available. 

Enhancing the perceptual capabilities of AI systems

Perception is an intelligent system’s window into the world. Perception begins with (possibly 

distributed) sensor data, which comes in diverse modalities and forms, such as the status of the system 

itself or information about the environment. Sensor data are processed and fused, often along with 

a priori knowledge and models, to extract information relevant to the AI system’s task such as 

 33 National Research Council Computer Science Telecommunications Board, Continuing Innovation in Information 

Technology (The National Academies Press, Washington, D.C., 2012), 11; https://doi.org/10.17226/13427.

34 https://www.nitrd.gov/PUBS/bigdatardstrategicplan.pdf
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geometric features, attributes, location, and velocity. Integrated data from perception forms 

situational awareness to provide AI systems with the comprehensive knowledge and a model of the 

state of the world necessary to plan and execute tasks effectively and safely. AI systems would greatly 

benefit from advancements in hardware and algorithms to enable more robust and reliable perception. 

Sensors must be able to capture data at longer distances, with higher resolution, and in real time. 

Perception systems need to be able to integrate data from a variety of sensors and other sources, 

including the computational cloud, to determine what the AI system is currently perceiving and to allow 

the prediction of future states. Detection, classification, identification, and recognition of objects 

remain challenging, especially under cluttered and dynamic conditions. In addition, perception of 

humans must be greatly improved by using an appropriate combination of sensors and algorithms, so 

that AI systems can work more effectively with people.10 A framework for calculating and propagating 

uncertainty throughout the perception process is needed to quantify the confidence level that the AI 

system has in its situational awareness and to improve accuracy.

Understanding theoretical capabilities and limitations of AI 

While the ultimate goal for many AI algorithms is to address open challenges with human-like solutions, 

we do not have a good understanding of what the theoretical capabilities and limitations are for AI and 

the extent to which such human-like solutions are even possible with AI algorithms. Theoretical work 

is needed to better understand why AI techniques—especially machine learning—often work well in 

practice. While different disciplines (including mathematics, control sciences, and computer science) 

are studying this issue, the field currently lacks unified theoretical models or frameworks to understand 

AI system performance. Additional research is needed on computational solvability, which is an 

understanding of the classes of problems that AI algorithms are theoretically capable of solving, and 

likewise, those that they are not capable of solving. This understanding must be developed in the 

context of existing hardware, in order to see how the hardware affects the performance of these 

algorithms. Understanding which problems are theoretically unsolvable can lead researchers to 

develop approximate solutions to these problems, or even open up new lines of research on new 

hardware for AI systems. For example, when invented in the 1960s, Artificial Neural Networks (ANNs) 

could only be used to solve very simple problems. It only became feasible to use ANNs to solve complex 

problems after hardware improvements such as parallelization were made, and algorithms were 

adjusted to make use of the new hardware. Such developments were key factors in enabling today’s 

significant advances in deep learning.

Pursuing research on general-purpose artificial intelligence

AI approaches can be divided into “narrow AI” and “general AI.” Narrow AI systems perform individual 

tasks in specialized, well-defined domains, such as speech recognition, image recognition, and 

translation. Several recent, highly-visible, narrow AI systems, including IBM Watson and DeepMind’s 

AlphaGo, have achieved major feats.35,36 Indeed, these particular systems have been labeled 

“superhuman” because they have outperformed the best human players in Jeopardy! and Go, 

respectively. But these systems exemplify narrow AI, since they can only be applied to the tasks for 

which they are specifically designed. Using these systems on a wider range of problems requires a 

significant re-engineering effort. In contrast, the long-term goal of general AI is to create systems that 

 35 In 2011, IBM Watson defeated two players considered among the best human players in the Jeopardy! game.

36 In 2016, AlphaGo defeated the reigning world champion of Go, Lee Se-dol. Notably, AlphaGo combines deep 

learning and Monte Carlo search—a method developed in the 1980s—which itself builds on a probabilistic method 

discovered in the 1940s.
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exhibit the flexibility and versatility of human intelligence in a broad range of cognitive domains, 

including learning, language, perception, reasoning, creativity, and planning. Broad learning 

capabilities would provide general AI systems the ability to transfer knowledge from one domain to 

another and to interactively learn from experience and from humans. General AI has been an ambition 

of researchers since the advent of AI, but current systems are still far from achieving this goal. The 

relationship between narrow and general AI is currently being explored; it is possible that lessons from 

one can be applied to improve the other and vice versa. While there is no general consensus, most AI 

researchers believe that general AI is still decades away, requiring a long-term, sustained research 

effort to achieve it.

Developing scalable AI systems

Groups and networks of AI systems may be coordinated or autonomously collaborate to perform tasks 

not possible with a single AI system, and may also include humans working alongside or leading the 

team. The development and use of such multi-AI systems creates significant research challenges in 

planning, coordination, control, and scalability of such systems. Planning techniques for multi-AI

systems must be fast enough to operate and adapt in real time to changes in the environment. They 

should adapt in a fluid manner to changes in available communications bandwidth or system 

degradation and faults. Many prior efforts have focused on centralized planning and coordination 

techniques; however, these approaches are subject to single points of failure, such as the loss of the 

planner, or loss of the communications link to the planner. Distributed planning and control techniques

are harder to achieve algorithmically, and are often less efficient and incomplete, but potentially offer 

greater robustness to single points of failure. Future research must discover more efficient, robust, and 

scalable techniques for planning, control, and collaboration of teams of multiple AI systems and humans.

Fostering research on human-like AI

Attaining human-like AI requires systems to explain themselves in ways that people can understand.

This will result in a new generation of intelligent systems, such as intelligent tutoring systems and 

intelligent assistants that are effective in assisting people when performing their tasks. There is a 

significant gap, however, between the way current AI algorithms work and how people learn and 

perform tasks. People are capable of learning from just a few examples, or by receiving formal 

instruction and/or “hints” to performing tasks, or by observing other people performing those tasks.

Medical schools take this approach, for example, when medical students learn by observing an 

established doctor performing a complex medical procedure. Even in high-performance tasks such as 

world-championship Go games, a master-level player would have played only a few thousand games

to train him/herself. In contrast, it would take hundreds of years for a human to play the number of 

games needed to train AlphaGo. More foundational research on new approaches for achieving humanlike AI would bring these systems closer to this goal.

Developing more capable and reliable robots

Significant advances in robotic technologies over the last decade are leading to potential impacts in a 

multiplicity of applications, including manufacturing, logistics, medicine, healthcare, defense and 

national security, agriculture, and consumer products. While robots were historically envisioned for 

static industrial environments, recent advances involve close collaborations between robots and 

humans. Robotics technologies are now showing promise in their ability to complement, augment, 

enhance, or emulate human physical capabilities or human intelligence. However, scientists need to 

make these robotic systems more capable, reliable, and easy-to-use.
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Researchers need to better understand robotic perception to extract information from a variety of 

sensors to provide robots with real-time situational awareness. Progress is needed in cognition and 

reasoning to allow robots to better understand and interact with the physical world. An improved 

ability to adapt and learn will allow robots to generalize their skills, perform self-assessment of their 

current performance, and learn a repertoire of physical movements from human teachers. Mobility and 

manipulation are areas for further investigation so that robots can move across rugged and uncertain 

terrain and handle a variety of objects dexterously. Robots need to learn to team together in a seamless 

fashion and collaborate with humans in a way that is trustworthy and predictable.

Advancing hardware for improved AI

While AI research is most commonly associated with advances in software, the performance of AI 

systems has been heavily dependent on the hardware upon which it runs. The current renaissance in 

deep machine learning is directly tied to progress in GPU-based hardware technology and its improved 

memory,37 input/output, clock speeds, parallelism, and energy efficiency. Developing hardware 

optimized for AI algorithms will enable even higher levels of performance than GPUs. One example is 

“neuromorphic” processors that are loosely inspired by the organization of the brain and, in some 

cases, optimized for the operation of neural networks.

38

Hardware advances can also improve the performance of AI methods that are highly data-intensive. 

Further study of methods to turn on and off data pipelines in controlled ways throughout a distributed 

system is called for. Continued research is also needed to allow machine learning algorithms to 

efficiently learn from high-velocity data, including distributed machine learning algorithms that 

simultaneously learn from multiple data pipelines. More advanced machine learning-based feedback 

methods will allow AI systems to intelligently sample or prioritize data from large-scale simulations, 

experimental instruments, and distributed sensor systems, such as Smart Buildings and the Internet of 

Things (IoT). Such methods may require dynamic I/O decision-making, in which choices are made in 

real time to store data based on importance or significance, rather than simply storing data at fixed 

frequencies.

Creating AI for improved hardware

While improved hardware can lead to more capable AI systems, AI systems can also improve the 

performance of hardware.39 This reciprocity will lead to further advances in hardware performance, 

since physical limits on computing require novel approaches to hardware designs.40 AI-based methods 

could be especially important for improving the operation of high-performance computing (HPC) 

systems. Such systems consume vast quantities of energy. AI is being used to predict HPC performance 

and resource usage, and to make online optimization decisions that increase efficiency; more 

advanced AI techniques could further enhance system performance. AI can also be used to create 

 37 GPU stands for graphics processing unit, which is a power- and cost-efficient processor incorporating hundreds of 

processing cores; this design makes it especially well suited for inherently parallel applications, including most AI 

systems. 38 Neuromorphic computing refers to the ability of hardware to learn, adapt, and physically reconfigure, taking 

inspiration from biology or neuroscience. 39 M. Milano and L. Benini, “Predictive Modeling for Job Power Consumption in HPC Systems,” In Proceedings of High 

Performance Computing: 31st International Conference, ISC High Performance 2016 (Springer Vol. 9697, 2016).

40 These physical limits on computing are called Dennard scaling, and lead to high on-chip power densities and the 

phenomenon called “dark silicon”, where different parts of a chip need to be turned off in order to limit 

temperatures and ensure data integrity.
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self-reconfigurable HPC systems that can handle system faults when they occur, without human 

intervention.41

Improved AI algorithms can increase the performance of multi-core systems by reducing data 

movements between processors and memory—the primary impediment to exascale computing 

systems that operate 10 times faster than today’s supercomputers.42 In practice, the configuration of 

executions in HPC systems are never the same, and different applications are executed concurrently, 

with the state of each different software code evolving independently in time. AI algorithms need to be 

designed to operate online and at scale for HPC systems.

 41 A. Cocaña-Fernández, J. Ranilla, and L. Sánchez, “Energy-efficient allocation of computing node slots in HPC 

clusters through parameter learning and hybrid genetic fuzzy system modeling,” Journal of Supercomputing 71 

(2015):1163-1174. 42 Exascale computing systems can achieve at least a billion billion calculations per second.
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Strategy 2: Develop Effective Methods for Human-AI Collaboration

2019 

Update

Developing AI systems that complement and augment human capabilities, 

with increasing focus on the future of work

Since the release of the 2016 National AI 

R&D Strategic Plan, national interest has 

grown in human-AI collaboration. When AI 

systems complement and augment human 

capabilities, humans and AI become

partners across a range of shared to fully 

autonomous scenarios. In particular, 

human-AI collaboration has been elevated 

as both a challenge and an opportunity in 

the context of the future of work. 

In the past three years, newly established 

as well as longstanding conferences, 

workshops, and task forces have 

prioritized human-AI collaboration 

broadly. For example, the Conference on 

Human Computation and Crowdsourcing

has grown from a workshop to a major 

international conference that fosters 

research in the intersection of AI and 

human-computer interaction (HCI).45 In 

2018, the Association for the Advancement 

of Artificial Intelligence selected human-AI 

collaboration as the emerging topic for its 

annual conference.46 In May 2019, the 

largest conference on human-computer 

interaction, CHI, included a workshop on

“Bridging the Gap Between AI and HCI.”47

The journal Human-Computer Interaction

put out a call in March 2019 for 

submissions for a special issue on 

“unifying human-computer interaction 

and artificial intelligence.”48

Human-AI Collaboration: 

Recent agency R&D programs

Since the release of the 2016 National AI R&D Strategic 

Plan, severalagencies have initiatedefforts forStrategy 2:

 NSF’s Future of Work at the Human-Technology 

Frontier43 Big Idea is supporting socio-technical 

research enabling a future where intelligent 

technologies collaborate synergistically with 

humans to achieve broad participation in the 

workforce and improve the social, economic, and 

environmental benefits across a range of work 

settings.

