Strengthening and Democratizing
the U.S. Artificial Intelligence
Innovation Ecosystem
An Implementation Plan for a
National Artificial Intelligence Research Resource

January 2023

Strengthening and Democratizing the U.S.
Artificial Intelligence Innovation Ecosystem:
An Implementation Plan for a National
Artificial Intelligence Research Resource
National Artificial Intelligence Research Resource Task Force
January 2023

Dear Mr. President and Members of Congress,
Artificial Intelligence (AI) is changing our country and our world. From how citizens
navigate their daily lives to how researchers drive discoveries in the lab to how
manufacturers build products, AI is giving rise to new capabilities. New AI and AI-driven
discoveries and capabilities hold the potential to drive practical solutions to addresscritical global challenges such as food production, climate change, poverty, and cancer.
We have only started to scratch the surface of what is possible, and cannot afford to missout on seizing the opportunity for leveraging AI to serve the public good.
However, the opportunities to pursue cutting-edge AI research and apply AI to new
domains and challenges are currently not accessible by all of America's incredible talent
nor harnessed by the public sector. Much of today's AI research relies on access to large
volumes of data and advanced computational power, which are often unavailable toresearchers beyond those at well-resourced technology companies and universities. This
access divide limits the ability to leverage AI to tackle the big challenges in our society. It
also constrains the diversity of researchers in the field and the breadth of ideas
incorporated into AI innovations, contributing to embedded biases and other systemic
inequalities found in AI systems today.
Recognizing this challenge, in the National AI Initiative Act of 2020, Congress directed the
National Science Foundation (NSF), in consultation with the White House Office of
Science and Technology Policy (OSTP), to establish a task force to create a roadmap for a
National AI Research Resource (NAIRR)—a shared research infrastructure that would
provide AI researchers and students with significantly expanded access to computational
resources, high-quality data, educational tools, and user support.
This final report of the NAIRR Task Force presents a roadmap and implementation plan
for a national cyberinfrastructure aimed at overcoming the access divide, reaping thebenefits of greater brainpower and more diverse perspectives and experiences applied todeveloping the future of AI technology and its role in our society. Such a national
cyberinfrastructure also presents a unique and critical opportunity to "design in" thestandards for responsible AI research practices and governance processes that uphold
our priority to develop and harness these groundbreaking technologies in a manner thatreinforces our Nation's democratic values and Americans' personal freedoms.

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OSTP and NSF formally launched the NAIRR Task Force in June 2021, appointing 12
leading experts equally representing academia, government, and private organizations.
Over the course of its work, the Task Force held 11 public meetings, engaged with 65
experts on a wide range of aspects related to the design of the NAIRR, and considered
responses from the public to two requests for information. We extend our gratitude to
the members of the Task Force who have donated an extraordinary number of hours of
their time to this effort, as well as to the many members of the public who havecontributed their expertise and provided inputs to the Task Force. The result of the last
one and one-half years of effort is this final report.
We see the NAIRR as a foundational investment that would amplify efforts across theFederal Government to cultivate AI innovation and advance trustworthy AI. Research,experimentation, and innovation are integral to our progress as a Nation, and it isimperative that we engage people from every zip code and every background to live up
to America's unique promise of possibility and ensure our leadership on the world stage.
The work of the NAIRR Task Force and this report will be an invaluable resource as wework collaboratively across government and across sectors to drive this important work
forward.
Sincerely,

Sethuraman Panchanathan
Director
National Science Foundation

Arati Prabhakar
Assistant to the President for
Science and Technology
Director, Office of Science andTechnology Policyiii

Executive Summary
Artificial Intelligence (AI) is an engine of innovation that is driving scientific discovery andeconomic growth. It is increasingly becoming an integral component of solutions that stand to
impact everything from routine daily tasks to societal-level challenges, ultimately serving the
public good. At the same time, there are also concerns that AI could have negative social andenvironmental consequences. To realize the positive and transformative potential of AI, it isimperative to harness all of America's ingenuity to advance the field in a manner that addresses
societal challenges, works for all Americans, and upholds our democratic values.
Yet progress at the current frontiers of AI is often tied to access to large amounts ofcomputational power and data. Such access today is too often limited to those in well-resourcedorganizations. This large and growing resource divide has the potential to limit and adversely skew
our AI research ecosystem. The imbalance threatens our Nation’s ability to cultivate an AI researchcommunity and workforce that reflect America's rich diversity and the ability to harness AI toadvance the public good.
A widely accessible AI research cyberinfrastructure that brings together computational
resources, data, testbeds, algorithms, software, services, networks, and expertise, as described inthis report, would help to democratize the AI research and development (R&D) landscape in the
United States for the benefit of all. It would help create pathways to broaden the range of
researchers involved in AI, and to grow and diversify approaches to, and applications of, AI. Thiscyberinfrastructure can also help to open up new opportunities for progress across all scientificfields and disciplines, including in critical areas such as AI auditing, testing and evaluation,
trustworthy AI, bias mitigation, and AI safety. Increased access and a diversity of perspectives
can, in turn, lead to new ideas that would not otherwise materialize and set the conditions fordeveloping AI systems that are inclusive by design.
As part of the National Artificial Intelligence Initiative Act of 2020, Congress established the
National Artificial Intelligence Research Resource (NAIRR) Task Force to "investigate thefeasibility and advisability of developing" the NAIRR as a national AI research
cyberinfrastructure, and "to propose a roadmap detailing [how the NAIRR] should be establishedand sustained." The recent CHIPS and Science Act of 2022 reinforces the importance of
democratizing access to a national AI research cyberinfrastructure, via investments that will
accelerate development of advanced computing—from next-generation graphics processing units
to high-density memory chips—as well as steps to actively engage broad and diverse U.S. talent
in frontier science and engineering, including AI.
This final report is the culmination of the Task Force's 18-month effort to develop a visionand implementation plan for establishing the NAIRR. It builds on the findings andrecommendations outlined in the Task Force's interim report released in May 2022, providing animplementation plan to achieve the objective of the NAIRR: to strengthen and democratize theU.S. AI innovation ecosystem in a way that protects privacy, civil rights, and civil liberties.

iv

The NAIRR should be established with four measurable goals in mind, namely to(1) spur innovation, (2) increase diversity of talent, (3) improve capacity, and (4) advancetrustworthy AI. The NAIRR should meet these goals by supporting the needs of researchers andstudents from diverse backgrounds who are pursuing foundational, use-inspired, and translational
AI research. These users should be U.S.-based or affiliated with U.S. organizations, to include
academic institutions, non-profit organizations, and startups or small businesses.
The NAIRR should comprise a federated set of computational, data, testbed, and software
resources from a variety of providers, along with technical support and training, to meet the needsof this target user base. The specific design, implementation, and evaluation of the NAIRR shouldbe centered around the four key goals, and should support the collection of data for assessment of
key indicators of system performance and success in progress toward these goals.
The NAIRR administration and governance should follow a cooperative stewardshipmodel, whereby a single Federal agency serves as the administrative home for NAIRR
operations and a Steering Committee comprising principals from Federal agencies withequities in AI research drives the strategic direction of the NAIRR. A Program Management
Office within the administrative home agency should provide funding and oversight for an
independent Operating Entity that manages the day-to-day operations of the NAIRR. The Steering
Committee, co-chaired by the National AI Initiative Office (NAIIO), would incorporate interestsand perspectives from across Federal agencies in the governance of the NAIRR. These agencies
should also directly support resource providers whose resources, in federation, would constitutethe NAIRR. Diverse perspectives and expertise should be tapped to inform the NAIRR's operations
through a User Committee, a Science Advisory Board, a Technology Advisory Board, and an
Ethics Advisory Board that provide advice to the Operating Entity.
The NAIRR should provide access to a federated mix of computational and data
resources, testbeds, software and testing tools, and user support services via an integrated
portal. Computational resources should include conventional servers, computing clusters, highperformance computing, and cloud computing, and should support access to edge computing
resources and testbeds for AI R&D. Open and protected data should be made available undertiered-access protocols and co-located with computational resources. The Operating Entity should
not itself operate the totality of the computer hardware that composes the NAIRR; instead,computing, along with data, testing, and training resources, should be delivered as services bypartner resource providers selected through Federal agency or multi-agency funding opportunities.
When fully implemented, the NAIRR should address both the capacity (ability to support a large
number of users) and capability (ability to train resource-intensive AI models) needs of the AI
research community.
The NAIRR must be broadly accessible to a range of users and provide a platform thatcan be used for educational and community-building activities in order to lower the barriers
to participation in the AI research ecosystem and increase the diversity of AI researchers.
The NAIRR access portal and public website should provide catalogs and search and discovery
tools to facilitate access to data, testbeds, and educational and training resources serving a rangeof experience levels.

v

The NAIRR should set the standard for responsible AI research through the design andimplementation of its governance processes. The NAIRR must be proactive in addressing
privacy, civil rights, and civil liberties issues by integrating appropriate technical controls, policies,and governance mechanisms from its outset. The Operating Entity should work with its Ethics
Advisory Board to develop criteria and mechanisms for evaluating proposed research andresources for inclusion in the NAIRR from a privacy, civil rights, and civil liberties perspective.
Regular training should be required to build NAIRR users' awareness about rights, responsibilities,
and best practices related to privacy, civil rights, and civil liberties in AI research, in accordancewith the Blueprint for an AI Bill of Rights published by the White House Office of Science andTechnology Policy in October 2022.
The NAIRR should implement system safeguards in accordance with establishedguidelines. These guidelines include those developed by the National Institute of Standards andTechnology (NIST) and the Five Safes framework: safe projects, safe people, safe settings, safe
data, and safe outputs. The Operating Entity should design the NAIRR cyberinfrastructure toconsist of multiple tiers, starting with two primary zones: an open science zone "NAIRR-Open"and a secure zone "NAIRR-Secure." Each zone should federate computational, network, and data
resources operating in accordance with security and access-control policies that are uniform within
the zone, but different between zones, reflecting the different priorities and needs of the users andresource operators. NAIRR-Open should adopt the best practices developed over two decades in
the open science community; be consistent with Federal open data, open government, and research
security policies; and manage access using single sign-on authentication and a resource allocation
mechanism managed by the Operating Entity. NAIRR-Secure should consist of one or more secure
enclaves adhering to a common set of security controls, and have the ability to support security
requirements arising from legally protected data.
NAIRR implementation should occur over four phases, beginning immediately after the
publication of this report. In phase one, Congress should authorize and appropriate funds toestablish the NAIRR. The administrative home agency and the NAIIO should coordinate theformation of the Steering Committee and stand up a Program Management Office, which will thenprepare the solicitation for the Operating Entity and manage the selection process.

Phased NAIRR Implementation Timeline

In phase two, the Operating Entity should establish its activities and oversee creation of the
NAIRR portal and user interface, building in appropriate technical and policy controls. The
architecture should support collection of key performance indicators for evaluation of NAIRR
progress. Resource providers should be selected via coordinated, multi-agency funding
opportunities ideally released within six months of the initial Operating Entity award.

viIn phase three, the NAIRR should achieve initial operational capability and the Operating
Entity should also formalize the policies, processes, and initial technical resources to be madeavailable to AI researchers. Initial capabilities include (1) a portal and user support resources, (2) amix of computational resource providers, (3) an allocation and identity system, and (4) a data
publication system. In phase four, activities should transition from building out the NAIRR toestablishing steady-state operations, as well as the planned evolution of NAIRR resources in
response to user uptake and demand.
Finally, the Task Force also presents a pilot option for the implementation of the NAIRR thatwould be initiated in parallel with the above phases to expedite the availability of NAIRR resources
to the AI R&D community.
As envisioned, the impact of the NAIRR will be significant and far-reaching, enabling
researchers to tackle problems that range from routine tasks to global challenges. In orderto achieve its vision and goals, the Task Force estimates the budget for the NAIRR as $2.6
billion over an initial six-year period. The bulk of this investment ($2.25 billion) is to fund theresources to be made accessible via the NAIRR, through appropriations to multiple Federal
agencies. The Task Force estimated this budget based on recent costs of advanced computing
resources as well as data, training, and software resources; estimates of usage levels to meet thecurrent needs of the AI R&D community; and expected growth of the AI R&D community.
Resource providers should be brought online every two years with a six-year lifetime, so that a
new $750 million investment is made every two years to ensure that the NAIRR resources remainstate-of-the-art. The Operating Entity will require between $55 million and $65 million per year
to support the coordination and management of NAIRR activities. An additional $5 million peryear is budgeted for external evaluation of the Operating Entity and NAIRR performance.
The vision for the NAIRR laid out in this report is designed to meet the national need forincreased access to the state-of-the-art resources that fuel AI innovation. The roadmap forachieving this vision builds on existing Federal investments; designs in protections for privacy,
civil rights, and civil liberties; and promotes diversity and equitable access. If successful, theNational AI Research Resource would transform the U.S. national AI research ecosystemand facilitate the ability to address societal-level problems by strengthening anddemocratizing participation in foundational, use-inspired, and translational AI R&D in the
United States.

vii

Contents
1. Introduction ...................................................................................................................................1
The Current Landscape of AI R&D ....................................................................................................................... 1
An Opportunity for Strengthening AI R&D in the United States ......................................................................... 3
The National AI Research Resource Task Force ................................................................................................... 4
Structure of This Report ...................................................................................................................................... 6

2. A National Cyberinfrastructure to Democratize and Accelerate AI R&D............................................ 7
NAIRR Vision and Goals ....................................................................................................................................... 7
The NAIRR User Base ........................................................................................................................................... 8
NAIRR Constituents ........................................................................................................................................... 10
Government ............................................................................................................................................. 10
Academia ................................................................................................................................................. 10
Industry .................................................................................................................................................... 11
Civil Society .............................................................................................................................................. 11

3. NAIRR Organization, Management, and Governance ..................................................................... 12
NAIRR Organizational Structure ........................................................................................................................ 12
Steering Committee ................................................................................................................................. 14
Individual Agencies .................................................................................................................................. 15
Program Management Office .................................................................................................................. 17
Operating Entity ...................................................................................................................................... 18
NAIRR Staff and Executive Leadership Team ........................................................................................... 20
NAIRR Advisory Boards ............................................................................................................................ 21
Evaluation Entity ...................................................................................................................................... 22
User Access and Resource Allocation ................................................................................................................ 22
Privacy, Civil Rights, and Civil Liberties Protections........................................................................................... 24
Scientific Integrity .............................................................................................................................................. 26
System Security and User Access Controls ........................................................................................................ 26
Open-Source Principles ............................................................................................................................ 28
Environmental Sustainability ............................................................................................................................. 28

4. NAIRR Structure and Specifications for Resource Elements ............................................................ 30
Access Portal and User Interface ....................................................................................................................... 30
Computational Resources .................................................................................................................................. 31
Capacity and Capability ........................................................................................................................... 31
NAIRR Software Resources ...................................................................................................................... 31
Data and Datasets.............................................................................................................................................. 32
Dataset Acceptance Criteria and Metadata Standards ........................................................................... 33
Role of the Operating Entity in Incentivizing and Curating Contributed Datasets and Other
Resources ................................................................................................................................................. 33
The NAIRR and Existing Federal Government Data ................................................................................. 34
Legal Compliance ..................................................................................................................................... 35
Co-Location of Resources .................................................................................................................................. 36
Educational Tools and Services.......................................................................................................................... 36viii

Tiered Technical Training and Support .................................................................................................... 37
Curation of Training Materials ................................................................................................................ 37
Platform for Educational Activities .......................................................................................................... 37
Technical Integration ......................................................................................................................................... 37
Software for Integration .......................................................................................................................... 37
Integrating Data Resources ..................................................................................................................... 38
Testbeds ............................................................................................................................................................ 38

5. Phased Buildout of NAIRR Organization and Resources ................................................................. 40
Phase 1: Program Launch and Operating Entity Selection ................................................................................ 41
Phase 2: Operating Entity Startup ..................................................................................................................... 41
Internal Planning and Operations ............................................................................................................ 41
Establishment of Initial NAIRR Resource Components ............................................................................ 42
Phase 3: NAIRR Initial Operational Capabilities ................................................................................................. 44
Initial Computational Resources .............................................................................................................. 44
Initial Data Resources .............................................................................................................................. 45
Initial Research Using the NAIRR ............................................................................................................. 45
Phase 4: NAIRR Ongoing (Steady-State) Operations ......................................................................................... 46
Evolution of NAIRR Resources .................................................................................................................. 46
Partnership Engagement Operations ...................................................................................................... 47
User Outreach, Engagement, and Support Operations ........................................................................... 47
Outreach and International Collaboration .............................................................................................. 47
NAIRR Budget .................................................................................................................................................... 48
NAIRR Evaluation (Phases 1–4).......................................................................................................................... 50
Roadmap for Implementation ........................................................................................................................... 52
Steps to Initiate the NAIRR in 2023: Actions for the U. S. Government ............................................................ 54
For the President and Executive Branch Departments and Agencies ...................................................... 54
For Congress ............................................................................................................................................ 54

6. Conclusion ................................................................................................................................... 55
Appendix A. Definitions ................................................................................................................. A-1
Appendix B. Details of NAIRR Task Force Establishment and Approach to Roadmap
Development.............................................................................................................. B-1
Charge to the NAIRR Task Force ...................................................................................................................... B-1
Task Force Approach ....................................................................................................................................... B-2
Initial Phase ........................................................................................................................................... B-2
Final Phase ............................................................................................................................................. B-3

Appendix C. Briefers to the Task Force .............................................................................................C-1
Appendix D. Public Input Provided on the Interim Report in Response to the Federal Request for
Information ................................................................................................................ D-1
Appendix E. Public Input Provided on the Initial Federal Request for Information on Designing
the NAIRR.................................................................................................................... E-1
Appendix F. Subject Matter Experts Consulted by Task Force Members ............................................ F-1
Appendix G. NAIRR Public Listening Session.................................................................................... G-1
Appendix H. NAIRR Task Force Staff and Contributors ..................................................................... H-2
Appendix I. Examples of NAIRR Evaluation Metrics ...........................................................................I-1

ix

Appendix J. Draft Legislative Language for NAIRR Authorization....................................................... J-1
Appendix K. Abbreviations ..............................................................................................................K-1
Appendix L. Notes ........................................................................................................................... L-1

List of Figures
Figure 1. The Current Fabric of U.S. Research Cyberinfrastructure.............................................................. 6
Figure 2. NAIRR Strategic Objective and Goals ............................................................................................. 8
Figure 3. A Vision for NAIRR Users and Resource Elements ......................................................................... 9
Figure 4. Proposed NAIRR Governance Structure....................................................................................... 14
Figure 5. Process for Selection and Integration of NAIRR Resource Providers........................................... 16
Figure 6. Example Elements of a Theory of Change for the NAIRR ............................................................ 50
Figure 7. NAIRR Implementation Roadmap ................................................................................................ 53

List of Tables
Table 1. NAIRR Six-Year Budget Summary .................................................................................................. 48
Table 2. Operating Entity Costs................................................................................................................... 50
Table 3. Roles in KPI Definition and Frequency of External Evaluation ...................................................... 51
Table H.1. Examples of Metrics that Could be Associated with a NAIRR Theory of Change ...................... I-1
Table H.2. Examples of Metrics for Assessing Progress toward NAIRR Goals ............................................ I-2xTask Force Membership
TESS DEBLANC-KNOWLES (CO-CHAIR, BEGINNING AUGUST 2022)
Senior Policy Advisor, National AI Initiative Office,White House Office of Science and Technology Policy

MANISH PARASHAR (CO-CHAIR, BEGINNING OCTOBER 2021)
Office Director of the Office of Advanced Cyberinfrastructure,
National Science Foundation

LYNNE PARKER (CO-CHAIR, JULY 2021–AUGUST 2022)

Former Deputy U.S. Chief Technology Officer and Founding Director, National AI Initiative Office,White House Office of Science and Technology Policy

ERWIN GIANCHANDANI (CO-CHAIR, JULY 2021–OCTOBER 2021)
Assistant Director for Technology, Innovation, and Partnerships,
National Science Foundation

DANIELA BRAGA

Founder & CEO of Defined.ai

MARK E. DEAN
OREN ETZIONICEO, Allen Institute for AI

JULIA LANE

Professor, New York University

FEI-FEI LI

Sequoia Professor of Computer Science at Stanford University and Denning Co-Director of the Stanford
Institute for Human-Centered AI (HAI)ANDREW MOORE

Vice President & General Manager, Google Cloud AI & Industry Solutions

MICHAEL L. NORMAN

Distinguished Professor, University of California, San DiegoDAN STANZIONE

Executive Director, Texas Advanced Computing Center
Associate Vice President for Research, The University of Texas at Austin

FREDERICK H. STREITZ

Chief Computational Scientist, Lawrence Livermore National Laboratory
Senior Advisor, CDC Center for Forecasting and Outbreak Analytics

ELHAM TABASSI

Chief of Staff, Information Technology Laboratory,
National Institute of Standards and Technologyxi

1. Introduction
The economic and national security of the United States has long relied on its unique andvibrant ecosystem for scientific discovery and technological innovation. The United States invests
in research and development (R&D) across science and engineering disciplines to advanceunderstanding of natural, built, and human systems and develop tools and methods for solving
practical challenges. This R&D leads to downstream development of applications and commercial
products that drive economic growth while supporting the human aspiration to explore,
understand, and improve the conditions of our world.
AI is increasingly a key driver of the Nation’s research and innovation ecosystem, as it holds
the potential to power discovery, innovation, and economic growth across every field of scienceand every sector of the economy. However, achieving this potential and harnessing AI to tackle
grand challenges require substantial and sustained investment in AI R&D as well as education andworkforce development. 1 It also requires access to the infrastructure necessary for AI
experimentation and training. Currently, uneven access to the resources that fuel AI R&D andtraining have limited opportunities for researchers and contributed to a lack of diversity in thefield. This lack of diversity means that the full range of talent is not being leveraged for this work.
Lack of diversity may also contribute to the development of biased or harmful AI systems andthreaten the Nation’s innovation potential and global leadership. Concerns related to misuse of AI
and environmental effects of AI development are also increasing. Making computational, data, andtraining resources available to more of America’s researchers through an approach grounded inequity and security can chart a path forward. In this future America can responsibly harness thepotential of AI for societal good and economic wellbeing—while also strengthening American
technological competitiveness for decades to come.

The Current Landscape of AI R&D
The term "Artificial Intelligence" refers to a machine-based system that can, for a given setof human-defined objectives, make predictions, recommendations, or decisions influencing real or
virtual environments (see Box 1). 2
AI systems can be applied totasks spanning diverse areas,
including planning and optimization,
perception and vision, modeling andsimulation,
naturallanguage
understanding,robotic
process
automation, recommendation, andprediction. These tasks can beaccomplished through statisticalinference extracted from "training"data (in the case of Machine Learning[ML]) or programmed logicalBox 1. Definition of Artificial Intelligence 3
The term "artificial intelligence" means a machine-based
system that can, for a given set of human-defined
objectives, make predictions, recommendations, ordecisions influencing real or virtual environments.
Artificial intelligence systems use machine and humanbased inputs to:(A) Perceive real and virtual environments.
(B) Abstract such perceptions into models throughanalysis in an automated manner.
(C) Use model inference to formulate options for
information or action.