 NOAA (National Oceanographic and Atmospheric 

Administration) is advancing human-AI 

collaboration for hurricane, tornado, and other 

severe weather predictions where the human 

forecaster and an AI system work together to 

improve severe weather warning generation and 

to identify distinct patterns that are precursors to 

extreme events. Sometimes referred to as “humans

above the loop,” human forecasters oversee the AI 

system's predictions and direct the outcomes. 

 NIH has ongoing research in natural language 

processing based on a database of 96.3 million 

facts extracted from all MEDLINE citations 

maintained by the National Library of Medicine.

 A 2019 DOE workshop report on Scientific Machine 

Learning identified priority research directions, 

major scientific use cases, and the emerging trend 

that human-AI collaborations will transform the 

way science is done.44

 43 https://www.nsf.gov/eng/futureofwork.jsp 44 DOE workshop report, Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial 

Intelligence: https://www.osti.gov/biblio/1478744. 45 Welcome to HCOMP 2019: https://www.humancomputation.com/. 46 AAAI-18 Emerging Topic Human-AI Collaboration:

http://www.aaai.org/Conferences/AAAI/2018/aaai18emergingcall.php. 47 Where is the Human? Bridging the Gap Between AI and HCI: CHI 2019 Workshop: https://michae.lv/ai-hci-workshop/. 48 Call: “Unifying Human Computer Interaction and Artificial Intelligence” issue of Human-Computer Interaction: 

https://ispr.info/2019/02/20/call-unifying-human-computer-interaction-and-artificial-intelligence-issue-of-humancomputer-interaction/.
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In the context of work, conferences have emerged exploring the role of the human, the machine, and 

their partnership, such as MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the

Initiative on the Digital Economy that launched the Annual AI and the Future of Work Congress.

49,50

As part of A 20-Year Community Roadmap for Artificial Intelligence Research in the U.S.,

51 in 2019 the 

Computing Community Consortium (CCC) held a workshop focused on meaningful interaction 

between humans and AI systems.52 Additionally, the CCC operated the Human Technology Frontier 

task force in 2017-2018 to focus on the potential of technology to augment human performance in, 

including but not limited to, the workplace, the classroom, and the healthcare system.53

The cross-strategy principle in the 2016 National AI R&D Strategic Plan, “appropriate trust of AI 

systems requires explainability, especially as the AI grows in scale and complexity,” has seen an R&D 

call to action in the context of human-AI collaborations. This principle has been identified by a 

number of professional societies and agencies as a priority area (see sidebar). This research area 

reflects the intersection of Strategies 2 and 3, as explainability, fairness, and transparency are key 

principles for AI systems to effectively collaborate with humans. Likewise, the challenge of 

understanding and designing human-AI ethics and value alignment into systems remains an open 

research area. In parallel, the private sector has responded with principles for effective human-AI 

collaboration.54,55

As Federal agencies have increased AI investments in the past three years along mission objectives, 

they have shared a common emphasis on human-machine cognition, autonomy, and agency, such 

as in decision support, risk modeling, situational awareness, and trusted machine intelligence (see 

sidebar). Through such R&D investments, research partnerships are growing across a number of 

axes, bringing together computational scientists; behavioral, cognitive, and psychological 

scientists; and scientists and engineers from other domains. New collaborations have formed 

between academic researchers and users of AI systems inside and outside the workplace.

Moving forward, it is critical that Federal agencies continue to foster AI R&D in the open world to 

promote the design of AI systems that incorporate and accommodate the situations and goals of users

so that AI systems and users can work collaboratively in both anticipated and unanticipated 

circumstances.

While completely autonomous AI systems will be important in some application domains (e.g., 

underwater or deep space exploration), many other application areas (e.g., disaster recovery and 

medical diagnostics) are most effectively addressed by a combination of humans and AI systems 

working together to achieve application goals. This collaborative interaction takes advantage of the 

complementary nature of humans and AI systems. While effective approaches for human-AI 

collaboration already exist, most of these are “point solutions” that only work in specific environments 

using specific platforms toward specific goals. Generating point solutions for every possible application 

instance does not scale; more work is thus needed to go beyond these point solutions toward more 

 49 https://futureofwork.csail.mit.edu/. 50 AI and Future of Work Innovation Summit 2019: https://analyticsevent.com/. 51 https://cra.org/ccc/wp-content/uploads/sites/2/2019/03/AI_Roadmap_Exec_Summary-FINAL-.pdf 52 Artificial Intelligence Roadmap Workshop 2 – Interaction: https://cra.org/ccc/events/artificial-intelligenceroadmap-workshop-2-interaction/. 53 https://cra.org/ccc/human-technology-frontier/ 54 https://www.microsoft.com/en-us/research/uploads/prod/2019/01/Guidelines-for-Human-AI-Interaction-camera-ready.pdf

55 https://www.partnershiponai.org/about/#our-work
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general methods of human-AI collaboration. The tradeoffs must be explored between designing 

general systems that work in all types of problems, requiring less human effort to build and greater 

facility for switching between applications, versus building a large number of problem-specific systems 

that may work more effectively for each problem. 

Future applications will vary considerably in the functional role divisions between humans and AI 

systems, the nature of the interactions between humans and AI systems, the number of humans and 

other AI systems working together, and how humans and AI systems will communicate and share 

situational awareness. Functional role divisions between humans and AI systems typically fall into one 

of the following categories: 

1. AI performs functions alongside the human: AI systems perform peripheral tasks that support the 

human decision maker. For example, AI can assist humans with working memory, short or longterm memory retrieval, and prediction tasks. 

2. AI performs functions when the human encounters high cognitive overload: AI systems perform 

complex monitoring functions (such as ground proximity warning systems in aircraft), decision 

making, and automated medical diagnoses when humans need assistance.

3. AI performs functions in lieu of a human: AI systems perform tasks for which humans have very 

limited capabilities, such as for complex mathematical operations, control guidance for dynamic 

systems in contested operational environments, aspects of control for automated systems in 

harmful or toxic environments, and in situations where a system should respond very rapidly (e.g., 

in nuclear reactor control rooms).

Achieving effective interactions between humans and AI systems requires additional R&D to ensure that 

the system design does not lead to excessive complexity, undertrust, or overtrust. The familiarity of 

humans with AI systems can be increased through training and experience, to ensure that the human 

has a good understanding of the AI system’s capabilities and what the AI system can and cannot do. To 

address these concerns, certain human-centered automation principles should be used in the design 

and development of these systems:56

1. Employ intuitive, user-friendly design of human-AI system interfaces, controls, and displays.

2. Keep the operator informed. Display critical information, states of the AI system, and changes to 

these states.

3. Keep the operator trained. Engage in recurrent training for general knowledge, skills, and abilities 

(KSAs), as well as training in algorithms and logic employed by AI systems and the expected failure 

modes of the system.

4. Make automation flexible. Deploying AI systems should be considered as a design option for 

operators who wish to decide whether they want to use them or not. Also important is the design 

and deployment of adaptive AI systems that can be used to support human operators during 

periods of excessive workload or fatigue.57,58

Many fundamental challenges arise for researchers when creating systems that work effectively with 

humans. Several of these important challenges are outlined in the following subsections.

 56 C. Wickens and J. G. Hollands, “Attention, time-sharing, and workload.” In Engineering, Psychology and Human 

Performance (London: Pearson PLC, 1999), 439-479. 57 https://www.nasa.gov/mission_pages/SOFIA/index.html 58 https://cloud1.arc.nasa.gov/intex-na/
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Seeking new algorithms for human-aware AI

Over the years, AI algorithms have become able to solve problems of increasing complexity. However, 

there is a gap between the capabilities of these algorithms and the usability of these systems by 

humans. Human-aware intelligent systems are needed that can interact intuitively with users and 

enable seamless machine-human collaborations. Intuitive interactions include shallow interactions, 

such as when a user discards an option recommended by the system; model-based approaches that 

take into account the users’ past actions; or even deep models of user intent that are based upon 

accurate human cognitive models. Interruption models must be developed that allow an intelligent 

system to interrupt the human only when necessary and appropriate. Intelligent systems should also 

have the ability to augment human cognition, knowing which information to retrieve when the user 

needs it, even when they have not prompted the system explicitly for that information. Future 

intelligent systems must be able to account for human social norms and act accordingly. Intelligent 

systems can more effectively work with humans if they possess some degree of emotional intelligence, so 

that they can recognize their users’ emotions and respond appropriately. An additional research goal 

is to go beyond interactions of one human and one machine, toward a “systems-of-systems”, that is, 

teams composed of multiple machines interacting with multiple humans.

Human-AI system interactions have a wide range of objectives. AI systems need the ability to represent 

a multitude of goals, actions that they can take to reach those goals, constraints on those actions, and 

other factors, as well as easily adapt to modifications in the goals. In addition, humans and AI systems 

must share common goals and have a mutual understanding of them and relevant aspects of their 

current states. Further investigation is needed to generalize these facets of human-AI systems to 

develop systems that require less human engineering. 

Developing AI techniques for human augmentation

While much of the prior focus of AI research has been on algorithms that match or outperform people 

performing narrow tasks, more work is needed to develop systems that augment human capabilities 

across many domains. Human augmentation research includes algorithms that work on a stationary 

device (such as a computer); wearable devices (such as smart glasses); implanted devices (such as brain 

interfaces); and in specific user environments (such as specially tailored operating rooms). For example, 

augmented human awareness could enable a medical assistant to point out a mistake in a medical 

procedure, based on data readings combined from multiple devices. Other systems could augment 

human cognition by helping the user recall past experiences applicable to the user’s current situation. 

Another type of collaboration between humans and AI systems involves active learning for intelligent 

data understanding. In active learning, input is sought from a domain expert and learning is only 

performed on data when the learning algorithm is uncertain. This is an important technique to reduce 

the amount of training data that needs to be generated in the first place, or the amount that needs to 

be learned. Active learning is also a key way to obtain domain expert input and increase trust in the 

learning algorithm. Active learning has so far only been used within supervised learning; further research 

is needed to incorporate active learning into unsupervised learning (e.g., clustering, anomaly detection) 

and reinforcement learning.59 Probabilistic networks allow domain knowledge to be included in the form 

of prior probability distributions. General ways of allowing machine learning algorithms to incorporate 

domain knowledge must be sought, whether in the form of mathematical models, text, or others. 

 59 While supervised learning requires humans to provide the ground-truth answers, reinforcement learning and 

unsupervised learning do not.
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Developing techniques for visualization and human-AI interfaces 

Better visualization and user interfaces are additional areas that need much greater development to 

help humans understand large-volume modern datasets and information coming from a variety of 

sources. Visualization and user interfaces must clearly present increasingly complex data and 

information derived from them in a human-understandable way. Providing real-time results is 

important in safety-critical operations and may be achieved with increasing computational power and 

connected systems. In these types of situations, users need visualization and user interfaces that can 

quickly convey the correct information for real-time response.

Human-AI collaboration can be applied in a wide variety of environments, and where there are 

constraints on communication. In some domains, human-AI communication latencies are low and 

communication is rapid and reliable. In other domains (e.g., NASA’s deployment of the rovers Spirit and 

Opportunity to Mars), remote communication between humans and the AI system has a very high 

latency (e.g., round trip times of 5-20 minutes between Earth and Mars), thus requiring the deployed 

platform(s) to operate largely autonomously, with only high-level strategic goals communicated to the 

platform. These communications requirements and constraints are important considerations for the 

R&D of user interfaces.

Developing more effective language processing systems

Enabling people to interact with AI systems through spoken and written language has long been a goal 

of AI researchers. While significant advances have been made, considerable open research challenges 

must be addressed in language processing before humans can communicate as effectively with AI 

systems as they do with other humans. Much recent progress in language processing has been credited 

to the use of data-driven machine learning approaches, which have resulted in successful systems that, 

for example, successfully recognize fluent English speech in quiet surroundings in real time. These 

achievements, however, are only first steps toward reaching longer-term goals. Current systems cannot 

deal with real-world challenges such as speech in noisy surroundings, heavily accented speech, 

children’s speech, impaired speech, and speech for sign languages. The development of language 

processing systems capable of engaging in real-time dialogue with humans is also needed. Such 

systems will need to infer the goals and intentions of its human interlocutors, use the appropriate 

register, style and rhetoric for the situation, and employ repair strategies in case of dialogue 

misunderstandings. Further research is needed on developing systems that more easily generalize 

across different languages. Additionally, more study is required on acquiring useful structured domain 

knowledge in a form readily accessible by language processing systems. 

Language processing advances in many other areas are also needed to make interactions between 

humans and AI systems more natural and intuitive. Robust computational models must be built for 

patterns in both spoken and written language that provide evidence for emotional state, affect, and 

stance, and for determining the information that is implicit in speech and text. New language 

processing techniques are needed for grounding language in the environmental context for AI systems 

that operate in the physical world, such as in robotics. Finally, since the manner in which people 

communicate in online interactions can be quite different from voice interactions, models of languages 

used in these contexts must be perfected so that social AI systems can interact more effectively with 

people.
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Strategy 3: Understand and Address the Ethical, Legal, and Societal 

Implications of AI

2019 

Update Addressing ethical, legal, and societal considerations in AI

Since the release of the 2016 National AI 

R&D Strategic Plan, R&D activities 

addressing the ethical, legal, and societal 

implications of AI system development 

and deployment have increased. There is a 

growing realization that AI systems must 

be “trustworthy,” and that AI could 

transform many sectors of social and 

economic life, including employment, 

healthcare, and manufacturing.

International organizations such as the 

Organisation for Economic Co-operation 

and Development (OECD)63 and the G7 

Innovation Ministers64 have encouraged 

R&D to increase trust in and adoption of AI.

The 2016 National AI R&D Strategic Plan

was prescient in identifying research 

themes in privacy; improving fairness,

transparency, and accountability of AI 

systems by design; and designing 

architectures for ethical AI. Research 

conferences dedicated to fairness, 

accountability, and transparency in ML 

and AI systems have flourished.65 Federal

agencies have responded with a variety of 

new research programs and meetings 

focused on these critical areas (see 

sidebar).

Explainability, fairness, and transparency:

Recent agency R&D programs

Since the release of the 2016 National AI R&D Strategic 

Plan, a number of agencies have initiated AI R&D

programs for Strategy 3:

 DARPA’s Explainable AI (XAI) program60 aims to 

create a suite of ML techniques that produce more 

explainable AI systems while maintaining a high 

level of learning performance (prediction 

accuracy). XAI will also enable human users to 

understand, appropriately trust, and effectively 

manage the emerging generation of AI systems. 

More generally, DoD is committed to “leading in 

military ethics and AI safety” as one of five key 

actions outlined in the strategic approach that 

guides its efforts to accelerate the adoption of AI 

systems.61

 NSF and Amazon are collaborating62 to jointly 

support research focused on AI fairness with the 

goal of contributing to trustworthy AI systems that 

are readily accepted and deployed to tackle grand 

challenges facing society. Specific topics of 

interest include, but are not limited to, 

transparency, explainability, accountability, 

potential adverse biases and effects, mitigation 

strategies, validation of fairness, and 

considerations of inclusivity.

 60 https://www.darpa.mil/program/explainable-artificial-intelligence

61 “Summary of the 2018 Department of Defense Artificial Intelligence Strategy”:

https://media.defense.gov/2019/Feb/12/2002088963/-1/-1/1/SUMMARY-OF-DOD-AI-STRATEGY.PDF. 62 https://www.nsf.gov/pubs/2019/nsf19571/nsf19571.htm 63 “OECD Initiatives on AI”: http://www.oecd.org/going-digital/ai/oecd-initiatives-on-ai.htm. 64 “G7 Innovation Ministers' Statement on AI”: http://www.g8.utoronto.ca/employment/2018-labour-annex-b-en.html.

65 http://www.fatml.org/; https://fatconference.org/; http://www.aies-conference.com/
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The 2019 Executive Order on Maintaining American Leadership in Artificial Intelligence emphasizes 

that maintaining American leadership in AI requires a concerted effort to promote advancements in 

technology and innovation, while protecting civil liberties, privacy, and American values:

1

The United States must foster public trust and confidence in AI technologies and protect civil 

liberties, privacy, and American values in their application in order to fully realize the 

potential of AI technologies for the American people.

More R&D is needed to develop AI architectures that incorporate ethical, legal, and societal concerns 

through technical mechanisms such as transparency and explainability. This R&D will require 

intensive collaboration among technical experts as well as stakeholders and specialists in other 

fields including the social and behavioral sciences, law, ethics, and philosophy. Since ethical 

decisions may also be heavily context- or application-dependent, collaboration with domain 

experts could be required as well. This interdisciplinary approach could be incorporated in the 

training, design, testing, evaluation, and implementation of AI in the interests of understanding and 

accounting for AI-induced decisions and actions and mitigating unintended consequences. 

Federal agencies should therefore continue to foster the growing community of interest in further 

R&D of these issues by sponsoring research and convening experts and stakeholders. 

When AI agents act autonomously, we expect them to behave according to the formal and informal 

norms to which we hold our fellow humans. As fundamental social ordering forces, law and ethics 

therefore both inform and adjudge the behavior of AI systems. The dominant research needs involve 

both understanding the ethical, legal, and social implications of AI, as well as developing methods for 

AI design that align with ethical, legal, and social principles. Privacy concerns must also be taken into 

account; further information on this issue can be found in the National Privacy Research Strategy.

66

As with any technology, the acceptable uses of AI will be informed by the tenets of law and ethics; the 

challenge is how to apply those tenets to this new technology, particularly those involving autonomy, 

agency, and control. 

As illuminated in “Research Priorities for Robust and Beneficial Artificial Intelligence,”67

In order to build systems that robustly behave well, we of course need to decide what good 

behavior means in each application domain. This ethical dimension is tied intimately to 

questions of what engineering techniques are available, how reliable these techniques are, and 

what trade-offs are made—all areas where computer science, machine learning, and broader AI 

expertise is valuable.

Research in this area can benefit from multidisciplinary perspectives that involve experts from 

computer science, social and behavioral sciences, ethics, biomedical science, psychology, economics, 

law, and policy research. Further investigation is needed in areas both inside and outside of the NITRDrelevant IT domain (i.e., in information technology, as well as in the disciplines mentioned previously) 

to inform the R&D and use of AI systems and their impacts on society.

The following subsections explore key information technology research challenges in this area.

 66 https://www.nitrd.gov/pubs/NationalPrivacyResearchStrategy.pdf 67 “An Open Letter: Research Priorities for Robust and Beneficial Artificial Intelligence” (Future of Life Institute):

http://futureoflife.org/ai-open-letter/.
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Improving fairness, transparency, and accountability by design 

Many concerns have been voiced about the susceptibility of data-intensive AI algorithms to error and

misuse, and the possible ramifications for gender, age, racial, or economic classes. The proper 

collection and use of data for AI systems, in this regard, represent an important challenge. Beyond 

purely data-related issues, however, larger questions arise about the design of AI to be inherently just, 

fair, transparent, and accountable. Researchers must learn how to design these systems so that their 

actions and decision-making are transparent and easily interpretable by humans, and thus can be 

examined for any bias they may contain, rather than just learning and repeating these biases. There are 

serious intellectual issues about how to represent and “encode” value and belief systems. Scientists 

must also study to what extent justice and fairness considerations can be designed into the system, 

and how to accomplish this within the bounds of current engineering techniques.

Building ethical AI

Beyond fundamental assumptions of justice and fairness are other concerns about whether AI systems 

can exhibit behavior that abides by general ethical principles. How might advances in AI frame new 

“machine-relevant” questions in ethics, or what uses of AI might be considered unethical? Ethics is 

inherently a philosophical question while AI technology depends on, and is limited by, engineering. 

Within the limits of what is technologically feasible, therefore, researchers must strive to develop 

algorithms and architectures that are verifiably consistent with, or conform to, existing laws, social 

norms and ethics—clearly a very challenging task. Ethical principles are typically stated with varying 

degrees of vagueness and are hard to translate into precise system and algorithm design. There are 

also complications when AI systems, particularly with new kinds of autonomous decision-making 

algorithms, face moral dilemmas based on independent and possibly conflicting value systems. Ethical 

issues vary according to culture, religion, and beliefs. However, acceptable ethics reference frameworks 

can be developed to guide AI system reasoning and decision-making in order to explain and justify its 

conclusions and actions. A multidisciplinary approach is needed to generate datasets for training that 

reflect an appropriate value system, including examples that indicate preferred behavior when 

presented with difficult moral issues or with conflicting values. These examples can include legal or 

ethical “corner cases,” labeled by an outcome or judgment that is transparent to the user.68 AI needs 

adequate methods for values-based conflict resolution, where the system incorporates principles that 

can address the realities of complex situations where strict rules are impracticable.

Designing architectures for ethical AI

Additional progress in fundamental research must be made to determine how to best design 

architectures for AI systems that incorporate ethical reasoning. A variety of approaches have been 

suggested, such as a two-tier monitor architecture that separates the operational AI from a monitor 

agent that is responsible for the ethical or legal assessment of any operational action.68 An alternative 

view is that safety engineering is preferred, in which a precise conceptual framework for the AI agent 

architecture is used to ensure that AI behavior is safe and not harmful to humans.

69 A third method is 

to formulate an ethical architecture using set theoretic principles, combined with logical constraints 

 68 A. Etzioni and O. Etzioni, “Designing AI Systems that Obey Our Laws and Values,” Communications of the ACM 59(9) 

(2016):29-31. 69 R. Y. Yampolsky, “Artificial Intelligence Safety Engineering: Why Machine Ethics is a Wrong Approach.” In Philosophy 

and Theory of Artificial Intelligence, ed. V.C. Muller (Heidelberg: Springer Verlag, 2013), 389-396.
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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on AI system behavior that restrict action to conform to ethical doctrine.70 As AI systems become more 

general, their architectures will likely include subsystems that can take on ethical issues at multiple 

levels of judgment, including:71 rapid response pattern matching rules, deliberative reasoning for 

slower responses for describing and justifying actions, social signaling to indicate trustworthiness for 

the user, and social processes that operate over even longer time scales to enable the system to abide 

by cultural norms. Researchers will need to focus on how to best address the overall design of AI 

systems that align with ethical, legal, and societal goals.

 70 R. C. Arkin, “Governing Legal Behavior: Embedding Ethics in a Hybrid Deliberative/Reactive Robot Architecture,”

Georgia Institute of Technology Technical Report, GIT-GVU-07-11, 2007. 71 B. Kuipers, “Human-like Morality and Ethics for Robots,” AAAI-16 Workshop on AI, Ethics and Society, 2016; 

https://web.eecs.umich.edu/~kuipers/papers/Kuipers-aaaiws-16.pdf
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Strategy 4: Ensure the Safety and Security of AI Systems

2019 

Update Creating robust and trustworthy AI systems 

Since the 2016 release of the National AI 

R&D Strategic Plan, there has been rapid 

growth in scientific and societal 

understanding of AI security and safety. 

Much of this new knowledge has helped 

identify new problems: it is more evident

now how AI systems can be made to do the 

wrong thing, learn the wrong thing, or 

reveal the wrong thing, for example, 

through adversarial examples, data 

poisoning, and model inversion, 

respectively. Unfortunately, technical 

solutions for these AI safety and security 

problems remain elusive.

To address all of these problems, the safety 

and security of AI systems must be 

considered in all stages of the AI system 

lifecycle, from the initial design and 

data/model building, to verification and 

validation, deployment, operation, and 

monitoring. Indeed, the notion of “safety 

(or security) by design” might impart an 

incorrect notion that these are only 

concerns of system designers; instead, they 

must be considered throughout the system 

lifecycle, not just at the design stage, and 

so must be an important part of the AI R&D

portfolio.

When AI components are connected to 

other systems or information that must be 

safe or secure, the AI vulnerabilities and 

performance requirements (e.g., very low

false-positive and false-negative rates, 

when operating over high volumes of data) 

AI safety and security: 

Recent agency R&D programs

Since the release of the 2016 National AI R&D Strategic 

Plan, a number of agencies have initiated efforts 

supporting Strategy 4:

 DOT published new Federal guidance for 

automated vehicles in October 2018 supporting 

the safe integration of automation into the broad 

multimodal surface transportation system. 