1

reasoning (as with expert systems). Today, the computational and storage capacity of computer
systems has advanced to a stage where storage and analysis of large quantities of data has become
not only possible, but also an increasingly dominant enabler of R&D. Parallel development ofadvanced software tools and algorithms have facilitated realization of powerful analytical andpredictive methods based on AI, which are being applied broadly across fields of science andengineering.
AI technologies and sustained investments in cyberinfrastructure have supported scientificand technological breakthroughs in diverse areas such as protein folding, nuclear fusion, and evenprogramming. The breakthroughs did not happen by chance. They emerged from an ecosystemcharacterized by decades of systematic investments in cyberinfrastructure, education and training,
and large and growing amounts of data and computational power; and the rich collaborations
between academic researchers and the private sector. The potential for the U.S. researchcommunity to contribute to the global AI research and innovation ecosystem is growing.
In recent years, academia has seen a significant growth in AI and computer science researchand education. Since 2016, about 2,000 computer science faculty members have published at least
one AI-related paper, and on the order of 900 have published at least five. 4 In 2019 roughly 28,000
undergraduate students received degrees in computer science, more than doubling the number ofdegrees awarded in 2014. Those who pursue doctoral programs in computer science and related
fields in North America are increasingly choosing to specialize in AI: The share of new computerscience PhD recipients specializing in AI increased from 19 to 25 percent between 2019 and 2020,
for a total of 442 in 2020. 5
However, increased investments in AI research and education have not been distributed
equally across the Nation’s researchers and innovators. 6 Of the U.S. resident AI PhDs conferredin 2020, approximately 51 percent were awarded to non-Hispanic Whites, 30 percent to Asians,
7 percent to Hispanics (compared to their representing 18.9 percent of the U.S. population), and 2
percent to Blacks or African Americans (compared to their representing 13.6 percent of the U.S.
population). These numbers show a decrease in the percentage of AI PhDs awarded to Hispanic
and Black or African American students relative to 2010. 7 Similarly, gender diversity in AI is low
and has demonstrated little change over the past decade. According to one estimate, about 20
percent of both the AI PhD and computer science PhD graduates in North America in 2020 werefemale.7,8 This lack of diversity among students and graduates gives rise to a corresponding lackof diversity in the workforce, and contributes to the development of AI tools and approaches thatperpetuate systemic bias and limits the breadth of ideas incorporated into AI innovation. 9
While academic and private sector interest in AI has grown, access to the computational anddata resources that fuel much of today’s AI has become concentrated in large private-sector firms,
well-resourced universities, and national laboratories, creating a growing divide that limits
innovation and growth. 10 The resulting impact on U.S. innovation and economic growth is evident.
Even though private investment in AI more than doubled between 2020 and 2021 to approximately$93.5 billion, the number of new companies has decreased.8 The disparity in availability of AI
research resources affects the quality and character of the U.S. AI innovation ecosystem,
contributing to a “brain drain” of top AI talent from academic and research institutions to a small2set of well-resourced corporations. 11 Such trends have adverse implications for the Nation’s
capacity to train the breadth of talent required to support future U.S. competitiveness andinnovation.

An Opportunity for Strengthening AI R&D in the United States
Sustained investments in AI R&D have enabled the United States to be a longstanding global
leader in the field of AI, from the foundations of the field to the present day. Conference papers
and AI repository publications by U.S.-based authors remain the most cited globally. However,American dominance is currently threatened. Countries such as China have made long-term
investments that are bearing fruit in terms of both their scientific and technological achievements.
For example, authors based in China have overtaken U.S.-based authors in AI journal publication
citations. The United States has been granted more AI patents than any other nation, although AI
patent applications from China far surpass those from the United States.8 These trends illustrate
the rapidly changing AI innovation landscape as output in AI R&D continues to grow rapidly
worldwide and as leadership in AI and other emerging technologies has become a central facet of
geopolitical competition.
AI breakthroughs could accelerate progress across a range of mission areas of Federal
agencies: from energy and sustainability to healthcare and biomedical treatments to foundational
research. For example, AI could support a broad spectrum of actions needed to build a moresustainable future—from mitigation of greenhouse gas emissions and development of data-driven
strategies for conservation, to automated solutions for managing consumption and the inventionof new clean energy sources and materials.
Realizing the benefits of AI for the Nation will rely on the ability of all U.S. researchers toaccess the necessary cyberinfrastructure, especially researchers with limited resources or who havebeen historically excluded from AI and related fields and industries. Engaging the full diversity of
U.S. talent will bring important perspectives, research capacity, and inspiring use cases.
Critical opportunities for strengthening the U.S. AI R&D ecosystem exist in four key areas:•

Innovation: Bringing together complementary resources, capabilities, and skills could
enable new modalities of research, new understanding and knowledge, and new,transformative solutions.

•

Diversity: Engaging the full breadth of talent in the United States can help introduce
new ideas and use contexts for AI, and expand and strengthen the potential of AI R&D
in the United States, including for addressing a range of societal challenges.

•

Capacity: Increasing the development of and access to resources optimized forfoundational, use-inspired, and translational AI R&D is essential for supporting a
growing AI R&D community and its needs.

•

Trustworthiness: Practical and societal implications of AI must be considered in all AI
R&D, given its potential for ubiquitous application throughout the economy andsociety. As with any powerful and complex tool, AI comes with risks; responsibility for
managing such risks is shared across all phases of the AI life cycle, including R&D.
3

Supporting research on AI’s societal implications, developing testing and evaluation
approaches, improving auditing capabilities, and developing best practices forresponsible AI R&D can help improve understanding and yield tools to manage AI
risks.
Cultivating a vibrant and inclusive AI innovation ecosystem that reflects American values
will drive economic growth, national security, and scientific progress, which will in turn increase
America's future technological competitiveness. Such outcomes will not be possible through action
by any single sector or entity, but require collaborative action among government, academia, theprivate sector, and non-profits. 12
In January 2021, as part of the National Artificial Intelligence Initiative Act of 2020,3
Congress established the National AI Initiative to further coordinate and enhance Federal actions
toward four objectives: (1) ensure continued U.S. leadership in AI research and development; (2)lead the world in the development and use of trustworthy AI systems in the public and private
sectors; (3) prepare the present and future U.S. workforce for the integration of AI systems acrossall sectors of the economy and society; and (4) coordinate ongoing AI research, development, anddemonstration activities among the civilian agencies, the Department of Defense, and theIntelligence Community to ensure that each informs the work of the others. The Initiative codifies
sustained and consistent support for AI R&D through grants, cooperative agreements, testbeds,
and access to data and computing resources, and requires that the National AI R&D Strategic Plan
that focuses AI R&D investments across agencies be updated every three years.

The National AI Research Resource Task Force
As part of the National AI Initiative,
Congress established the National Artificial
Box 2. Definition of NAIRR3Intelligence Research Resource (NAIRR) Task
The terms "National Artificial Intelligence
Force, calling for it to “investigate the feasibility
Research Resource" and "Resource" refer toand advisability of establishing and sustaining aa system that provides researchers andstudents across scientific fields and[NAIRR] and to propose a roadmap detailingdisciplines with access to computational
how [a NAIRR] should be established andresources, co-located with publicly available,
sustained.”3 A widely accessible, AI-specificartificial intelligence-ready government andresearch cyberinfrastructure (as defined in Box
non-government datasets, and a research
environment with appropriate educational2) could meet the opportunities and challenges
tools and user support.
described above, in alignment with the National
AI R&D Strategic Plan, and help to build astronger, more inclusive U.S. AI R&D ecosystem. This vision is reinforced by the recent CHIPS
and Science Act of 2022, which appropriates funding to accelerate advanced computing
development, from next-generation graphics processing units to high-density memory chips, andauthorizes investments to help actively engage the full breadth and diversity of U.S. talent in thefrontiers of science and engineering, including AI. 13
The NAIRR Task Force strongly agrees that a shared, AI-focused federation of
cyberinfrastructure resources—including computer hardware, data, algorithms, software, services,
networks, and expertise—is necessary to transform the AI R&D landscape in the United States.
4More equitable access to computational power, large and unbiased datasets, and software tools isneeded to empower a diverse collection of individuals and teams across the country to advance AI
methods and technologies; use AI to make progress on science, engineering, and societal
challenges; and actively contribute to the development and adoption of AI systems, policies, andpractices that respect privacy, civil rights, and civil liberties. The NAIRR Task Force found thatdeveloping a NAIRR is both feasible and advisable, and this final report provides theimplementation plan to do so.
This report to the President and Congress represents theculmination of the Task Force’s efforts and provides a pathforwardandspecificationsformeeting
national
cyberinfrastructure needs and transforming the AI R&D
landscape for the benefit of all. It builds upon and extends the
Task Force’s interim report, submitted to the President andCongress in May 2022, 14 which set forth the Task Force’s visionand preliminary recommendations for key aspects of the
NAIRR, based on a variety of information-gathering and public
input, as indicated in Box 3. (See Appendix B for more details
on how the Task Force conducted its work. Appendix F lists
subject matter experts who briefed the Task Force, while
Appendix G provides information on the public listening
session.)To succeed, the NAIRR must be designed to leverage andcomplement the existing cyberinfrastructure fabric for R&D
across the Nation—and augment or supplement it accordingly.
The current fabric spans high-performance and leadershipcomputing facilities, distributed computing frameworks,
commercial cloud resources, and the networks that bring themto users; data; software and tools; testbeds; and educational tools
and programs (see Figure 1). A successful NAIRR must also
foster the participation of individuals and groups across sectors and domains in AI R&D, andprovide opportunities to include the expertise and experience of all stakeholders.

5Figure 1. The Current Fabric of U.S. Research Cyberinfrastructure

Structure of This Report
The following chapters set out a roadmap for the NAIRR, including key implementation
steps, attributes, and specifications necessary for the NAIRR to fulfill its purpose. Chapter 2
describes the vision and goals for the NAIRR and identifies responsible entities and a general
timeline for its establishment. Chapter 3 describes key attributes for NAIRR governance, technicalresource components, security and user access controls, and user training and education tools andstrategies. Specific actors and actions are identified to the extent possible at this time, recognizingthat many decisions will wait until implementation or be revisited then. Chapter 4 provides moredetailed specifications for NAIRR resource components, including NAIRR initial operational
capabilities (i.e., the set of initial resources and functions that must be in place to launch NAIRR
operations). Chapter 5 describes a phased buildout plan for establishing NAIRR governance,
management, resources, and operations. Chapter 5 also provides a budget estimate forestablishment and sustainment of the NAIRR, a list of actions for each buildout phase, andimmediate next steps for U.S. Executive Branch agencies and Congress.

62. A National Cyberinfrastructure to Democratize
and Accelerate AI R&D
NAIRR Vision and Goals
The NAIRR is envisioned as a widely-accessible, national cyberinfrastructure that will
advance and accelerate the U.S. AI R&D environment and fuel AI discovery and innovation in the
United States by empowering a diverse set of users across a range of fields through access tocomputational, data, and training resources. Created by leveraging, linking, and augmenting the
Nation's existing cyberinfrastructure resources, the NAIRR would support cutting-edge
explorations in AI R&D and improve the ease of collaboration across disciplines and sectors that
address pressing problems with AI. It would create opportunities to train the future AI workforce,
support and advance trustworthy and responsible AI, and catalyze development of ideas that canbe practically deployed for societal and economic benefits.
The NAIRR would accelerate these outcomes by enabling U.S.-based researchers to accessthe digital resources that enable AI R&D: computational power, datasets, software tools, andtraining and collaboration resources. These would be made available through an integrated user
portal with key user functionalities such as single sign-on access to resources, collaboration tools,
search tools for resource discovery, detailed resource specifications and user guides, an interface
for computational job submission, and consolidated accounting of resource use. Researchers would
be able to request computational allocations across a range of high-performance computing (HPC),
commercial cloud, and other remote, on-premises or distributed computing resources. User support
services and interactive training modules would support users new to the field, which, along withclearly-defined policies and standards of practice, would promulgate best practices for trustworthy
AI model development and responsible data use by design. A publicly-accessible NAIRR user
portal would provide curated catalogs that list commonly-used AI datasets, testbeds, educational
resources, and relevant metadata, serving as a clearinghouse for the AI R&D community. Througha tiered-access model, vetted researchers would be able to conduct research on sensitive or
restricted data in secure enclaves.
The Task Force believes that the objective for establishing the NAIRR should be to strengthen
and democratize the U.S. AI innovation ecosystem in a way that protects privacy, civil rights, andcivil liberties. The NAIRR objective will be achieved by pursuit of four measurable goals: (1) spur
innovation, (2) increase diversity of talent, (3) improve capacity, and (4) advance trustworthy AI,
as illustrated in Figure 2.

7

Figure 2. NAIRR Strategic Objective and Goals

The NAIRR User BaseThe NAIRR should support the needs of researchers and students from diverse backgrounds
who are pursuing foundational, use-inspired, and translational AI research. The users of the
NAIRR are envisioned to fall into three primary categories:(1) Researchers conducting AI research: those who advance the state of the art in AI or
understanding of its sociotechnical dimensions, or those who develop innovative
applications of AI to solve problems in another domain of study (while also furthering AI
itself), including science, engineering, medicine, business, education, and the humanities.
(2) Educators incorporating AI tools and training resources into learning
environments: for example, through classroom demonstrations, homework assignments,
and interactive experiences.
(3) Students learning about AI: those studying at community colleges, four-year colleges
and universities, or graduate schools who are learning and experimenting with thedevelopment of AI models, tools, and applications as well as exploring the societal andeconomic implications of AI innovations; and those pursuing re-skilling programs in AI.
The primary user groups of the NAIRR should be U.S.-based and affiliated with U.S.
academic institutions; non-profit organizations; Federal agencies or federally funded research anddevelopment centers (FFRDCs); State, local, or Tribal agencies; and startups or small businesses
that have been awarded Federal grants via the Small Business Innovation Research (SBIR) or
Small Business Technology Transfer (STTR) programs, or other similar Federal programs, forsmall businesses to advance foundational, use-inspired, or translational AI R&D.

8

To ensure that the AI research ecosystem is diverse, the NAIRR should aim to transform its
users’ capabilities and outcomes. For example, the NAIRR should reduce barriers to participationin AI R&D and education, and make it easier and less costly for researchers—especially those whohave not historically been engaged and have been underrepresented in AI R&D—to access key AI
research tools. To ensure that there is ample workforce capacity, educators should have new,readily available options for incorporating AI tools and training materials that support student
learning in AI, including the ethics of AI. Students should gain new and early exposure to AI tools
and methodologies that transform their understanding; increase their interest in AI and otherscience, technology, engineering, and mathematics (STEM) fields; and broaden engagement
across the full pool of talent to help build a strong and diverse future AI innovation ecosystem. A
vision for how users will access and benefit from the NAIRR is illustrated in Figure 3. To
maximize the impact of the NAIRR, complementary agency programs could also be initiated, withassociated Federal appropriations, to support the entry of new researchers into AI R&D who may
then leverage the NAIRR, as a parallel means of growing, diversifying, and democratizing the
R&D community.

Figure 3. A Vision for NAIRR Users and Resource Elements

9NAIRR ConstituentsThe success of the NAIRR will hinge on the leadership, participation, and engagement of adiverse mix of organizations, groups, and researchers across a range of sectors and disciplines.
Government, academia, industry, and civil society groups will all have critical roles to play inrealizing the vision of the NAIRR.
Government
The U.S. Government should have the primary role in establishing the NAIRR. The Federal
Government should be its principal sponsor, funding NAIRR to help meet its goals in the national
interest and the government-wide National AI Initiative, which involves activities across Federal
agencies and is coordinated by the National AI Initiative Office (NAIIO) within the White House
Office of Science and Technology Policy (OSTP). Federal departments, agencies, and offices that
conduct or support AI R&D or provide research cyberinfrastructure should take active roles insupporting the establishment and governance of the NAIRR and funding its component resources.
The government has a strong foundation on which to build the NAIRR. Many Federal
agencies already support AI R&D and R&D cyberinfrastructure. As reported in the Networking
and Information Technology R&D Program and the NAIIO Supplement to the President’s fiscal
year (FY) 2023 Budget, 15 11 Federal departments plus the independent agencies National Science
Foundation (NSF) and the National Aeronautics and Space Administration (NASA) reported
investments in AI R&D. Four of these departments and agencies (the Department of Defense, theDepartment of Health and Human Services, the Department of Energy [DOE], and NSF) reported
funding more than $200 million dollars each in AI R&D in FY 2022.
Many Federal agencies are making important strides in using AI to advance their agency
missions—from improving education outcomes to transforming the detection and treatment ofdiseases (and much more). Their work could be accelerated by research facilitated through the
NAIRR. As a national resource, the NAIRR could be leveraged by agency researchers andsupported by agencies through the multi-agency governance structure described in Chapter 3.
Federal agencies (including via their FFRDCs) can also contribute research resources to the
NAIRR, such as large datasets, computing resources, software tools, and AI testbeds. State,
territorial, local, and Tribal governments may also contribute datasets suitable for research, andcould benefit from the results and applications of research performed through the NAIRR.
Academia
The NAIRR should provide researchers, educators, and students at universities and colleges
across the United States with access to the computational and data resources that fuel cutting-edge
AI research, along with training materials and user support. The NAIRR offers particular value toinstitutions whose researchers have not historically received significant Federal AI researchfunding or cyberinfrastructure support, or whose lack of resources has inhibited participation in
the AI R&D enterprise. The NAIRR thus offers opportunities to broaden participation in AI
research, complementing provisions in the CHIPS and Science Act of 2022 aimed at strengthening
research capacity and expanding STEM education opportunities in emerging technologies at10historically Black colleges and universities and minority-serving institutions such as Tribal
colleges or universities and Hispanic-serving institutions. Its accessible education resource
catalogs and training tools will offer value for learners from diverse backgrounds, organizational
affiliations, and geographic locations.
It will be critical to ensure that universities and their researchers have an important role inestablishing and managing the NAIRR for several reasons. First, academic researchers andresearch groups will be vital users of and contributors to the collaborative resources such as
datasets and research tools available through the NAIRR. Second, academic researchers engaged
in cutting-edge research will be key to providing strategic advice and oversight for the NAIRR’s
investments in computational and data resources. Finally, universities are the front line indesigning the curricula and training materials that are necessary to expand the capacity of a diverse
AI workforce.
Industry
Companies should benefit from the flow of a diverse group of graduates whose training is
supported by NAIRR resources and from the innovations resulting from NAIRR-supported
research. Startups and small businesses should have the opportunity to use NAIRR resources fortheir own R&D.
For-profit and not-for-profit organizations have products and services that could be madeavailable through the NAIRR, and thus should also have the opportunity to provide resources forinclusion in the federated cyberinfrastructure—potentially through commercial cloud computing
contracts or through the incentivized contribution of software tools or datasets. As the NAIRR
evolves there should be opportunities for companies to provide funding or other contributions
towards the NAIRR’s operations through partnership agreements. Industry experts may also
participate as technical advisers on NAIRR advisory boards.
Civil Society
The NAIRR should be a platform on which researchers can study and examine societal
implications of AI and to develop and test solutions that would maximize the benefits of AI. A
variety of scientific and advocacy groups—scientific societies and associations; groups concernedwith data privacy, civil rights, and civil liberties implications of AI; philanthropic organizations;and academic researchers—should have the opportunity to leverage the NAIRR for research andevaluation that promote the responsible development and use of AI. Scientific and advocacy
groups could also participate in oversight of the NAIRR as members of advisory boards. They
should play an important role in ensuring that public interests, such as the development oftrustworthy AI, are properly represented and considered among NAIRR governance andmanagement entities.

113. NAIRR Organization, Management, and Governance
The impact of AI extends to nearly all sectors of the Nation’s economy and aspects of society.
Thus, it is critical that the governance of the NAIRR appropriately reflects not only the breadthand diversity of the users of the NAIRR, but also the broad suite of constituents likely to be
impacted by the AI innovations that result.
The organizational structure for NAIRR management and governance should incorporate theinterests and perspectives of the many Federal agencies involved in AI R&D, take advantage ofthe distributed nature of existing and future cyberinfrastructure, and facilitate input from thevarious constituents and communities involved in and affected by AI research. This chapter lays
out the recommended organizational structure and management elements of the NAIRR. It also
describes the key governance functions that will require policies and oversight, such as building
considerations of privacy, civil rights, and civil liberties into all facets of the NAIRR’s design andoperations as well as ensuring system security.

NAIRR Organizational Structure
Federal agencies currently invest in the infrastructure that enables federally funded research
via a range of different models, in alignment with their mission needs. While management of the
NAIRR could be handled entirely within a single government agency (which has the benefit ofclear ownership, authority, and responsibility), excluding other agencies would likely narrow its
focus to that agency’s specific mission, leaving the needs of researchers supported by otheragencies unmet, and translating to a loss of opportunity for the Nation.
Instead, the Task Force recommends that one agency serve as the “administrative home” forthe NAIRR to provide core funding for a third-party (non-government) Operating Entity thatcarries out the activities needed to coordinate, federate, and sustain the NAIRR. This funding
would provide for the operations of the Operating Entity, not the research resources that would bea part of the NAIRR. Other agencies should play a major role in NAIRR stewardship by (1)forming a multi-agency Steering Committee that provides strategic guidance and collective
oversight of the NAIRR, (2) funding resource providers that would be federated together toconstitute the NAIRR, and (3) providing staffing for the Program Management Office. It is
critically important that all agencies involved in the NAIRR work together through the Steering
Committee to coordinate the provisioning of resources and ensure that all agency perspectives arereflected. The Task Force majority recommendation for the NAIRR administrative home isdescribed in Box 4.

12

The Task Force makes its recommendations after careful review of the successful cooperative
stewardship model for multidisciplinary users of the Nation’s synchrotron, neutron, and highmagnetic-field user facilities reviewed by the National Research Council. 16 In this model, theresponsibility for design, construction, operation, maintenance, and upgrading of a research facility
core rests with a single clearly identified Federal agency—the steward. The steward then engages
partners—other agencies, industry, and private institutions—in the planning, design, construction,
support, and funding of the experimental stations and other sub facilities. While no model is
without flaws, the Task Force believes this model will best serve the AI R&D priorities acrossFederal agencies and achieve the societal-level impacts envisioned for the NAIRR.
Leveraging this model, the agency
serving as the administrative home for the
NAIRR would fund and oversee the core
operations, but would not establish thestrategic direction of the NAIRR, norfund all the individual resource providers.
As described below, a Steering
Committee, with representation fromagencies participating in the NAIRR,
should set the strategic direction of the
NAIRR and drive decisions about which
resources will be brought into the NAIRR
from which providers.

Box 4. Designating the NAIRR
Administrative HomeThe Task Force, by majority opinion, recommends
the designation of NSF as the administrative home
for the NAIRR. The Task Force defined the key
attributes envisioned for an effective administrative
home to include the following:◦ Mission alignment.
◦ Capacity and capabilities to effectively support
administrative activities.
◦ Existing relationship with the AI researchcommunity and other NAIRR constituents.
◦ Experience supporting foundational, useinspired, and translational AI research.
◦ Existing relationship to building workforce
capacity at multiple levels.
◦ Focus on equity and diversity and the ability tosupport democratization of resource access.
The Task Force assessed that NSF meets these
attributes and could effectively oversee theoperations of the NAIRR within the collaborative
NSF's
interagency
framework
proposed.
relationship with America's research community inthe field of computer science and across all domainsof science and engineering, as well as its
experience in funding broadly-used national
cyberinfrastructure resources, services, andexpertise, provides it with the existing relationships,
trust, and expertise necessary for a rapid andeffective stand up of the NAIRR.

Given the complexity of the NAIRR,
the Task Force recommends that its dayto-day operations be managed by a single,
non-governmental Operating Entity. The
Operating Entity will require a dedicated,
expert, stable workforce composed ofhighly trained technical talent capable ofmanaging long-term, complex needs andsystems with a high degree of objectivity.
The Operating Entity must leverageexternal input-gathering mechanisms.
Given the NAIRR’s many operational
requirements, expert advice is needed on
issues spanning technical resource design,development, management, interoperability, standards, and improvement; user experience design,development, and improvement; ethical design, development, and use of research resources; legal
and regulatory compliance, intellectual property management and agreements; and education andtraining. Experts from a wide range of scientific and academic disciplines, including social scienceand ethics, and also drawn from government, industry, and non-profit sectors, must therefore be
actively engaged, for example, by including them among members of the advisory boards and a13

User Committee. These advisory bodies are intended to bring diverse perspectives, providing
strategic management advice to inform the NAIRR’s operations.
The recommended organizational structure for the NAIRR (see Figure 4) shows how
different elements of the NAIRR management and governance structure should relate and interact.
A detailed description of each of these elements is provided in the sections that follow.