Preparing for the Future of Transportation: 

Automated Vehicles 3.0 72 advances DOT’s 

principles for safe integration of automated 

vehicles. The document also reiterates prior 

safety guidance, provides new multimodal safety 

guidance, and outlines a process for working with 

DOT as this new technology evolves. As of May 

2019, fourteen companies had released 

Voluntary Safety Self-Assessments detailing how 

they will incorporate safety into their design and 

testing of automated driving systems.73

 In December 2018, IARPA announced two 

programs on AI security: Secure, Assured, 

Intelligent Learning Systems (SAILS)74 and 

Trojans in Artificial Intelligence (TrojAI).

75 DARPA 

announced another program in February 2019, 

Guaranteeing AI Robustness against Deception 

(GARD).

76 Together, these programs aim to 

combat a range of attacks on AI systems.

 As noted in Strategy 3, DoD is committed to 

“leading in military ethics and AI safety” as one of 

five key actions outlined in the strategic 

approach that guides its efforts to accelerate the 

adoption of AI systems.77

 72 https://www.transportation.gov/av/3

73 https://www.nhtsa.gov/automated-driving-systems/voluntary-safety-self-assessment 74 https://www.iarpa.gov/index.php/research-programs/sails

75 https://www.iarpa.gov/index.php/research-programs/trojai

76 https://www.darpa.mil/news-events/2019-02-06 77 “Summary of the 2018 Department of Defense Artificial Intelligence Strategy”:

https://media.defense.gov/2019/Feb/12/2002088963/-1/-1/1/SUMMARY-OF-DOD-AI-STRATEGY.PDF.
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are inherited by the larger systems. These challenges are not static; as AI systems continue to grow 

in capabilities, they will likely grow in complexity, making it ever harder for correct performance or 

privacy of information to be verified and validated. This complexity may also make it increasingly 

difficult to explain decisions in ways that justify high levels of trust from human users (see Strategy 3).

Making AI trustworthy—now and into the future—is a critical issue that requires Federal Government 

R&D investments (see sidebar), along with collaborative efforts among government, industry, 

academia, and civil society. Engineering trustworthy AI systems may benefit from borrowing existing 

practices in safety engineering in other fields that have learned how to account for potential 

misbehavior of non-AI autonomous or semi-autonomous systems. However, AI-specific problems 

mean that novel techniques for program analysis, testing, formal verification, and synthesis will be 

critical to establish that an AI-based system meets its specifications—that is, that the system does 

exactly what it is supposed to do and no more. These problems are exacerbated in AI-based systems 

that can be easily fooled, evaded, and misled in ways that can have profound security implications. 

An emerging research area is adversarial ML, which explores both the analysis of vulnerabilities in 

ML algorithms as well as algorithmic techniques that yield more robust learning.Well-known attacks 

on ML include adversarial classifier evasion attacks, where the attacker changes behavior to escape 

being detected, and poisoning attacks, where training data itself is corrupted. There is growing need 

for research that systematically explores the space of adversaries that attack ML and other AI-based 

systems and to design algorithms that provide provable robustness guarantees against classes of 

adversaries.

Methods must be developed to make safe and secure the creation, evaluation, deployment, and 

containment of AI, and these methods must scale to match the capability and complexity of AI. 

Evaluating these methods will require new metrics, control frameworks, and benchmarks for testing 

and assessing the safety of increasingly powerful systems. Both methods and metrics must 

incorporate human factors, with safe AI objectives defined by human designers’ goals, safe AI 

operations defined by human users’ habits, and safe AI metrics defined by human evaluators’ 

understanding. Producing human-driven and human-understandable methods and metrics for the 

safety of AI systems will enable policymakers, the private sector, and the public to accurately judge 

the evolving AI safety landscape and appropriately proceed within it.

Before an AI system is put into widespread use, assurance is needed that the system will operate safely 

and securely, in a controlled manner. Research is needed to address this challenge of creating AI 

systems that are reliable, dependable, and trustworthy. As with other complex systems, AI systems face 

important safety and security challenges due to:78

• Complex and uncertain environments: In many cases, AI systems are designed to operate in complex 

environments, with a large number of potential states that cannot be exhaustively examined or 

tested. A system may confront conditions that were never considered during its design.

• Emergent behavior: For AI systems that learn after deployment, a system's behavior may be 

determined largely by periods of learning under unsupervised conditions. Under such conditions, 

it may be difficult to predict a system’s behavior.

• Goal misspecification: Due to the difficulty of translating human goals into computer instructions, 

the goals that are programmed for an AI system may not match the goals that were intended by 

the programmer.

 78 J. Bornstein, “DoD Autonomy Roadmap – Autonomy Community of Interest,” Presentation at NDIA 16th Annual 

Science & Engineering Technology Conference, March 2015.
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• Human-machine interactions: In many cases, the performance of an AI system is substantially

affected by human interactions. In these cases, variation in human responses may affect the

safety of the system.79

To address these issues and others, additional investments are needed to advance AI safety and 

security,

80 including explainability and transparency, trust, verification and validation, security against

attacks, and long-term AI safety and value-alignment.

Improving explainability and transparency

A key research challenge is increasing the “explainability” or “transparency” of AI. Many algorithms, 

including those based on deep learning, are opaque to users, with few existing mechanisms for 

explaining their results. This is especially problematic for domains such as healthcare, where doctors 

need explanations to justify a particular diagnosis or a course of treatment. AI techniques such as 

decision-tree induction provide built-in explanations but are generally less accurate. Thus, researchers 

must develop systems that are transparent, and intrinsically capable of explaining the reasons for their 

results to users.

Building trust

To achieve trust, AI system designers need to create accurate, reliable systems with informative, userfriendly interfaces, while the operators must take the time for adequate training to understand system 

operation and limits of performance. Complex systems that are widely trusted by users, such as manual 

controls for vehicles, tend to be transparent (the system operates in a manner that is visible to the user), 

credible (the system’s outputs are accepted by the user), auditable (the system can be evaluated), 

reliable (the system acts as the user intended), and recoverable (the user can recover control when 

desired). A significant challenge to current and future AI systems remains the inconsistent quality of 

software production technology. As advances bring greater linkages between humans and AI systems, 

the challenge in the area of trust is to keep pace with changing and increasing capabilities, anticipate 

technological advances in adoption and long-term use, and establish governing principles and policies 

for the study of best practices for design, construction, and use, including proper operator training for 

safe operation.

Enhancing verification and validation 

New methods are needed for verification and validation of AI systems. “Verification” establishes that a 

system meets formal specifications, while “validation” establishes that a system meets the user’s 

operational needs. Safe AI systems may require new means of assessment (determining if the system is 

malfunctioning, perhaps when operating outside expected parameters), diagnosis (determining the 

causes for the malfunction), and repair (adjusting the system to address the malfunction). For systems 

operating autonomously over extended periods of time, system designers may not have considered 

every condition the system will encounter. Such systems may need to possess capabilities for selfassessment, self-diagnosis, and self-repair in order to be robust and reliable.

79 J. M. Bradshaw, R. R. Hoffman, M. Johnson, and D. D. Woods, “The Seven Deadly Myths of Autonomous Systems,” 

IEEE Intelligent Systems 28(3)(2013):54-61. 80 See, for instance: D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mane, “Concrete Problems in 

AI Safety,” 2016, arXiv: 1606.06565v2; S. Russell, D. Dewey, and M. Tegmark, “Research Priorities for Robust and 

Beneficial Artificial Intelligence,” 2016, arXiv: 1602.03506; T. G. Dietterich and E. J. Horvitz, “Rise of Concerns about AI: 

Reflections and Directions,” Communications of the ACM, 58(10)(2015); and K. Sotala and R. Yampolskiy, “Responses 

to catastrophic AGI risk: A survey,” Physica Scripta, 90(1), 19 December 2014.
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Securing against attacks

AI embedded in critical systems must be robust in order to handle accidents but should also be secure 

to a wide range of intentional cyber attacks. Security engineering involves understanding the 

vulnerabilities of a system and the actions of actors who may be interested in attacking it. While 

cybersecurity R&D needs are addressed in greater detail in the NITRD 2016 Federal Cybersecurity R&D 

Strategic Plan,

81 some cybersecurity risks are specific to AI systems. For example, one key research area 

is “adversarial machine learning” that explores the degree to which AI systems can be compromised by 

“contaminating” training data, by modifying algorithms, or by making subtle changes to an object that 

prevent it from being correctly identified (e.g., prosthetics that spoof facial recognition systems). The 

implementation of AI in cybersecurity systems that require a high degree of autonomy is also an area 

for further study. One recent example of work in this area is DARPA’s Cyber Grand Challenge that 

involved AI agents autonomously analyzing and countering cyber attacks.82

Achieving long-term AI safety and value-alignment

AI systems may eventually become capable of “recursive self-improvement,” in which substantial 

software modifications are made by the software itself, rather than by human programmers. To ensure 

the safety of self-modifying systems, additional research is called for to develop: self-monitoring 

architectures that check systems for behavioral consistency with the original goals of human designers; 

confinement strategies for preventing the release of systems while they are being evaluated; value 

learning, in which the values, goals, or intentions of users can be inferred by a system; and value 

frameworks that are provably resistant to self-modification.

 81 https://www.nitrd.gov/pubs/2016-Federal-Cybersecurity-Research-and-Development-Strategic-Plan.pdf; this is 

being updated in 2019.

82 https://archive.darpa.mil/CyberGrandChallenge_CompetitorSite/
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Strategy 5: Develop Shared Public Datasets and Environments for AI Training 

and Testing

2019 

Update Increasing access to datasets and associated challenges

At the time of the 2016 National AI R&D 

Strategic Plan’s release, publicly available 

datasets and environments were already 

playing a critical role in pushing forward AI 

R&D, particularly in areas such as 

computer vision, natural language 

processing, and speech recognition. 

ImageNet,84 with more than 14 million 

labeled objects, along with associated 

computer vision community challenges 

(e.g., the ImageNet Large Scale Visual 

Recognition Challenge85 that evaluates

algorithms for object detection and image 

classification), have played an especially

vitalrole in the community. As translational 

applications for ML are being found in 

myriad application areas such as

healthcare, medicine, and smart and 

connected communities, the need has 

grown for publicly available datasets in 

domain-specific areas.

The importance of datasets and models –

in particular, those of the Federal 

Government – is explicitly called out in the 

2019 Executive Order on Maintaining 

American Leadership in Artificial 

Intelligence:

1

Heads of all agencies shall review their 

Federal data and models to identify 

opportunities to increase access and 

use by the greater non-Federal AI 

research community in a manner that 

benefits that community, while 

protecting safety, security, privacy, and 

confidentiality. Specifically, agencies 

shall improve data and model 

inventory documentation to enable 

discovery and usability, and shall 

Shared Public Datasets and Environments for AI 

Training and Testing: Recent agency R&D programs

Since the release of the 2016 National AI R&D Strategic 

Plan, a number of agencies have initiated efforts 

supporting Strategy 5:

 DOT sponsored the Second Strategic Highway 

Research Program (SHRP2) Naturalistic Driving 

Study (NDS),

83 which recorded more than 

5.4 million trips taken by more than 3,400 drivers 

and vehicles. An in-vehicle data acquisition system 

(DAS) unit gathered and stored data from forward 

radar, four video cameras, accelerometers, vehicle 

network information, a geographic positioning 

system, and an onboard lane tracker. Data from the 

DAS were recorded continuously while participants’ 

vehicles were operating. Whereas summaries of 

the NDS data are public, access to the detailed 

datasets requires qualified research ethics training.

 The VA Data Commons is creating the largest linked

medical–genomics dataset in the world with tools 

for enabling ML and AI, and guided by veterans’

preferences. This effort is leveraging applicable 

NIST standards, laws, and executive orders.

 GSA (General Services Administration) is working to 

enable the use of cloud computing resources for 

federally funded AI R&D. Data.gov and code.gov, 

housed at GSA, contain over 246,000 datasets and 

code from across agencies and automatically 

harvest datasets released by agencies.