Figure 4. Proposed NAIRR Governance Structure

Steering Committee
Many Federal agencies individually and collectively have stakes in the NAIRR’s success,and are therefore envisioned to contribute to its governance. NAIRR governance should follow the
proposed cooperative stewardship model and serve the interests of all agencies involved. A
Steering Committee comprising principals (e.g., deputy or assistant secretaries) at departments,
agencies, and offices with significant AI R&D investments or equities in the NAIRR should beconstituted to provide strategic direction. This Steering Committee should be chaired by the
Director of the NAIIO, in accordance with the office’s role as coordinator of Federal activities insupport of the National AI Initiative, and should have rotating co-chairs. The involvement of
deputy or assistant secretaries ensures top-level commitment to agencies’ engagement in the
NAIRR and its governance. The Steering Committee may establish operational working
committees to manage more operational issues.
Agencies that have already made substantial investments in AI R&D and cyberinfrastructure
are likely most able to provide guidance about the NAIRR’s initial setups and structure, and14

therefore are most likely equipped to lead the initial phases of the NAIRR’s development. Since
all agencies stand to benefit, additional agencies should be brought into the Steering Committee
over time. The Steering Committee composition should be reviewed every three years by the
NAIIO. As part of these periodic reviews, additional agencies could commit funding or resources
to the NAIRR or become members of the Steering Committee, or participating agencies could electto discontinue participation.
The Steering Committee will establish the overall strategic direction for the NAIRR andshould be responsible for overseeing and approving the following:•

The operating plan, budget (see Chapter 5), and requests for proposals (RFP) to solicit
bids for the Operating Entity, including the terms and conditions and functions of the
Operating Entity.

•

The review of proposals for and selection of the awardee to serve as the Operating
Entity.

•The identification of resources that could be federated, selection of individual resource
providers, and determination of how resources could be allocated and made accessible
via the NAIRR.

•

Once the NAIRR has been initiated, the development of key performance indicators(KPIs) for the Operating Entity and NAIRR as a whole, in collaboration with other
NAIRR governance entities.

•

Work with an external, independent evaluator to conduct a periodic review of NAIRR
activities and performance against KPIs, and assess program needs and inform decision
making and planning.

The Steering Committee should initiate work on the above areas through the administrative
home and NAIRR Program Management Office, and may manage certain of the above tasks
through operational working committees. The Steering Committee should monitor the progress of
the NAIRR towards its objectives and provide recommendations annually in a publicly available
report to the NAIIO.
Individual Agencies
Federal agencies with AI R&D investments or equities should contribute NAIRR resource
elements by incorporating appropriate funding for NAIRR resources in their annual budget
requests. Funding for core operations of the NAIRR through the Operating Entity should be
provided by the agency serving as the administrative home; individual resource elements can befunded separately with provisions for federation as part of the NAIRR.
First, funding should be directly allocated by Congress to the agency serving as theadministrative home for the NAIRR to provide for the activities of the Operating Entity, including
project management, portal development and deployment, federation support, and concierge
services such as training and user support. The administrative home agency should staff a Program
Management Office, which is described in detail in the next section.

15

Second, funding should be directly allocated by Congress to individual agencies to fund theresources made available through the NAIRR, many of which may be aligned to specific agency
mission interests, which should be federated together to constitute the NAIRR. Resources can befunded individually or as part of multi-agency funding opportunities coordinated through theProgram Management Office. In addition to software and platform-as-a-service providers, the
NAIRR resource providers may represent one or both of the following:•

Expansions of existing computing capacity (e.g., on-premise computers at a university
center or at an FFDRC), dedicated computing time or storage purchased fromcommercial cloud computing providers, or purchases of new, specialized computational
facilities dedicated to AI research.

•

Trusted data providers and hosts for a transparent and responsible AI data commons.
Access to data should be tiered, controlled by the data providers, and provided throughthe same portal through which computational resources are provided.

Given the costs of these resources and their broad applicability to many types of AI R&D
and research using AI-enabled methods, in some cases it will be more efficient for multiple
agencies to collaborate in funding NAIRR resources rather than having each participating NAIRR
agency individually purchase and contribute computing and data storage resources to the NAIRR.
Additional context about the process for selecting and integrating resource providers into the
NAIRR is provided in Figure 5.

Figure 5. Process for Selection and Integration of NAIRR Resource Providers

Third, appropriations provided to Federal agencies for AI R&D programs should be sufficient
to support inclusion of NAIRR allocations to enable access to AI research resources as part ofFederal awards to investigators funded through agencies’ own intramural and extramural proposal

16and review processes. Including NAIRR resources as part of the awards will enable such federally
funded researchers to leverage the NAIRR’s full capabilities in meeting agencies’ R&D objectives.
Program Management Office
While the Steering Committee should provide strategic direction for the NAIRR and haveultimate accountability for its success, the scale and complexity of the NAIRR would requireongoing operational oversight and management by Federal Government employees through a
dedicated NAIRR Program Management Office. The Program Management Office should include
8 to 10 dedicated Federal agency staff members, including experts in cyberinfrastructure, data, AI
R&D, scientific integrity, ethics, and other areas necessary to execute the Steering Committee’svision; staffing could be expanded as needed. The Task Force recommends that the Program
Management Office staff include individuals who are on detail from participating agencies,
including for leadership positions in the office. In practice, the Program Management Officeshould serve as the operational arm of the Steering Committee and do the following:•

In consultation with the Steering Committee, develop the solicitation and solicit bids for
the Operating Entity, which includes the identification of key Operating Entity
personnel such as the Director and key staff.

•

Manage the review process and recommend an award by the administrative homeagency for the funding of the Operating Entity.

•

Identify an external independent evaluation entity whose independent assessment would
inform periodic review of the Operating Entity and the NAIRR by the Steering
Committee and Program Management Office.

•In collaboration with the Operating Entity, develop multi-agency funding opportunities
for resource providers.

•

In collaboration with the Operating Entity, manage the review of responses to multiagency resource provider funding opportunities.

•

Administer the Operating Entity contract (i.e., oversee operations/processes including
federation of resource providers, assess the Operating Entity’s performance on arecurring basis).

•

Oversee resource allocation and utilization.

Selection of the Operating Entity should be conducted in consultation with the Steering Committee
and through a standard solicitation process. Criteria to guide the selection process should be
developed by the Steering Committee, and should include but not be limited to experiencemanaging multi-agency initiatives; identification of key staff personnel; expertise in AI R&D; astrong diversity plan; and an ability to execute according to the NAIRR implementation timeline
presented in Chapter 5.

17

Operating Entity
The Operating Entity should be a distinct, non-government organization, governed by aformal charter and associated policies, with an executive leadership team managing day-to-day
operations. It may take the form of an independent legal entity or a consortium of one or morepartners (e.g., existing organizations such as research universities, industry, laboratories, andFFRDCs) that work jointly to initiate, manage, and sustain the NAIRR. The Operating Entity
should not itself operate the totality of the computer hardware that makes up the NAIRR; instead,computing, data, and training resources would be delivered by resource providers at universities,
FFRDCs, and from the private sector. The Operating Entity would manage the day-to-day
operations of the NAIRR. It would have five major responsibilities: (1) linking and coordinating
the provisioning of federated NAIRR resources; (2) developing NAIRR policies and procedures;(3) continually modernizing the NAIRR; (4) advancing diversity, equity, inclusion, andaccessibility (DEIA) in all aspects of the NAIRR, including operations; and (5) establishingmechanisms to enable evaluation, oversight, and the collection of data for assessing KPIs. These
responsibilities are described further below.
Coordinate the Provisioning of NAIRR Resources
The Operating Entity should work with the Program Management Office (with guidance fromthe Steering Committee) to develop one or more multi-agency funding opportunities for resource
providers. While agencies may opt to fund resource providers separately, a multi-agency funding
opportunity would optimize federation and coordination of individual resource providers. The
Steering Committee or their designees should review proposal submissions (in concert with theProgram Management Office and Operating Entity) and select awardees. From awards madethrough the multi-agency funding opportunity process, agencies would contract for resource
providers to provide services to the NAIRR, using contracts based on a set of common terms andconditions. In some cases, an agency might provide funding to the Operating Entity for directcontracting of services, such as from cloud providers. Subsequently, the Operating Entity will
provide continuous management oversight and service delivery evaluation of resource providers
in the context of their federation within the NAIRR, including creating the ground rules forinteroperability across resource providers. The Operating Entity will be responsible for working
with the providers to implement course corrections as needed. It will also receive and evaluate, ona yearly basis at a minimum, requests from the User Committee regarding what resources the
NAIRR should offer.
Develop and Communicate NAIRR Policies and Procedures
The Operating Entity must transparently communicate which individuals or groups are
eligible to use the resources, how resources will be allocated among interested users, and how theusers will be able to request and gain access to the resources. Thus, the Operating Entity, in
consultation with the NAIRR advisory boards and the Steering Committee, will need to establish
the corresponding policies and procedures. As part of this effort, the Operating Entity must
establish review processes grounded in principles of scientific integrity and ethics to allocate
resources fairly, equitably, and transparently for the full diversity of users and user types, including
those who have long been underrepresented in AI R&D. To support these efforts, the Operating
18

Entity will develop portals and services with information about how to access and use resources;
hire personnel to serve as the central support staff for NAIRR users and to produce documentation
on its use; and create open funding opportunities and associated review processes for project
proposals to use the NAIRR’s computing resources. When possible, the Operating Entity should
leverage existing approaches, such as review processes, employed by Federal research funding
agencies.
Continually Update the NAIRR with the Latest Technologies and Capabilities
The Operating Entity should manage a continual updating of the NAIRR infrastructure toinclude the latest computational, networking, and data collection, storage, and dissemination
technologies and capabilities through biennial multi-agency funding opportunities. In
collaboration with the User Committee and informed by metrics related to NAIRR resource usage
and KPIs for the NAIRR more generally, the Operating Entity should regularly identify new areas
for innovation and investment and their requirements from a NAIRR perspective, and work with
the Steering Committee to scope the biennial funding opportunities accordingly. This ongoingrefresh of resources is critical for the NAIRR to be able to power AI R&D at the cutting edge. The
Operating Entity should have primary operational responsibility for vetting resources that become
part of the NAIRR, including recommending to the Steering Committee when to onboard andsunset individual resources, and authority to set the standards for the security configurations ofthese resources. As an independent organization, the Operating Entity will have flexibility incontracting, partnering, or entering into other agreements with individual resource providers, withoversight provided by the Program Management Office and the Steering Committee. NAIRR
operational responsibilities will be distributed among the Operating Entity, federated resource
providers, and possibly contractors via partnerships or other agreement types, depending on the
Operating Entity’s needs.
The Operating Entity should provide annual reports, including the contributions of resource
providers, to the Program Management Office and the Steering Committee, and make thesepublicly available. To be fully transparent and accountable about how and why individual
resources or resource providers are selected or no longer supported, reports will include a set ofrecommendations to the Steering Committee regarding how to augment, reallocate, or reduce the
NAIRR’s offerings.
It is likely that needs will emerge that must be addressed in a timely manner. Anothermechanism for identifying emerging needs related to the NAIRR’s infrastructure will be for the
Operating Entity to conduct a range of activities (with guidance from the User Committee) tosolicit input from scientific and user communities and agencies, such as through investigatorinitiated workshops to scope emerging areas of science and technology. In addition, the Operating
Entity should maintain awareness of computational, data, training, and other infrastructureadvances, and strive to make these cutting-edge developments available to the community either
through contracts with resource providers executed through the multi-agency funding
opportunities or through internal discretionary development funds (e.g., on an initial pilot basis).