 The NIH Science and Technology Research 

Infrastructure for Discovery, Experimentation, and 

Sustainability (STRIDES) initiative, a partnership 

with industry-leading cloud service providers, is

enabling researcher access to major data assets 

that are funded across NIH and that are stored in 

cloud environments. 

 83 https://insight.shrp2nds.us/ 84 http://www.image-net.org/ 85 http://www.image-net.org/challenges/LSVRC/
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prioritize improvements to access and quality of AI data and models based on the AI research 

community’s feedback.

A new NSTC Subcommittee on Open Science was created in 2018 to coordinate Federal efforts on 

open and FAIR (findable, accessible, interoperable, and reusable) data. R&D investments will be 

needed to develop tools and resources that make it easier to identify, use, and manipulate relevant 

datasets (including Federal datasets), verify data provenance, and respect appropriate use policy. 

Many of these datasets themselves may be of limited use in an AI context without an investment in 

labeling and curation. Federal agencies should engage and work with AI stakeholders to ensure that 

appropriately vetted datasets and models that are released for sharing are ready and fit for use and 

that they are maintained as standards and norms evolve. Ultimately, development and adoption of 

best practices and standards in documenting dataset and model provenance will enhance 

trustworthiness and responsible use of AI technologies.

Since 2016, there have also been increased concerns about data content, such as potential bias (see 

Strategy 3)86,87 or private information leakage. The 2016 National AI R&D Strategic Plan noted that 

“dataset development and sharing must ... follow applicable laws and regulations and be carried 

out in an ethical manner.” The DOT-supported InSight project provides such carefully structured 

access to data collected during the Naturalistic Driving Study (see sidebar). The 2016 National AI R&D 

Strategic Plan also noted that new “technologies are needed to ensure safe sharing of data, since 

data owners take on risk when sharing their data with the research community.” For example, 

CryptoNets88 allows neural networks to operate over encrypted data, ensuring that data remain 

confidential, because decryption keys are not needed in neural networks. Researchers have also 

begun developing new ML techniques that use a differential privacy framework to provide 

quantifiable privacy guarantees over the used data.89 At the same time, privacy methods must 

remain sufficiently explainable and transparent to help researchers correct them and make them 

safe, efficient, and accurate. Furthermore, AI could reveal discoveries beyond the original or 

intended scope; therefore, researchers must remain cognizant of the potential dangers in access to 

data or discoveries by adversaries.

Data alone are of little use without the ability to bring computational resources to bear on large-scale 

public datasets. The importance of computational resources to AI R&D is called out in the 2019 

Executive Order on Maintaining American Leadership in Artificial Intelligence:

1

The Secretaries of Defense, Commerce, Health and Human Services, and Energy, the 

Administrator of the National Aeronautics and Space Administration, and the Director of the 

National Science Foundation shall, to the extent appropriate and consistent with applicable 

law, prioritize the allocation of high-performance computing resources for AI-related 

applications through: (i) increased assignment of discretionary allocation of resources and 

resource reserves; or (ii) any other appropriate mechanisms.

 86 Emily M. Bender and Batya Friedman, “Data Statements for NLP: Toward Mitigating System Bias and Enabling 

Better Science,” Transactions of the Association for Computational Linguistics 6 (2018):587-604.

87 Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, “Semantics derived automatically from language corpora 

contain human-like biases,” Science 356(6334):183-186, 14 Apr 2017. 88 Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing, “CryptoNets: 

Applying neural networks to encrypted data with high throughput and accuracy,” 2016 International Conference on 

Machine Learning 48:201-210; http://proceedings.mlr.press/v48/. 89 Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, “Deep 

Learning with Differential Privacy,” 23rd ACM Conference on Computer and Communications Security,” 2016: 308-318.
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and:

...the Select Committee, in coordination with the General Services Administration (GSA), shall 

submit a report to the President making recommendations on better enabling the use of 

cloud computing resources for federally funded AI R&D.

The need for computational capacity for many AI challenges has been increasing rapidly.32 Federal 

funding may provide computational capabilities for Federally-funded research. Some companies 

and universities, however, may have additional computational demands. Overall, there is a national 

need to study and invest in shared computational resources to promote AI R&D.

The benefits of AI will continue to accrue, but only to the extent that training and testing resources for 

AI are developed and made available. The variety, depth, quality, and accuracy of training datasets and 

other resources significantly affects AI performance. Many different AI technologies require high-quality 

data for training and testing, as well as dynamic, interactive testbeds and simulation environments. 

More than just a technical question, this is a significant “public good” challenge, as progress would 

suffer if AI training and testing is limited to only a few entities that already hold valuable datasets and 

resources, yet we must simultaneously respect commercial and individual rights and interests in the 

data. Research is needed to develop high-quality datasets and environments for a wide variety of AI 

applications and to enable responsible access to good datasets and testing and training resources.

Additional open-source software libraries and toolkits are also needed to accelerate the advancement 

of AI R&D. The following subsections outline these key areas of importance.

Developing and making accessible a wide variety of datasets to meet the needs of a diverse 

spectrum of AI interests and applications

The integrity and availability of AI training and testing datasets is crucial to ensuring scientifically 

reliable results. The technical as well as the socio-technical infrastructure necessary to support 

reproducible research in the digital area has been recognized as an important challenge—and is 

essential to AI technologies as well. The lack of vetted and openly available datasets with identified 

provenance to enable reproducibility is a critical factor to confident advancement in AI.90 As in other 

data-intensive sciences, capturing data provenance is critical. Researchers must be able to reproduce 

results with the same as well as different datasets. Datasets must be representative of challenging realworld applications, and not just simplified versions. To make progress quickly, emphasis should be 

placed on making available already existing datasets held by government, those that can be developed 

with Federal funding, and, to the extent possible, those held by industry.

The machine learning aspect of the AI challenge is often linked with “big data” analysis. Considering 

the wide variety of relevant datasets, it remains a growing challenge to have appropriate 

representation, access, and analysis of unstructured or semi-structured data. How can the data be 

represented—in absolute as well as relative (context-dependent) terms? Current real-world databases 

can be highly susceptible to inconsistent, incomplete, and noisy data. Therefore, a number of data 

preprocessing techniques (e.g., data cleaning, integration, transformation, reduction, and 

representation) are important to establishing useful datasets for AI applications. How does the data 

preprocessing impact data quality, especially when additional analysis is performed?

 90 Toward this end, in 2016 the Intelligence Advanced Research Projects Activity issued a Request for Information on 

novel training datasets and environments to advance AI. See https://iarpa.gov/index.php/working-with-iarpa

/requests-for-information/novel-training-datasets-and-environments-to-advance-artificial-intelligence.
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Encouraging the sharing of AI datasets—especially for government-funded research—would likely 

stimulate innovative AI approaches and solutions. However, technologies are needed to ensure safe 

sharing of data, since data owners take on risk when sharing their data with the research community.

Dataset development and sharing must also follow applicable laws and regulations and be carried out 

in an ethical manner. Risks can arise in various ways: inappropriate use of datasets, inaccurate or 

inappropriate disclosure, and limitations in data de-identification techniques to ensure privacy and 

confidentiality protections.

Making training and testing resources responsive to commercial and public interests

With the continuing explosion of data, data sources, and information technology worldwide, both the 

number and size of datasets are increasing. The techniques and technologies to analyze data are not 

keeping up with the high volume of raw information sources. Data capture, curation, analysis, and 

visualization are all key research challenges, and the science needed to extract valuable knowledge 

from enormous amounts of data is lagging behind. While data repositories exist, they are often unable 

to deal with the scaling up of datasets, have limited data provenance information, and do not support 

semantically rich data searches. Dynamic, agile repositories are needed. 

One example of the kind of open/sharing infrastructure program that is needed to support the needs of 

AI research is the IMPACT program (Information Marketplace for Policy and Analysis of Cyber-risk & Trust)

developed by the Department of Homeland Security (DHS).91 This program supports the global cyber 

security risk research effort by coordinating and developing real-world data and information sharing 

capabilities, including tools, models, and methodologies. IMPACT also supports empirical data sharing 

between the international cybersecurity R&D community, critical infrastructure providers, and their 

government supporters. AI R&D would benefit from comparable programs across all AI applications.

Developing open-source software libraries and toolkits

The increased availability of open-source software libraries and toolkits provides access to cuttingedge AI technologies for any developer with an Internet connection. Resources such as the Weka 

toolkit,92 MALLET,93 and OpenNLP,94 among many others, have accelerated the development and 

application of AI. Development tools, including free or low-cost code repository and version control 

systems, as well as free or low-cost development languages (e.g., R, Octave, and Python) provide low 

barriers to using and extending these libraries. In addition, for those who may not want to integrate 

these libraries directly, any number of cloud-based machine learning services exist that can perform 

tasks such as image classification on demand through low-latency web protocols that require little or 

no programming for use. Finally, many of these web services also offer the use of specialized hardware, 

including GPU-based systems. It is reasonable to assume that specialized hardware for AI algorithms,

including neuromorphic processors, will also become widely available through these services. 

Together, these resources provide an AI technology infrastructure that encourages marketplace 

innovation by allowing entrepreneurs to develop solutions that solve narrow domain problems without 

requiring expensive hardware or software, without requiring a high level of AI expertise, and permitting 

rapid scaling-up of systems on demand. For narrow AI domains, barriers to marketplace innovation are 

extremely low relative to many other technology areas.

 91 https://www.dhs.gov/csd-impact 92 https://sourceforge.net/projects/weka/ 93 http://mallet.cs.umass.edu

94 https://opennlp.apache.org
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To help support a continued high level of innovation in this area, the U.S. Government can boost efforts 

in the development, support, and use of open AI technologies. Particularly beneficial would be open 

resources that use standardized or open formats and open standards for representing semantic 

information, including domain ontologies when available.

Government may also encourage greater adoption of open AI resources by accelerating the use of open 

AI technologies within the government itself, and thus help to maintain a low barrier to entry for 

innovators. Whenever possible, government should contribute algorithms and software to open source 

projects. Because government has specific concerns, such as a greater emphasis on data privacy and 

security, it may be necessary for the government to develop mechanisms to ease government adoption 

of AI systems. For example, it may be useful to create a task force that can perform a “horizon scan”

across government agencies to find particular AI application areas within departments, and then 

determine specific concerns that would need to be addressed to permit adoption of such techniques 

by these agencies.
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Strategy 6: Measure and Evaluate AI Technologies through Standards and 

Benchmarks

2019 

Update Supporting development of AI technical standards and related tools

The 2016 National AI R&D Strategic Plan

states that “Standards, benchmarks, 

testbeds, and their adoption by the AI 

community are essential for guiding and 

promoting R&D of AI technologies.” In the 

intervening three years, emphasis on 

standards and benchmarks has continued 

to rise in the U.S. and globally. The 2019

Executive Order on Maintaining American 

Leadership in Artificial Intelligence

explicitly calls out the importance of such 

standards:1

...[T]he Secretary of Commerce, 

through the Director of [NIST], shall 

issue a plan for Federal engagement in 

the development of technical 

standards and related tools in support 

of reliable, robust, and trustworthy 

systems that use AI technologies.

With AI innovation potentially impacting 

all sectors and domains of society, many 

standards development organizations 

have new AI-related considerations and 

work items underway, including activities 

related to AI ethics and trustworthy AI 

systems (see Strategy 3). The International 

Organization for Standardization (ISO) and 

the International Electrotechnical 

Commission (IEC) have convened a joint 

technical subcommittee on AI (ISO/IEC

Joint Technical Committee 1, Subcommittee

42 on Artificial Intelligence95) to develop standards for AI systems and associated considerations. It 

is critical that Federal, industry, and academic researchers continue to inform these activities, 

particularly as AI advances and systems reach into areas such as transportation, health care, and 

food that align with the missions of government agencies.

Since 2016, the surge in AI-related standards activities has outpaced the launch of new AI-focused 

benchmarks and evaluations, particularly as related to trustworthiness of AI systems. In the

Standards, benchmarks, and related tools:

Recent agency R&D programs

Since the release of the 2016 National AI R&D Strategic 

Plan, NIST in particular has initiated efforts for Strategy 6:

 NIST is engaged in the standardization program of 

ISO/IEC JTC 1 SC 42 on Artificial Intelligence.

95 A 

NIST expert is the convener for the Big Data work 

effort in SC 42. The U.S. delegation to SC 42 

includes NIST and other Federal agency experts, 

as well as representatives from industry and

academia. U.S. input to SC 42 is facilitated by the 

International Committee for Information 

Technology Standards (INCITS).

 NIST staff participate in additional AI standards 

activities through standards organizations, such 

as the American Society of Mechanical Engineers, 

IEEE, and ISO/IEC. Their activities cover such topics

as computational modeling for advanced 

manufacturing, ontologies for robotics and 

automation, personal data privacy, and 

algorithmic bias. 

 NIST experts are raising awareness about the 

importance of consensus standards for AI in 

multilateral fora, including bodies such as G20 and 

G7.96 NIST brings unique Federal Government

expertise that grounds policy discussions in 

practice, in particular, through close collaboration 

with the private sector. Similarly, NIST lends its 

standards and related experience to 

intergovernmental bilateral discussions. 

 95 https://www.iso.org/committee/6794475.html 96 https://home.treasury.gov/policy-issues/international/g-7-and-g-20
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intervening time, however, considerations of fairness and bias in benchmark datasets have become 

increasingly important, with researchers pursuing new facial recognition datasets that seek to 

minimize bias. Much more plentiful are benchmarks that test the application-level performance of 

AI algorithms (e.g., false-positive or false-negative rates for classification algorithms) and 

benchmarks that quantify the compute-level performance of AI software and hardware systems. 

Two such recent activities are MLPerf97 and DAWNbench.98

Assessing, promoting, and assuring all aspects of AI trustworthiness requires measuring and 

evaluating AI technology performance through benchmarks and standards. Beyond being safe, 

secure, reliable, resilient, explainable, and transparent, trustworthy AI must preserve privacy while 

detecting and avoiding inappropriate bias. As AI technologies evolve, so will the need to develop 

new metrics and testing requirements for validation of these essential characteristics.

Standards, benchmarks, testbeds, and their adoption by the AI community are essential for guiding and 

promoting R&D of AI technologies. The following subsections outline areas where additional progress 

must be made. 

Developing a broad spectrum of AI standards

The development of standards must be hastened to keep pace with the rapidly evolving capabilities 

and expanding domains of AI applications. Standards provide requirements, specifications, guidelines, 

or characteristics that can be used consistently to ensure that AI technologies meet critical objectives 

for functionality and interoperability, and that they perform reliably and safely. Adoption of standards 

brings credibility to technology advancements and facilitates an expanded interoperable marketplace. 

One example of an AI-relevant standard that has been developed is P1872-2015 (Standard Ontologies 

for Robotics and Automation), developed by the Institute of Electrical and Electronics Engineers. This 

standard provides a systematic way of representing knowledge and a common set of terms and 

definitions. These allow for unambiguous knowledge transfer among humans, robots, and other 

artificial systems, as well as provide a foundational basis for the application of AI technologies to 

robotics. Additional work in AI standards development is needed across all subdomains of AI.

Standards are needed to address:

• Software engineering: to manage system complexity, sustainment, security, and to monitor and 

control emergent behaviors;

• Performance: to ensure accuracy, reliability, robustness, accessibility, and scalability;

• Metrics: to quantify factors impacting performance and compliance to standards;

• Safety: to evaluate risk management and hazard analysis of systems, human computer 

interactions, control systems, and regulatory compliance;

• Usability: to ensure that interfaces and controls are effective, efficient, and intuitive;

• Interoperability: to define interchangeable components, data, and transaction models via 

standard and compatible interfaces;

• Security: to address the confidentiality, integrity, and availability of information, as well as 

cybersecurity;

• Privacy: to control for the protection of information while being processed, when in transit, or 

being stored;

 97 https://mlperf.org/

98 https://dawn.cs.stanford.edu/benchmark/
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• Traceability: to provide a record of events (their implementation, testing, and completion), and 

for the curation of data; and

• Domains: to define domain-specific standard lexicons and corresponding frameworks.

Establishing AI technology benchmarks

Benchmarks, made up of tests and evaluations, provide quantitative measures for developing 

standards and assessing compliance to standards. Benchmarks drive innovation by promoting 

advancements aimed at addressing strategically selected scenarios; they additionally provide 

objective data to track the evolution of AI science and technologies. To effectively evaluate AI 

technologies, relevant and effective testing methodologies and metrics must be developed and 

standardized. Standard testing methods will prescribe protocols and procedures for assessing, 

comparing, and managing the performance of AI technologies. Standard metrics are needed to define 

quantifiable measures in order to characterize AI technologies, including but not limited to: accuracy, 

complexity, trust and competency, risk and uncertainty, explainability, unintended bias, comparison 

to human performance, and economic impact. It is important to note that benchmarks are data driven. 

Strategy 5 discusses the importance of datasets for training and testing.

As a successful example of AI-relevant benchmarks, the National Institute of Standards and Technology 

has developed a comprehensive set of standard test methods and associated performance metrics to 

assess key capabilities of emergency response robots. The objective is to facilitate quantitative 

comparisons of different robot models by making use of statistically significant data on robot 

capabilities that was captured using the standard test methods. These comparisons can guide 

purchasing decisions and help developers to understand deployment capabilities. The resulting test 

methods are being standardized though the ASTM International Standards Committee on Homeland 

Security Applications for robotic operational equipment (referred to as standard E54.08.01).99 Versions 

of the test methods are used to challenge the research community through the RoboCup Rescue Robot 

League competitions,100 which emphasize autonomous capabilities. Another example is the IEEE Agile 

Robotics for Industrial Automation Competition (ARIAC),101 a joint effort between IEEE and NIST,102

which promotes robot agility by utilizing the latest advances in artificial intelligence and robot 

planning. A core focus of this competition is to test the agility of industrial robot systems, with the goal 

of enabling those on the shop floors to be more productive, more autonomous, and requiring less time 

from shop floor workers.

While these efforts provide a strong foundation for driving AI benchmarking forward, they are limited 

by being domain-specific. Additional standards, testbeds, and benchmarks are needed across a 

broader range of domains to ensure that AI solutions are broadly applicable and widely adopted.

Increasing the availability of AI testbeds

The importance of testbeds was stated in the Cyber Experimentation of the Future report: “Testbeds are 

essential so that researchers can use actual operational data to model and run experiments on 

real-world system[s] ... and scenarios in good test environments.”103 Having adequate testbeds is a 

 99 2019 update: The resulting test methods are now standards issued by ASTM International Standards Committee on 

Homeland Secruity Applications for Response Robots (referred to as E54.09).

100 http://www.robocup2016.org/en/

101 http://robotagility.wixsite.com/competition

102 2019 update: IEEE is no longer a partner of ARIAC, which is now in its third year. 103 SRI International and USC Information Sciences Institute, “Cybersecurity Experimentation of the Future (CEF): 

Catalyzing a New Generation of Experimental Cybersecurity Research,” Final Report, July 31, 2015.
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need across all areas of AI. The government has massive amounts of mission-sensitive data unique to 

government, but much of this data cannot be distributed to the outside research community. 

Appropriate programs could be established for academic and industrial researchers to conduct 

research within secured and curated testbed environments established by specific agencies. AI models 

and experimental methods could be shared and validated by the research community by having access 

to these test environments, affording AI scientists, engineers, and students unique research 

opportunities not otherwise available.

Engaging the AI community in standards and benchmarks

Government leadership and coordination is needed to drive standardization and encourage its 

widespread use in government, academia, and industry. The AI community—made up of users, 

industry, academia, and government—must be energized to participate in developing standards and 

benchmark programs. As each government agency engages the community in different ways based on 

its role and mission, community interactions can be leveraged through coordination in order to 

strengthen their impact. This coordination is needed to collectively gather user-driven requirements, 

anticipate developer-driven standards, and promote educational opportunities. User-driven 

requirements shape the objectives and design of challenge problems and enable technology 

evaluation. Having community benchmarks focuses R&D to define progress, close gaps, and drive 

innovative solutions for specific problems. These benchmarks must include methods for defining and 

assigning ground truth. The creation of benchmark simulation and analysis tools will also accelerate AI 

developments. The results of these benchmarks also help match the right technology to the user’s 

need, forming objective criteria for standards compliance, qualified product lists, and potential source 

selection.

Industry and academia are the primary sources for emerging AI technologies. Promoting and 

coordinating their participation in standards and benchmarking activities are critical. As solutions 

emerge, opportunities abound for anticipating developer- and user-driven standards through sharing 

common visions for technical architectures, developing reference implementations of emerging 

standards to show feasibility, and conducting precompetitive testing to ensure high-quality and 

interoperable solutions, as well as to develop best practices for technology applications. 

One successful example of a high-impact, community-based, AI-relevant benchmark program is the 

Text Retrieval Conference (TREC),104 which was started by NIST in 1992 to provide the infrastructure 

necessary for large-scale evaluation of information retrieval methodologies. More than 250 groups have 

participated in TREC, including academic and commercial organizations both large and small. The 

standard, widely available, and carefully constructed set of data put forth by TREC has been credited 

with revitalizing research on information retrieval.105,106 A second example is the NIST periodic 

benchmark program in the area of machine vision applied to biometrics,107 particularly face 

recognition.108 This began with the Face Recognition Technology (FERET) evaluation in 1993, which 

provided a standard dataset of face photos designed to support face recognition algorithm 

development as well as an evaluation protocol. This effort has evolved over the years into the Face 

 104 http://trec.nist.gov 105 E. M. Voorhees and D. K. Harman, TREC Experiment and Evaluation in Information Retrieval (Cambridge: MIT Press, 2005). 106 http://googleblog.blogspot.com/2008/03/why-data-matters.html 107 http://biometrics.nist.gov

108 http://face.nist.gov
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Recognition Vendor Test (FRVT),109 involving the distribution of datasets, hosting of challenge 

problems, and conducting of sequestered technology evaluations. This benchmark program has 

contributed greatly to the improvement of facial recognition technology. Both TREC and FRVT can serve 

as examples of effective AI-relevant community benchmarking activities, but similar efforts are needed 

in other areas of AI. 

It is important to note that developing and adopting standards, as well as participating in benchmark 

activities, comes with a cost. R&D organizations are incentivized when they see significant benefit. 

Updating acquisition processes across agencies to include specific requirements for AI standards in 

requests for proposals will encourage the community to further engage in standards development and 

adoption. Community-based benchmarks, such as TREC and FRVT, also lower barriers and strengthen 

incentives by providing types of training and testing data otherwise inaccessible, fostering healthy 

competition between technology developers to drive best-of-breed algorithms, and providing 

objective and comparative performance metrics for relevant source selections.

 109 P. J. Phillips, “Improving Face Recognition Technology,” Computer 44(3)(2011): 84-96.
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Strategy 7: Better Understand the National AI R&D Workforce Needs

2019 

Update

Advancing the AI R&D workforce, including those working on AI systems and 

those working alongside them, to sustain U.S. leadership

Since the release of the 2016 National AI 

R&D Strategic Plan, the demand for AI 

researchers and practitioners has grown 

rapidly. Studies have shown that the 

number of hiring opportunities is expected

to rise into the millions over the next 

decade. As one data point, the U.S. Bureau 

of Labor Statistics projects that the number 

of positions for computer and information 

scientists and engineers will grow by 19% 

from 2016 to 2026, almost three times faster 

than the average for all occupations.111

Moreover, through 2028, AI researchers are 

expected contribute to as much as $11.5 

trillion of cumulative growth promised by 

intelligent technologies in the G20 

countries alone.112

U.S. academic institutions are struggling to 

keep pace with the explosive growth in 

student interest and enrollment in 

AI.113,114,115 At the same time, industry, with 

its sustained financial support and access 

to advanced computing facilities and 

datasets, exerts a strong pull on academic 

research and teaching talent.116

It is critical to maintain a robust academic 

research ecosystem in AI that, in 

collaboration with industry R&D, can 

continue to deliver tremendous

dividends117 by advancing national health, 

prosperity, and welfare, and securing the 

national defense.