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Advance Diversity, Equity, Inclusion, and Accessibility
The Operating Entity must be explicitly responsible for incorporating DEIA into all aspectsof the NAIRR, including the AI R&D that the NAIRR enables. A DEIA focus should be built into
the overall organization, operational plan, and federated system of resources from the beginning,rather than as an afterthought. Extending access to AI research resources as broadly as possible,
and incorporating a diverse set of viewpoints into the prioritization of investments, the review ofresources and resource providers, and the evolution of the AI research ecosystem, are core to the
NAIRR’s diversity and capacity goals. NAIRR user access policies therefore must be grounded in
the principles of equity, fairness, and accessibility. Assessment of progress and input on
engagement with and support of a broad and diverse AI community will be a key aspect of NAIRR
governance and oversight activities.
Establish Data Collection, Evaluation, Governance, and Operational Oversight
Mechanisms
The Operating Entity should establish mechanisms for monitoring system and organizational
performance, including by designing appropriate metrics-collection mechanisms into the systemarchitecture. It will need to engage with an independent, external evaluator to support the review,and create a process for updating organizational and operational procedures as issues are identified.
As part of its key role in NAIRR governance, the Operating Entity will also need to define ethics
and scientific integrity policies, as well as mechanisms for reporting, adjudicating, and remediating
any violations, with guidance from its advisory boards and the Program Management Office.
NAIRR Staff and Executive Leadership TeamThe Operating Entity should have an executive leadership team—including a Director, Chief
Executive Officer, and Chief Operating Officer—that is responsible and accountable for day-today operational decision-making for NAIRR operations; interfacing with advisory groups andgovernment oversight entities; managing outreach, communications, and partner engagement; andscouting and strategizing for new and emerging AI R&D needs. 17 Importantly, the Operating
Entity Director or executive leadership team should be allocated 5–10 percent of total resources
for discretionary allocations; these allocations could be leveraged during emergency situations,allowing the NAIRR to be agile in responding to urgent or atypical needs—for example, as wasdone with research efforts established in response to the emergence of the COVID-19 pandemic.
To support its responsibilities and functions, the Operating Entity must be able to hire andretain high-quality and experienced staff. For example, ensuring that the NAIRR is resourced with
cutting-edge technologies and capabilities requires that the Operating Entity comprise staff
members who are expert in advanced research cyberinfrastructure. Similarly, promoting equitable
access to resources requires that the Operating Entity’s leadership understands barriers to access.
The Operating Entity will need to explore a range of mechanisms for making the work of the
Operating Entity attractive to an expert, dedicated staff. In addition, for the NAIRR to successfully
promote diversity, equity, and inclusion in AI, it must embody these principles by ensuring
diversity among its own staff and leadership and enlisting experts with a range of backgrounds andexperiences.

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NAIRR Advisory Boards
Since the NAIRR will serve many communities and have so many operational requirements,
the Operating Entity will need advice on a variety of operational issues, including (1) technicalresource design, development, management, interoperability, standards, and improvement toensure that the NAIRR remains at the cutting edge of innovation; (2) user experience design,development, and improvement to ensure broadly available and equitable access and use of
research resources; (3) ethical design and development of access protocols and mechanisms; (4)legal and regulatory compliance; (5) intellectual property management and agreements to ensurethat the NAIRR is—and is seen as—trustworthy; and (6) education and training to meet theworkforce capacity needs of the AI ecosystem.
To ensure that the NAIRR meets its objective and goals, the Operating Entity should establish
several independent boards, focused on different aspects of the NAIRR's mission (e.g., science andtechnology, data policies, ethics, privacy, civil rights, and civil liberties). These oversight boards
and advisory boards should be tasked with providing guidance in specific areas and input on
metrics to be used for evaluation.
To this end, the NAIRR should establish at least four advisory boards:•

A Science Advisory Board to provide advice about the rapidly changing needs acrossmultiple scientific domains so that the NAIRR can rapidly adapt to support innovation.
The Science Advisory Board should include individuals with management experiencedrawn from the scientific community, the public at large, public interest groups, theprivate sector, and other large-scale cyberinfrastructure projects.

•

A Technology Advisory Board to advise the Operating Entity about cutting-edge
technological solutions in the provisioning and use of computational and data
infrastructures, workforce training, and on privacy- and security-related technologies.
The Technology Advisory Board should include recognized experts from across thecomputing, data, and security communities and should be selected to represent industry
and government, with some academic involvement.

•

An Ethics Advisory Board to advise the Operating Entity on issues of ethics, fairness,bias, accessibility, and AI risks and blind spots. The Ethics Advisory Board’s intendedroles are to (1) evaluate the ethical use of AI, computational, and data resources by
NAIRR awardees as well as issues related to scientific integrity, and help the Operating
Entity ensure that privacy, civil rights, and civil liberties are not violated; (2) evaluate
and advise on the fairness and appropriateness of data and training delivered by the
NAIRR; (3) provide guidance on approaches to understanding issues of ethics, bias, andfairness and on NAIRR ethics policies and practices; and (4) handle concerns and/or
complaints brought to the Operating Entity’s attention or by the User Committee. The
Ethics Advisory Board should provide periodic insight and feedback on a broad rangeof policy issues, guidelines, and practices, including in areas such as privacy, civilrights, and civil liberties. The Ethics Advisory Board should be selected to include

21

experts in privacy, civil rights, civil liberties, and ethics as well as to represent usergroups, scientific societies, advocacy and civil society groups, and government.
•

A User Committee to provide the user perspective for the NAIRR, providing feedback
on operational and governance issues, offering perspectives on user needs andrequirements, and identifying new directions for the NAIRR to create value and serve
the community. It should be composed of subject matter experts from across multiple
scientific and user communities and be selected to represent AI researchers, with someindustry and ex-officio government representation.

The activities of these advisory boards should be supported by staff at the Operating Entity.
As a guiding principle, each board should consist of 6–8 members to be selected by the Operating
Entity after an open call and with input from the Program Management Office and Steering
Committee. Special attention should be paid to diversity, inclusivity, and representation/affiliationof board membership. The exact number of, and nominees for, these advisory boards should be
reviewed on a regular basis by the Operating Entity in consultation with the Program Management
Office as the number of domains supported by and types of services provided by the NAIRR
evolve. Members should represent government, academia, and industry sectors, with the relative
weights appropriate for each board. Care should be taken to address potential conflicts of interest.
The term of membership for individuals should be three years, with staggered expirations (e.g.,
one-third rolling off each year). The members of each board will select a chair from among theirranks, who can serve an additional two years in this capacity. Advisory boards report to the
Operating Entity executive management and are responsible for delivering written guidance
annually. Board reports will be shared with the Program Management Office and the Steering
Committee by the Operating Entity. Each board should meet a minimum of twice a year.
Evaluation Entity
Evaluation of NAIRR performance—toward both its high-level goals and its operational
KPIs—should be conducted by an independent, external evaluator with experience in assessing
major R&D infrastructure programs. This entity should be contracted by the Program Management
Office with the input from the Steering Committee, and its evaluation approach developed inparallel with Operating Entity activities so that appropriate metrics can be developed and theassociated data collection may be incorporated into the NAIRR’s design.

User Access and Resource Allocation
Since the fundamental objective of the NAIRR is to democratize access to AI resources, the
NAIRR must primarily be sustained through Federal investment, with direct user fees employed
only to scale beyond a base level of resources. As described in Chapter 2, the primary users of the
NAIRR would be U.S.-based AI researchers and students at U.S. academic institutions, non-profit
organizations, Federal agencies or FFRDCs, or startups and small businesses awarded SBIR or
STTR funding. Others (e.g., private sector researchers other than small businesses) would beallowed to access NAIRR resources, but only at limited levels and in support of research that is in
the public interest. Supporting the academic research community should be prioritized through theresource allocation process, with particular attention to underserved communities.
22

Access to the NAIRR should be granted directly to researchers by Federal funding agencies
or the NAIRR Operating Entity. Awards may be flexibly structured to include in-kind credits or
tokens redeemable for computer time, data access, or other services.
With oversight and approval from the Steering Committee and Program Management Office,the Operating Entity should establish multiple allocation processes based on the nature, size, andscope of the requests, which are divided along two primary tracks: one driven by participating
agencies and a second peer-review track run by the Operating Entity. Within the agency-driven
track, agencies should be given latitude in how to make awards, within the constraints of their
allocated credits and in close coordination with the Operating Entity. Credits could be awarded
directly through agency research grant funding programs or could be made to awardees through aseparate process managed by the agency in close coordination with the Operating Entity. Because
not every participating agency may have the expertise or resources to run such a process, theagency could choose to leverage the peer-review track managed by the Operating Entity. The
Operating Entity should be responsible for keeping the agencies within their allocation caps, which
would be determined based on a combination of factors such as an agency’s support of AI R&D,
contributions of resources to NAIRR, or number of allocation requests received, while enabling
the agencies to decide who receives the allocations.
The peer-review track should be managed by the Operating Entity and subdivided by size
and type as follows:•

Startup requests: These requests should be capped at a modest size (e.g., suitable for aclassroom of students for a single semester, or approximately $1,000 worth ofcomputational time/storage). Requests should be reviewed by staff at the Operating
Entity, with turnaround times to the applicant of less than two weeks. Startup
allocations would typically expire in one year and then could be renewed.

•

Research requests: Larger requests in support of significant AI research projects
should be peer reviewed through the Operating Entity. The Operating Entity shouldorganize review panels quarterly, and should place caps on the size and duration ofrequests based on the capacity of resources within the NAIRR.

•

Purchases: Users could opt to purchase additional allocations if they need services that
extend beyond the amount they can acquire through the open startup and research tiers,
or could be made by entities that would not otherwise qualify for access (see below).

In both the agency-driven and peer-review tracks, allocations should be provided in credits
with base rates derived from the cost of computational time or data storage. Some services, suchas downloading data or models from a repository, would not require any credits.
The tracks should be structured with different criteria and processes for selection. Within thepeer-review track, the basic principle would be that, as the size of the request grows larger, the bar
for review increases. At the startup request level, the application would be a simple form thatvalidates enrollment and eligibility, along with a description of the project. At the research request
level, the application should be more extensive, including a proposal describing the work,
underlying funding support, estimates of the computational resources needed, and so forth.

23

Possible outcomes include full acceptance, full rejection, cuts in the amount awarded, or redirecting the investigator to different resources within the NAIRR. For government-owned
or -controlled resources made accessible to researchers through the NAIRR, the NAIRR resource
allocation process should not bypass existing access approval processes but rather route NAIRR
researchers into these existing processes.
If sufficient NAIRR resources are available, the Operating Entity may develop a directcharge model for a subset of available resources. This “purchase” option can be useful both for
granting access to users who would not otherwise be eligible for NAIRR access, as well as
allowing those users who receive NAIRR access to grow their allocation beyond what can be freely
provided. Revenues from cost recovery can be used to further expand the capacity of the NAIRR,
providing access for additional users without sacrificing the availability of resources for the typicaluser base. A thoughtful and publicly-disseminated approach to establishing cost models can ensurethat the NAIRR’s public funding stays consistent with the original goal of democratizing access.
The Operating Entity should establish an allocation system to award credits in alignment withavailable resources. Because AI workloads are extremely difficult to estimate in advance, NAIRR
policies should permit the augmentation of resources through justified supplements, advances, ortransfers from other accounts. The Operating Entity, with guidance from the Steering Committee
and Program Management Office, should regularly review and adjust the division of resources
across the agency-driven and peer-review tracks.

Privacy, Civil Rights, and Civil Liberties Protections
The NAIRR should serve as an exemplar for how transparent and responsible AI R&D canbe performed with proper training and oversight at multiple levels. Processes to ensure that NAIRR
operations, research, and governance are conducted in a transparent fashion with appropriate
oversight should be integrated across all aspects of the design, implementation, administration,
management, and use of the NAIRR. The NAIRR Operating Entity, with input from the advisoryboards, must be proactive in addressing privacy, civil rights, and civil liberties issues. It must
integrate appropriate technical controls, policies, and governance mechanisms from the beginning.
One important initial step will be to include a diverse set of experts from relevant disciplines aspart of NAIRR leadership and governance. The Steering Committee, Program Management
Office, and Operating Entity must work together to ensure diversity among NAIRR decisionmakers, and draw from the expectations for automated systems described in the Blueprint for an
AI Bill of Rights 18 as well as best practices defined in the AI Risk Management Framework (seeBox 5). The Operating Entity leadership should hire staff with expertise in protecting privacy andmitigating ethical and societal issues, who would work with the advisory boards to design privacy,
civil rights, and civil liberties considerations into the Operating Entity’s governance and review
structures and activities.

24Consideration for ethical issues should be foundational to the NAIRR and permeate its
decision-making processes. One specific area for attention is the data to be incorporated into the
NAIRR. The Operating Entity should develop publicly reviewable controls for datasets that the
NAIRR hosts and a mechanism to
Box 5. Guiding Principles for NAIRR Policies
ensure that datasets with legal,
Multiple efforts are underway nationally andethical, or discriminatory issues areinternationally to articulate responsible AI principles andquarantined
andappropriately
operational strategies. The Blueprint for an AI Bill ofhandled, drawing from the principles
Rights was released by the White House in October 2022,
and expectations detailed in theand includes a set of five principles and associatedpractices to help guide the design, use, and deployment
Blueprint for an AI Bill of Rights.
of automated systems to protect the rights of theThis should include support for
American public in the age of artificial intelligence.18
system auditing and for maintenance
These five core protections are: safe and effective
systems; algorithmic discrimination protections; dataof an archive of retired datasets to
privacy, notice, and explanation; and human alternatives,
provide researchers with the ability
consideration, and fallback. The Operating Entity should
to study data with different types ofconsider this framework when developing its policies andprocedures.
biases to better understand common
data issues and potential harms, as
NIST is developing an AI Risk Management Framework,
which is anticipated to be released in early 2023. The
well as the robustness of AI models
framework is being developed through a consensuswhen applied to such datasets.
driven, open, transparent, and collaborative process, andcompliance will be voluntary. 19 Overall, the framework is
intended to give AI developers the ability to incorporate
trustworthiness considerations into the design,development, use, and evaluation of AI products,
services, and systems. The Operating Entity should
consider this framework when developing its policies andprocedures.

The Operating Entity should
establish, implement, and publicize
acceptancecriteria
andrecommended best practices for all
resources joining the NAIRR toensure that they are vetted fromprivacy, civil rights, civil liberties,
and equity perspectives. These acceptance criteria should be more stringent for resources that arelikely to be used in contexts that raise heightened concerns about privacy, civil rights, and civil
liberties. It will be critical for the NAIRR to act quickly to provide such information, because much
harm can result from delaying decision-making.
The impacts of any controls instituted should be evaluated and adjustments made as needed.
The Ethics Advisory Board, in consultation with the User Committee, should play a central role indesigning and implementing privacy, civil rights, and civil liberties requirements across all NAIRR
systems, policies, and practices, and in ensuring dissemination of those requirements across theecosystem. The uptake and use of the requirements should be incorporated into the NAIRR KPIs.
The Operating Entity should work with the Ethics Advisory Board to develop criteria andmechanisms for evaluating research and resource proposals from a privacy, civil rights, and civil
liberties perspective; submit these criteria and mechanisms to the Program Management Office forreview by the Steering Committee; and publish the criteria on the NAIRR website.

25

Finally, ensuring awareness about rights, responsibilities, and best practices related to
privacy, civil rights, and civil liberties is essential. All NAIRR users will be required to complete
training, renewed annually, before being granted access to the NAIRR.

Scientific Integrity
The Operating Entity should also be responsible for addressing scientific integrity concerns.
The Operating Entity should work with the User Committee to develop criteria and establish
mechanisms for addressing researchers’ and AI users’ concerns associated with NAIRR-enabledresearch, submit them to the Program Management Office for review by the Steering Committee,
and publish the criteria on the NAIRR website. These criteria and mechanisms should be informedby the Presidential Memorandum on Restoring Trust in Government Through Scientific Integrity
and Evidence-Based Policymaking 20 and the guidance put forward in the 2023 Framework forFederal Scientific Integrity Policy and Practice from the National Science and Technology
Council’s Scientific Integrity Framework Interagency Working Group. 21 There should bemechanisms that allow early, easy, safe, and confidential reporting of perceived concerns. The
Operating Entity staff should work closely with the Ethics Advisory Board to ensure that bestpractices are followed and that concerns are quickly addressed. KPIs should be established toensure that this goal is satisfactorily met.
The Operating Entity should provide public information about research performed using
NAIRR resources through regularly updated and publicly available project registries containing
information such as (1) project names, descriptions, and anticipated value to the public; (2) project
teams and affiliations; (3) data used; (4) research questions and methods; and (5) anticipateddeliverables and associated delivery dates. The processes and policies established by the Operating
Entity should reinforce the expectation that data, code, and publications resulting from federally
funded research should be made publicly accessible to the extent possible. Users would be
expected to comply with Federal agency public access policies updated in response to thememorandum issued by OSTP on August 25, 2022. 22

System Security and User Access Controls
The cybersecurity threat landscape is rapidly changing and evolving as new actors, attack
methods, and vulnerabilities emerge. AI research, as an asset to economic growth and national
security, is a high-value target. Cybersecurity risks extend beyond technical considerations tohuman behavior. Creating a culture of usable security and training is key to mitigating human
mistakes that can lead to compromise. Just as convenience could conflict with security, fostering
an open research environment has tradeoffs with providing secure access to high-value information
and resources and protecting intellectual property.
The Operating Entity should implement system safeguards using government-applicable
NIST security guidelines as well as the Five Safes framework: safe projects, safe people, safe
settings, safe data, and safe outputs. The Five Safes framework structures protection across fivedimensions: research projects and individuals working on projects are reviewed and approved;
people using the resource must sign security agreements and complete training, and users’ accessis monitored; settings operationalize security needs and are managed through a central platform;
26

data is appropriately safeguarded against security, re-identification, and privacy risks; and exports
are technically and contractually controlled, and evaluated and monitored to prevent unauthorized
disclosure.
The Operating Entity should design the NAIRR to consist of multiple tiers, starting with atleast two primary zones: an open science zone, NAIRR-Open, and a secure zone, NAIRR-Secure.
Each zone will federate computational, network, and data resources operating in accordance withsecurity and access-control policies that are uniform within the zone, but different between zones,
reflecting the different priorities and needs of the users and resource operators. For example, easeof access and use may be of greater importance in the open science zone and appropriate forclassroom settings, while data security may be of greater importance in the secure data zone andappropriate for sharing and analyzing Federal agency protected data.
The NAIRR-Open zone should adopt the best practices developed over two decades in the
open science community, drawing from experiences and approaches used by ACCESS, the OpenScience Grid, and the National Research Platform. 23 Access to open science resources should bemanaged using single sign-on authentication and a resource allocation mechanism managed by the
Operating Entity.
The NAIRR-Secure zone should consist of one or more secure enclaves adhering to acommon set of security controls, 24 and have the ability to support security requirements forsensitive information, such as those necessary to protect Controlled Unclassified Information andthose arising from the Health Insurance Portability and Accessibility Act and other laws andregulations. 25 User-based access will be an important element in the NAIRR-Secure zone. The
NAIRR-Secure zone should be administered by a specialized resource provider, subject to all of
the oversight and reporting responsibilities of any NAIRR resource provider, but with theadditional responsibility of security monitoring and controls compliance for its set of managed
projects. To the extent that the data owners (e.g., Federal agencies, other non-governmental
resources) require an Authorization to Operate, then it will be the responsibility of the NAIRRSecure resource provider to obtain it.
Because the datasets to which the NAIRR provides access could include sensitive data on
human beings or confidential government data, and because the security landscape is constantly
changing, the Operating Entity will require staff with expertise in security, privacy, and usability,
and will need to establish security controls and mechanisms that can keep up with the rapid paceof change and ensure the security and confidentiality of such data in accordance with Federal
regulations. The value of access to sensitive data is also constantly changing, as evidenced by the
recent experience with the COVID-19 pandemic; as a result, the Operating Entity will also require
staff with expertise in measuring the value and use of data access, in accordance with therequirements of Title II of the Evidence Act. The Operating Entity must also comply with all
Federal regulations for protected data, and adopt both value- and risk-based approaches forprotecting sensitive data not otherwise covered by Federal regulations.

27

Open-Source Principles
The NAIRR Operating Entity and resource providers should adopt the principle of open
source for products developed with Federal funds. Exceptions to open-source requirements should
be provided for small businesses supported through SBIR or STTR programs that are given accessto the NAIRR, and in cases where data are protected. The Operating Entity should leverage
existing programs at Federal agencies that support translational activities such as having aprofessional software developer package software and tools developed as part of research projects
for longer-term open-source availability. The NSF Cyberinfrastructure for Sustained Scientific
Innovation (CSSI) 26 and Pathways to Enable Open-Source Ecosystems (POSE) 27 programs are
two relevant examples of existing programs that focus on open-source development and support
such translational activities.
More generally, research products should be made freely available through the NAIRR solong as they are reasonably mature and documented (i.e., production-level resources).

Environmental Sustainability
A system to source hardware in an environmentally sustainable way and measure and manage
discarded hardware and other electronic waste (i.e., electronic devices that have reached their endof life) should be established for all resources made available through the NAIRR. Key elementsof electronic waste management include maximizing the life cycle and usability of systems, aswell as plans for electronic waste recycling, systems and equipment repurposing, and hardware
reselling. Recycling electronic waste presents an opportunity for the recovery of critical minerals, inaddition to reducing greenhouse gas emissions and limiting disposal. When reuse or recycling is notpossible, disposal of electronic waste should involve accurately characterizing the waste and sending
it to proper permitted disposal sites. For all discarded equipment, records should be kept tracking
the disposal of potentially hazardous waste.
The Operating Entity, with the assistance of its Technology Advisory Board, should also
work toward identifying computing technologies that are energy efficient and carbon neutral, andthat have little or no negative effect on water quality, air quality, waste accumulation, soil
contamination, or the U.S. carbon footprint. The Operating Entity could consider evaluating
potential resource providers based on the energy efficiency and/or environmental sustainability ofthe design of the proposed resources. For example, resource providers could work with theEnvironmental Protection Agency’s Energy Star for Data Centers program 28 to improve
efficiency, reduce data center cooling energy, and optimize environmental performance.
The Operating Entity and resource providers should acquire, develop, and promote the useof tools to monitor and optimize applications for energy-efficient operation. This would require
NAIRR resources to be instrumented with technologies that can identify utilization and energy use
at the component level, as energy usage is specific to an application’s execution. They should also
identify application development tools and environments that can assist a programmer in thecreation of highly energy-efficient applications and promote energy-efficient user behaviors.
These tools should also help the operating system to allocate system capacity to each application
with the goal of optimizing energy use.

28

The Operating Entity should also promote the importance of studying environmental issues
through its support of relevant AI research areas. It should track and report on the percentage oftime the NAIRR infrastructure is used for environmental research. Possible areas of study include
environmental systems modeling and analysis, climate modeling, bio-systems modeling,watershed modeling and analysis, energy systems management, and waste management.
Predictive maintenance and sensor systems learning are other relevant areas of AI research.

29

4. NAIRR Structure and Specifications
for Resource Elements
The NAIRR Operating Entity should develop an integrated portal to provide the user basedescribed in Chapter 2 with access to a federated mix of on-premise and commercial computationaland data resources and services. Computational resources would include conventional servers,
computing clusters, HPC, and cloud computing, and should also support access to edge computing
resources and testbeds for AI R&D. The NAIRR Operating Entity should make open and protected
data available via resource providers and partnerships. Data should be co-located withcomputational resources where possible. Data providers should facilitate user access to restrictedstatistical data through the Standard Application Process (SAP) established under the 2018
Foundations for Evidence-Based Policymaking Act, where appropriate and possible. 29 The
NAIRR Operating Entity and resource providers should make software, training, and educational
resources available to support a diverse set of users with varying levels of AI research experienceand proficiency.
This chapter provides details of these key components, along with desired capabilities when
the NAIRR begins initial operations. Given the fast pace of technological development, theOperating Entity should maintain the flexibility to adjust approaches to the elements detailed
below, in consultation with the Steering Committee and Program Management Office.