National AI R&D workforce: 

Recent agency activities

Since the release of the 2016 National AI R&D Strategic 

Plan, a number of agencies have initiated efforts

supporting Strategy 7:

 Apart from supporting undergraduate and 

graduate students through standard AI research 

grants, agencies are prioritizing computationaland data-enabled science and engineering in 

their graduate fellowship programs. For 

example, in 2018, DOE added a new track to its 

Computational Science Graduate Fellowship 

program. This track supports students pursuing 

advanced degrees in applied mathematics, 

statistics, or computer science, and promotes 

more effective use of high-performance systems, 

including in the areas of AI, ML, and deep 

learning.44,110 Also in 2018, NSF began prioritizing 

computational and data-enabled science and 

engineering in a subset of awardees of its 

Graduate Research Fellowships Program.

 The Census Bureau has created the Statistical 

Data Modernization (SDM) project to bring its 

workforce, operations, and technologies up to 

the current state of the art and set the standard 

for statistical agencies in today’s data-driven 

society. SDM’s workforce transformation 

component will enable the hiring of new data 

scientists with expertise in new methods and 

analytics, including the use of AI methods and 

tools to process and analyze big data. The 

workforce transformation will also address the 

upskilling of the current data science workforce.

 110 https://www.krellinst.org/csgf/math-cs 111 https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm 112 https://www.accenture.com/t20180920T094705Z__w__/us-en/_acnmedia/Thought-LeadershipAssets/PDF/Accenture-Education-and-Technology-Skills-Research.pdf 113 https://cra.org/data/generation-cs/ 114 https://cra.org/wp-content/uploads/2018/05/2017-Taulbee-Survey-Report.pdf 115 http://web.cs.wpi.edu/~cew/papers/CSareas19.pdf 116 https://www.nitrd.gov/rfi/ai/2018/AI-RFI-Response-2018-Yolanda-Gil-AAAI.pdf 117 https://www.nap.edu/catalog/13427/continuing-innovation-in-information-technology
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In the three years since the release of the 2016 National AI R&D Strategic Plan, various reports have 

called for continued support for the development of instructional materials and teacher 

professional development in computer science at all levels. Emphasis is needed at the K–12 levels 

to feed the Nation’s pipeline of AI researchers over many decades.118 At the undergraduate level, 

there is a need to focus on integrating advanced computational skills and methods with domainspecific knowledge from other disciplines, given the growing role of computing across disciplines.119

Sustained support is also needed at the graduate level, where students are conducting fundamental 

research in ML and AI. Indeed, the 2019 Executive Order on Maintaining American Leadership in 

Artificial Intelligence requires that:1

Heads of implementing agencies that also provide educational grants shall, to the extent 

consistent with applicable law, consider AI as a priority area within existing Federal 

fellowship and service programs ... [including] ... (A) high school, undergraduate, and 

graduate fellowship; alternative education; and training programs; (B) programs to 

recognize and fund early-career university faculty who conduct AI R&D, including through 

Presidential awards and recognitions; (C) scholarship for service programs; (D) direct 

commissioning programs of the United States Armed Forces; and (E) programs that support 

the development of instructional programs and curricula that encourage the integration of 

AI technologies into courses in order to facilitate personalized and adaptive learning 

experiences for formal and informal education and training.

More broadly, the need for a firm grounding in computational thinking, including through computer

science education, is also noted prominently in the Federal Government’s December 2018 five-year 

strategic plan for science, technology, engineering, and mathematics (STEM) education.120

In addition, it is imperative to broaden the participation among groups traditionally 

underrepresented in computing and related fields. 

Finally, the AI R&D workforce will consist of multidisciplinary teams comprising not just computer 

and information scientists and engineers, but also experts from other fields key to AI and ML 

innovation and its application, including cognitive science and psychology, economics and game 

theory, engineering and control theory, ethics, linguistics, mathematics, philosophy, and the many

domains in which AI may be applied. 

Federal agencies are giving priority to training and fellowship programs at all levels to prepare the 

workforce with requisite AI R&D skills through apprenticeships, skills programs, fellowships, and 

course work in relevant disciplines (see sidebar). Such training opportunities target both scientists 

and engineers who contribute to AI R&D innovations and users of AI R&D who may possess relevant 

domain knowledge. In the case of the former, long-term Federal investment in AI R&D, as described 

in Strategy 1, further supports the growth of this workforce, both through training the next 

generation of researchers and by making faculty positions more attractive to current graduate and 

postdoctoral students. In the case of the latter, new programs are bringing AI-relevant skills to 

current and future users of AI systems (see sidebar). Federal agencies must therefore continue to 

strategically foster expertise in the AI R&D workforce that spans multiple disciplines and skill 

categories to ensure sustained national leadership.

 118 https://github.com/touretzkyds/ai4k12/wiki

119 https://www.nap.edu/catalog/24926/assessing-and-responding-to-the-growth-of-computer-scienceundergraduate-enrollments

120 https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf
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Attaining the needed AI R&D advances outlined in this strategy will require a sufficient AI R&D 

workforce. Nations with the strongest presence in AI R&D will establish leading positions in the 

automation of the future. They will become the frontrunners in competencies like algorithm creation 

and development; capability demonstration; and commercialization. Developing technical expertise 

will provide the basis for these advancements.

While no official AI workforce data currently exist, numerous recent reports from the commercial and 

academic sectors are indicating an increased shortage of available experts in AI. AI experts are 

reportedly in short supply,121 with demand expected to continue to escalate.

122 High-tech companies 

are reportedly investing significant resources into recruiting faculty members and students with AI 

expertise.123 Universities and industries are reportedly in a battle to recruit and retain AI talent.124

Additional studies are needed to better understand the current and future national workforce needs 

for AI R&D. Data is needed to characterize the current state of the AI R&D workforce, including the needs 

of academia, government, and industry. Studies should explore the supply and demand forces in the AI 

workplace, to help predict future workforce needs. An understanding is needed of the projected AI R&D 

workforce pipeline. Considerations of educational pathways and potential retraining opportunities 

should be included. Diversity issues should also be explored, since studies have shown that a diverse 

information technology workforce can lead to improved outcomes.125 Once the current and future AI 

R&D workforce needs are better understood, then appropriate plans and actions can be considered to 

address any existing or anticipated workforce challenges.

 121 “Startups Aim to Exploit a Deep-Learning Skills Gap,” MIT Technology Review, January 6, 2016. 122 “AI talent grab sparks excitement and concern,” Nature, April 26, 2016.

123 “Artificial Intelligence Experts are in High Demand,” The Wall Street Journal, May 1, 2015.

124 “Million dollar babies: As Silicon Valley fights for talent, universities struggle to hold on to their stars,” The 

Economist, April 2, 2016. 125 J. W. Moody, C. M. Beise, A. B. Woszczynski, and M. E. Myers, “Diversity and the information technology workforce: 

Barriers and opportunities,” Journal of Computer Information Systems 43 (2003): 63-71.
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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Strategy 8: Expand Public–Private Partnerships to Accelerate Advances in AI

Strategy 8 is new in 2019 and reflects the 

growing importance of public-private 

partnerships enabling AI R&D. 

American leadership in science and 

engineering research and innovation is 

rooted in the Nation’s unique governmentuniversity-industry R&D ecosystem. As the 

American Association of Arts and Sciences 

has written, “America’s standing as an 

innovation leader” relies upon “establishing 

a more robust national GovernmentUniversity-Industry research

partnership.”126 Since the release of the 

2016 National AI R&D Strategic Plan, the 

Administration has amplified this vision of 

promoting “sustained investment in AI R&D 

in collaboration with academia, industry, 

international partners and allies, and other 

non-Federal entities to generate 

technological breakthroughs in AI and 

related technologies and to rapidly 

transition those breakthroughs into 

capabilities that contribute to U.S.

economic and national security.”1

Over the last several decades, fundamental 

research in information technology 

conducted at universities with Federal

funding, as well as in industry, has led to 

new, multi-billion-dollar sectors of the 

Nation’s economy.127 Concurrent advances 

across government, universities, and 

industry have been mutually reinforcing and 

have led to an innovative, vibrant AI sector. 

Many of today’s AI systems have been 

enabled by the American government-university-industry R&D ecosystem (see sidebar).

Since the release of the 2016 National AI R&D Strategic Plan, additional emphasis has been placed on 

the benefits of public-private partnerships. These benefits include strategically leveraging resources, 

including facilities, datasets, and expertise, to advance science and engineering innovations; 

 126 Restoring the Foundation: The Vital Role of Research in Preserving the American Dream (American Academy of Arts and 

Sciences, Cambridge, MA, 2014); https://www.amacad.org/multimedia/pdfs/publications/researchpapersmonographs

/AmericanAcad_RestoringtheFoundation_Brief.pdf .

127 National Research Council Computer Science Telecommunications Board, Continuing Innovation in Information 

Technology (The National Academies Press, Washington, D.C., 2012); https://doi.org/10.17226/13427.

Advancing the Nation’s AI innovation ecosystem, 

spanning government, universities, and industry

 Deep convolutional neural networks have proven to 

be a key innovation rooted in AI research. Although 

this modeling approach emerged from early Federal 

investments in the late 1980s, there were not 

enough data nor enough computational capabilities 

available at the time for neural networks to make 

accurate predictions. Through the combination of a 

rise in big data, today’s data-intensive scientific 

methods, and conceptual advances in how to 

structure and optimize the networks, neural 

networks have re-emerged as a useful way to 

improve accuracy in AI models. Interactions 

between academia and the private sector, including 

government funding, in recent years have helped 

reduce the error rate in speech recognition systems, 

enabling innovations such as real-time 

translation.

126

 Similarly, Federal investments in reinforcement 

learning in the 1980s and 1990s—an approach 

rooted in behavioral psychology that involves 

learning to associate behaviors with desired 

outcomes—have led to today’s deep learning 

systems. Through interactions across sectors,

computers are increasingly learning like humans, 

without explicit instruction, and reinforcement 

learning is driving progress in self-driving cars and 

other forms of automation where machines can hone 

skills through experience. Reinforcement learning 

was the key technology underlying AlphaGo, the 

program that defeated the world’s best Go players,

which has seen a growing number of victories over 

professional players since 2016.126
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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accelerating the transition of these innovations to practice; and enhancing education and training for 

next-generation researchers, technicians, and leaders. Government-university-industry R&D 

partnerships bring pressing, real-world challenges faced by industry to university researchers, enabling 

“use-inspired research”; leverage industry expertise to accelerate the transition of open and published 

research results into viable products and services in the marketplace for economic growth; and grow 

research and workforce capacity by linking university faculty and students with industry 

representatives, industry settings, and industry jobs (see sidebar).126,128,129,130 These partnerships build 

upon joint engagements among Federal agencies that enable synergies in areas where agencies’ 

missions intersect. The Nation also benefits from relationships between Federal agencies and 

international funders who can work together to address key challenges of mutual interest across a 

range of disciplines.

While there are many structures and mechanisms for public-private partnerships, some common 

categories for engagement include: 

1. Individual project-based collaborations. These efforts enable engagement by university 

researchers with those in other sectors, including Federal agencies, industry, and international 

organizations, to identify and leverage synergies in areas of mutual interest.

2. Joint programs to advance open, precompetitive, fundamental research. Direct partnerships 

among organizations across sectors enable funding and support for open, precompetitive, 

fundamental research in areas of mutual interestto the partners. In general, non-Federal partners 

contributing research resources receive the same intellectual property rights afforded to the U.S. 

Government by the Bayh-Dole Act.

131

3. Collaborations to deploy and enhance research infrastructure. Collaborations among Federal 

agencies, industry, and international organizations significantly enhance the potential for 

developing new and enhancing existing research infrastructure that in turn enables 

groundbreaking experimentation by researchers. Partners may offer financial and/or in-kind 

support to develop and/or enhance research infrastructure.

4. Collaborations to enhance workforce development including broadening participation. Multisector 

partnerships set the foundation for rigorous, engaging, and inspiring instructional materials that 

enhance workforce development and diversity in STEM professions.

In each of these cases, leveraging each partner’s strengths for the benefit of all is vitally important to 

achieving success.

 128 Mathematical Sciences Research Institute report, “Partnerships: A Workshop on Collaborations between the NSF/MPS 

& Private Foundations,” 2015; http://library.msri.org/msri/Partnerships.pdf.

129 Computing Community Consortium, “The Future of Computing Research: Industry-Academic Collaborations,” 2016; 

http://cra.org/ccc/wp-content/uploads/sites/2/2016/06/15125-CCC-Industry-Whitepaper-v4-1.pdf.

130 Computing Community Consortium, “Evolving Academia/Industry Relations in Computing Research: Interim Report 

released by the CCC,” 2019; https://www.cccblog.org/wp-content/uploads/2019/03/Industry-Interim-Report-wfootnotes.pdf. 

131 https://history.nih.gov/research/downloads/PL96-517.pdf
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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Advances in AI R&D stand to benefit 

from all of these types of publicprivate partnerships. Partnerships can 

promote open, precompetitive,

fundamental AI R&D; enhance access 

to research resources such as 

datasets, models, and advanced 

computational capabilities; and foster 

researcher exchanges and/or joint 

appointments between government, 

universities, and industry to share AI 

R&D expertise. Partnerships can also 

promulgate the inherently 

interdisciplinary nature of AI R&D, 

which requires convergence between 

computer and information science, 

cognitive science and psychology, 

economics and game theory, 

engineering and control theory, ethics,

linguistics, mathematics and statistics, 

and philosophy to drive the 

development and evaluation of future 

AI systems that are fair, transparent, 

and accountable, as well as safe and 

secure. Federal agencies are actively 

pursuing public-private partnerships 

to achieve these goals (see sidebar).

Federal agencies must therefore 

continue to pursue and strengthen 

public-private partnerships in AI R&D 

to drive technology development and 

economic growth by leveraging 

investments and expertise in areas of 

mutual interest to government, 

industry, and academia. In doing so, 

the U.S. Government will capitalize on 

a uniquely American innovation 

ecosystem that has transformed 

nearly every aspect of the Nation’s 

economy and society over the last two 

decades through novel information 

technologies.127

 132 https://www.diu.mil/

133 https://www.dhs.gov/science-and-technology/svip

Public-private partnerships: 

Recent agency R&D programs

A number of agencies have already initiated public-private 

partnerships in support of AI R&D:

 The Defense Innovation Unit (DIU)132 is a DoD organization 

that solicits commercial solutions capable of addressing 

DoD needs. The DIU in turn provides pilot contracts, which 

can include hardware, software, or other unique services. 

If successful, pilot contracts lead to follow-on contracts 

between companies and any DoD entity. A key DIU feature 

is the rapid pace of the pilot and subsequent contracts.

 NSF and the Partnership on AI, a diverse, 

multistakeholder organization working to better 

understand AI’s impacts, are partnering to jointly support 

high-risk, high-reward research at the intersection of the 

social and technical dimensions of AI.15

 The DHS Science and Technology Directorate’s Silicon 

Valley Innovation Program (SVIP)133 looks to harness 

commercial R&D innovation ecosystems across the Nation 

and around the world for technologies with government 

applications. SVIP employs a streamlined application and 

pitch process; brings government, entrepreneurs, and 

industry together to find cutting-edge solutions; and coinvests in and accelerates transition to market.

 The Department of Health and Human Services (HHS) 

piloted the Health Tech Sprint initiative, also known in its 

first iteration as “Top Health,” modeled in part after the 

Census Bureau's Opportunity Project. This effort created a 

nimble framework to public-private collaborations 

around bidirectional data links. It piloted new models for 

iterating on data release for AI training and testing, and it 

developed a voluntary incentivization framework for a

public–private AI ecosystem. 

 The HHS Division of Research, Innovation, and Ventures is 

part of the Biomedical Advanced Research and Development

Authority at the Office of the Assistant Secretary for 

Preparedness and Response. It oversees an accelerator 

network and is recruiting a nonprofit partner that can work 

with private investors to fund innovative technologies and 

products to solve systemic health security challenges, 

with AI applications being one area of interest. 

Accelerators will connect startups and other businesses

with product development and business support services. 
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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Abbreviations

AFOSR Air Force Office of Scientific Research

AI artificial intelligence

DARPA Defense Advanced Research 

Projects Agency

DHS Department of Homeland Security

DoD Department of Defense

DOE Department of Energy

DOT Department of Transportation

FDA Food and Drug Administration

FRVT Face Recognition Vendor Test

GPS Global Positioning System

GPU graphics processing unit

GSA General Services Administration

HHS Department of Health and Human 

Services

HPC high-performance computing

IARPA Intelligence Advanced Research 

Projects Activity

IEC International Electrotechnical 

Commission

IEEE Institute of Electrical and 

Electronics Engineers

IMPACT Information Marketplace for Policy 

and Analysis of Cyber-risk & Trust

(DHS)

ISO International Organization for 

Standardization 

IT information technology

IWG interagency working group

ML machine learning

MLAI Machine Learning and Artificial 

Intelligence (Subcommittee of the 

NSTC)

NASA National Aeronautics and Space 

Administration

NCO National Coordination Office for 

NITRD

NDS Naturalistic Driving Study (DOT)

NIFA National Institute of Food and 

Agriculture (USDA)

NIH National Institutes of Health

NIST National Institute of Standards and

Technology

NITRD Networking and Information 

Technology Research and 

Development program

NLM National Library of Medicine (NIH)

NSF National Science Foundation

NSTC National Science and Technology 

Council

NTIA National Telecommunications and 

Information Administration

ODNI Office of the Director of National 

Intelligence

OSTP Office of Science and Technology 

Policy

R&D research and development

RFI Request for Information

STEM science, technology, engineering, 

and mathematics

SVIP Silicon Valley Innovation Program 

(DHS)

TREC Text Retrieval Conference

USDA U.S. Department of Agriculture

VA U.S. Department of Veterans Affairs

XAI explainable AI
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

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National Science & Technology Council

Chair

Kelvin Droegemeier, Director, OSTP

Staff

Chloé Kontos, Executive Director, NSTC

Select Committee on Artificial Intelligence

Co-Chairs

Michael Kratsios, Deputy Assistant to the 

President for Technology Policy (The White 

House)

France A. Córdova, Director, NSF

Steven Walker, Director, DARPA

Subcommittee on Machine Learning and Artificial Intelligence

Co-Chairs

Lynne Parker, Assistant Director for Artificial 

Intelligence, OSTP

James Kurose, Assistant Director, Directorate 

for ComputerInformation Science and

Engineering (CISE), NSF

Charles Romine, Director, Information 

Technology Laboratory, NIST

Stephen Binkley, Deputy Director, Science 

Programs, Office of Science, DOE

Executive Secretary 

Faisal D’Souza, NITRD NCO

Subcommittee on Networking & Information Technology Research & Development

Co-Chairs

Kamie Roberts, Director, NITRD NCO James Kurose, Assistant Director, CISE, NSF

Executive Secretary 

Nekeia Butler, NITRD NCO

Artificial Intelligence Research & Development Interagency Working Group

Co-Chairs

Jeff Alstott, Program Manager, IARPA Office of 

the Director of National Intelligence (ODNI)

Henry Kautz, Division Director, CISE Division 

of Information and Intelligent Systems, NSF

Staff

Faisal D’Souza, Technical Coordinator, NITRD NCO

Strategic Plan Writing Team

Jeff Alstott, IARPA

Gil Alterovitz, VA

Sameer Antani, NIH

Charlotte Baer, NIFA, USDA

Daniel Clouse,ODNI

Faisal D’Souza, NITRD NCO

Kimberly Ferguson-Walter, 

U.S. Navy

Michael Garris, NIST

Erwin Gianchandani, NSF

Ross Gillfillan, OSTP

Travis Hall, NTIA

Meghan Houghton, NSF

Henry Kautz, NSF

Erin Kenneally, DHS

David Kuehn, DOT

James Kurose, NSF

James Lawton, AFOSR

Steven Lee, DOE

Aaron Mannes, DHS

Lynne Parker, OSTP

Dinesh Patwardhan, FDA

Elham Tabassi, NIST
THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

About the National Science and Technology Council

The National Science and Technology Council (NSTC) is the principal means by which the Executive Branch coordinates science and

technology policy across the diverse entities that make up the Federal research and development enterprise. A primary objective 

of the NSTC is to ensure that science and technology policy decisions and programs are consistent with the President’s stated goals. 

The NSTC prepares research and development strategies that are coordinated across Federal agencies aimed at accomplishing 

multiple national goals. The work of the NSTC is organized under committees that oversee subcommittees and working groups 

focused on different aspects of science and technology. More information is available at https://www.whitehouse.gov/ostp/nstc.

About the Office of Science and Technology Policy

The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology Policy, Organization, 

and Priorities Act of 1976 to provide the President and others within the Executive Office of the President with advice on the 

scientific, engineering, and technological aspects of the economy, national security, homeland security, health, foreign relations, 

the environment, and the technological recovery and use of resources, among other topics. OSTP leads interagency science and 

technology policy coordination efforts, assists the Office of Management and Budget with an annual review and analysis of Federal 

research and development (R&D) in budgets, and serves as a source of scientific and technological analysis and judgment for the 

President with respect to major policies, plans, and programs of the Federal Government. More information is available at 

https://www.whitehouse.gov/ostp.

About the Select Committee on Artificial Intelligence

The Select Committee on Artificial Intelligence (AI) advises and assists the NSTC to improve the overall effectiveness and productivity 

of Federal R&D efforts related to AI to ensure continued U.S. leadership in this field. It addresses national and international policy 

matters that cut across agency boundaries, and it provides formal mechanisms for interagency policy coordination and development 

for Federal AI R&D activities, including those related to autonomous systems, biometric identification, computer vision, humancomputer interactions, machine learning, natural language processing, and robotics. It also advises the Executive Office of the President 

on interagency AI R&D priorities; works to create balanced and comprehensive AI R&D programs and partnerships; leverages Federal

data and computational resources across department and agency missions; and supports a technical, national AI workforce.

About the Subcommittee on Machine Learning and Artificial Intelligence 

The Machine Learning and Artificial Intelligence (MLAI) Subcommittee monitors the state of the art in machine learning (ML) and 

artificial intelligence within the Federal Government, in the private sector, and internationally to watch for the arrival of important 

technology milestones in the development of AI, to coordinate the use of and foster the sharing of knowledge and best practices 

about ML and AI by the Federal Government, and to consult in the development of Federal MLAI R&D priorities. The MLAI 

Subcommittee reports to the Committee on Technology and the Select Committee on AI. The MLAI Subcommittee also coordinates

AI taskings with the Artificial Intelligence Research & Development Interagency Working Group (see below).

About the Subcommittee on Networking & Information Technology Research & Development

The Networking and Information Technology Research and Development (NITRD) Program is the Nation’s primary source of 

Federally funded work on pioneering information technologies (IT) in computing, networking, and software. The NITRD 

Subcommittee guides the multiagency NITRD Program in its work to provide the R&D foundations for assuring continued U.S. 

technological leadership and meeting the needs of the Nation for advanced IT. It reports to the NSTC Committee on Science and 

Technology Enterprise. The Subcommittee is supported by the interagency working groups that report to it and by its National 

Coordination Office. More information is available at https://www.nitrd.gov/about/.

About the Artificial Intelligence Research & Development Interagency Working Group

The NITRD AI R&D Interagency Working Group (IWG) coordinates Federal R&D in AI; it also supports and coordinates activities 

tasked by the Select Committee on AI and the NSTC Subcommittee on Machine Learning and Artificial Intelligence. This vital work 

promotes U.S. leadership and global competitiveness in AI R&D. The NITRD AI R&D IWG spearheaded the update of this National 

Artificial Intelligence Research and Development Strategic Plan. More information is available at https://www.nitrd.gov/groups/AI.

About this Document

This document includes the original text from the 2016 National AI R&D Strategic Plan with updates prepared in 2019 following 

Administration and interagency evaluation of the 2016 Plan and of community responses to a Request for Information on updating 

the Plan. The 2016 strategies were broadly determined to be valid going forward with some reemphases and with a call for a new 

strategy on Private-Public Partnerships in AI. A shaded call-out box has been inserted at the top of each strategy to highlight 

updated imperatives and/or new focus areas. The 2019 update adds an entirely new Strategy 8 on Private-Public Partnerships in AI.

Copyright Information

This document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). It may be freely 

distributed, copied, and translated, with acknowledgment to OSTP; requests to use any images must be made to OSTP. 

Published in the United States of America, 2019.