Access Portal and User Interface
The Operating Entity is responsible for development of an NAIRR user portal that supports
key user functionalities such as single sign-on, team allocations, data search and discovery,
collaboration tools, resource discovery, job submission, consolidated accounting, spend alerts,
information about data use, and cost-optimization of workflows. The portal will be one way toaccess NAIRR resources. Alternate access methods such as secure shell or scripting interfaces
should also be made available for advanced users. The portal will allow users to select their AI
applications, computational resources, and data sources from a curated catalog, and to launch andmonitor jobs from a portal that provides a uniform, integrated view.
The portal should have built-in help functions and an integrated help desk ticketing system.
The portal should maintain an up-to-date catalog of resource provider user documentation andtraining materials. Chat functions, meeting rooms, forums, and other functionality may be included
to support collaboration and community building among students, researchers, resource providers,and other users. The portal should also enable data search and discovery and leverage automatedtechnologies so that (1) metrics on data use can drive data acquisition and (2) diverse, communitydriven data curation, linkage, and validation activities can be fostered. A user account would be
required to manage computational allocations, monitor usage, submit jobs, and post to thecommunity forum.
The Operating Entity should provide a public website through which some key elements areavailable without the need for a user account and sign-on. For example, linked catalogs of AI
30

education tools and testbeds, as well as an index of AI datasets with metadata, annotations ofknown problems and deprecation status, and community-contributed code, should be readily
available.
The Operating Entity should assess the cost of building the user portal and public website inhouse versus acquiring it commercially. To speed development, the Operating Entity could
outsource the design, construction, and maintenance of the user portal to a commercial entity thathas previously created successful user portals. All major aspects of the portal should be includedin NAIRR initial operational capabilities.

Computational Resources
To lower barriers to entry into AI research, the Operating Entity and resource providers must
make access to computational and data resources available to a variety of new users who otherwise
would face financial, logistical, or capacity challenges engaging in the AI research ecosystem.
Expanded access should be provided by leveraging existing resources in all sectors, augmenting
the capacity of federally provided resources as appropriate, creating new research computing anddata infrastructure to serve the AI R&D community, and providing financial support where needed.
The NAIRR should also support the federation of user-supplied computing resources, testbeds,
and sensors at the edge.
Capacity and Capability
When fully implemented, the NAIRR should address both the capacity (i.e., ability to support
many users) and capability (i.e., ability to train the most resource-intensive AI models) needs of
the AI research community. To meet existing capacity needs, the NAIRR should provide a mix ofcomputational resources (i.e., on-premise and commercial cloud, dedicated, and shared resources)
with a range of central processing unit (CPU) and graphics processing unit (GPU) options withmultiple accelerators per node, high-speed networking, and sufficient memory capacity (i.e., atleast one terabyte per node). The exact balance of computational resources will depend on the
results of resource provider funding opportunities. Users should have the option of selecting which
resources they would like to use through a range of mechanisms, including the user portal, directcommand-line access, or optionally interactive “notebook”-like environments.
To meet users’ capability needs, the NAIRR system should include at least one large-scalemachine-learning supercomputer capable of training 1 trillion-parameter models. This could bemade available by leveraging an existing supercomputer or newly procured through a competitivebid process managed by the Operating Entity in consultation with the Steering Committee andrelevant advisory boards.
NAIRR Software Resources
AI research has grown explosively through the development and dissemination of open
source software (OSS) frameworks including TensorFlow, PyTorch, and their derivatives. Boththese packages were developed by commercial entities and could have been kept proprietary.
Instead, they were released as OSS projects, to the benefit of, and for further development by, the31AI research community. The success of these projects has inspired many other OSS projects andtools. 30
The Operating Entity, with advice from the Technology Advisory Board, should assess OSS
packages most used by AI researchers and specify a standard software environment for the NAIRR
federation. 31 This software environment should be containerized as a lightweight virtual machine,and be supported across resource providers. Academic teams with their own on-premise servers
would be encouraged to adopt the NAIRR federation standard. In addition, the Operating Entity
should explore new AI workflow orchestration tools and templates for standard AI analysis tasks,
such as cnvrg.io, 32 which can meet the needs of industry researchers and might be suitable for
adoption by the NAIRR federation.

Data and Datasets
The Operating Entity should provide a search and discovery service with metadata about theusage of all datasets. Such a service should be consistent with Section 202(c) of the Evidence Act.
It should be designed to dovetail with the capabilities anticipated through development of a Federal
data catalog, but extend beyond Federal data.
The Operating Entity should support data resource providers by either funding the creationof or providing continuing support to existing AI data repositories. In coordination with theTechnology Advisory Board, the Operating Entity should publish interoperability guidelines forsuch data repositories, and encourage data repositories to compete to become NAIRR data resource
providers. These guidelines should be informed by the Desired Characteristics of Data
Repositories for Federally Funded Research developed by the National Science and Technology
Council’s Subcommittee on Open Science. 33 Having such repositories and datasets visible,
searchable, and discoverable inside the NAIRR, as well as implementing mechanisms to trackdataset use, are important to the success of the NAIRR.
NAIRR-Open and NAIRR-Secure zones should federate computational, network, and data
resources operating in accordance with security and access-control policies that are uniform within
the zone, but different between zones, reflecting the restrictions associated with the data in eachzone. NAIRR-Secure should coordinate and collaborate with the program office designated by the
Office of Management and Budget to oversee the SAP, and others as appropriate, in making
available and specifying security and user access controls required for restricted (confidential)
government and third-party data.29 SAP is required by the Evidence Act to be the “front door” foraccessing restricted data within the possession of Federal statistical agencies.

32

Dataset Acceptance Criteria and Metadata Standards
The Operating Entity should evaluate and characterize datasets into tiers, each with a different
level of acceptance criteria. Examples include high, medium, and low levels of metadata;
provenance; information about dataset usage, and the availability of persistent identifiers. The
Operating Entity should ensure that each dataset is evaluated according to industry standards orbest practices and that a determination is made on how each should be categorized. Where possible,
such cataloging efforts should be aligned with efforts to develop a Federal data catalog.
The Operating Entity should not define dataset standards, as this area continues to evolve
rapidly and would be best addressed by the community of users. However, the Operating Entity
should provide a public-facing list of acceptable formats to ensure compatibility with resources
and tools, encourage broader use, and leverage existing community-driven principles andstandards such as those developed by the Research Data Alliance and NIST, among others.
Regardless of category, substantive documentation should be provided with each directory or filecontaining data. The Operating Entity should also specify what it means for a dataset to be“analysis-ready” and categorize datasets accordingly. For example, an analysis-ready dataset
should be in a structured format (e.g., a relational table or JSON 34 or Neo4j 35 formats) and should
include details such as the semantics and provenance, information about the data-generation
process, a data dictionary, related code, summary statistics for quality-assurance purposes, andinformation about how it has been used in previous analyses. Further, such a dataset should
conform to standards in cases where datatypes are normally represented in a standard ontology(e.g., geographic information system [GIS] vector objects, gene ontology codes for molecules).
Not all datasets need be in analysis-ready form. Some types of data or partial datasets are importantor rare, and can be contributed with the goal that others can help transform them into analysisready data.
Role of the Operating Entity in Incentivizing and Curating Contributed Datasets
and Other Resources
Since the quality of many AI models depends on high-quality training and test data, theOperating Entity should establish a data service that facilitates access to and additional use ofexisting curated datasets of value and interest to the NAIRR user community. Curation of AI data,
models, tools, and workflows should be done by the user community in an AI data commons,
facilitated by the NAIRR search and discovery platform. Such a community system, governed byterms of use as well as a review system, would facilitate data sharing and curation by members of
the community. In the context of a commons model, researchers who contribute to the common
good through data curation and code sharing, and whose contributions are recognized and valued
by relevant communities, could be incentivized through high-profile NAIRR recognition and/or
preferential access to NAIRR resources.
The NAIRR Operating Entity should test, on a trial basis, a service for searching for,discovering, and curating valuable external data as well as data generated with NAIRR resources.
One option would be to contract with one or more commercial AI marketplaces to meet its users'

33

data curation needs. The “AI marketplace” is a powerful concept that has emerged in thecommercial sector; it refers to the social and technical infrastructure through which the user
community contributes, documents, and shares data, codes, and models. Contributions arevalidated and valued by the community, and community standards are enforced by the company
managing the marketplace. Another option is for the NAIRR to develop its own “AI data
commons” with attributes similar to a commercial marketplace. Such an option is likely to bepreferable for the federally funded NAIRR. However, since both commons and marketplace
options have merit, the Operating Entity should have flexibility regarding development of data
curation services, and the services should be implemented on a trial basis and evaluated for efficacy
by the Operating Entity in the first five years of NAIRR operation.
Substantial Operating Entity resources should be dedicated to technical support staff who can
support community-driven curation efforts. Data users, contributors, and curators will require
support to understand and meet the technical standards of NAIRR data repositories. Further,training and additional support will be critical to the integrity and quality of NAIRR datasets, andto protect privacy, civil rights, and civil liberties.
The NAIRR and Existing Federal Government Data
Federal agencies hold data that could fuel foundational, use-inspired, and translational AI
research in domains such as transportation, healthcare, and natural hazards research. Sources ofFederal agency data include statistical data, administrative data, and data from federally funded
intramural and extramural research. While some of these datasets are already accessible to the
public, many others are not.
Since Federal datasets could be highly valuable to AI research and advance national goals,
there are three other Federal Government data efforts with which the NAIRR could engage. One
is data.gov, which is a website that points to other resources containing information and data
generated by agency or agency-funded projects. Most of the retrievable data on data.gov are in
web or text form, which might be of interest to some NAIRR researchers. However, scientificnumerical datasets are deeply buried in data.gov and not easily accessible. The Operating Entity
and Program Management Office could work with data.gov to encourage additional contributions
conforming to NAIRR data acceptance criteria, which should include measures of data use.
Another is the SAP, through which researchers will be able to discover and apply for access torestricted data acquired by Federal statistical agencies through a single application process andportal. 36 Finally, the National Secure Data Service (NSDS) demonstration project, established by
the CHIPS and Science Act of 2022, has the potential to complement the SAP and existingstatistical agency efforts with additional capability for data acquisition, linkage, and protection(see Box 6 for more details).

34

The Steering Committee should facilitate the establishment of a NAIRR-Federal Interagency
Council on Statistical Policy (ICSP) working group. This working group should collaborate toassess options for establishing a secure node for the purpose of enabling large-scale AI analysis ofgovernment data for statistical purposes. Where such resources are not intended to be madeaccessible via the SAP or the NSDS
Box 6. The National Secure Datademonstration project, the working groupService Demonstration Project
should define the Confidential Information
The CHIPS and Science Act of 2022 includes
Protection and Statistical Efficiency Acta provision that requires NSF to create acompliant data access protocols and controls.
demonstration for the National Secure Data
This NAIRR-ICSP collaboration should
Service (NSDS). The intent of thisdemonstration is “to develop, refine, and test
facilitate the provisioning of timely access formodels to inform the full implementation of theappropriate (i.e., approved) projects to
Commission on Evidence-Based Policymaking
restricted (i.e., confidential) government andrecommendation for a government-wide data
linkage and access infrastructure for statistical
third-party data.
activities conducted for statistical purposes.”

The NAIRR should also encourage andsupport additional contributions of State andlocal datasets conforming to NAIRR data acceptance criteria, and subject to the legal requirementsof the State and local government agencies, either by working with data.gov 37 or the eventual
NSDS.
In terms of existing high-quality data repositories managed by agencies such as the National
Institutes of Health (NIH) and NASA, the Operating Entity will need to determine whether toreproduce large datasets that are already available from these other sources or find other means ofcoordinating access for NAIRR researchers. This coordination could benefit from regularconvening of leadership from various Federal data efforts to identify ways to improve coordination
and avoid inefficiency or redundancy.
Legal Compliance
The Operating Entity should ensure that data access through the NAIRR is in compliancewith applicable Federal laws. Consider, for example, data use agreements (DUAs), which arecontractual documents established between provider and recipient institutions and used for thetransfer of nonpublic or restricted data. A DUA in the case of the NAIRR would benefit from being
structured around the Five Safes framework to ensure safe use. Generally, a DUA will define
publication responsibilities, disposition of intellectual property arising out of the use of the data,
ownership of derived datasets, and expectations for disposal of the data. The use of a DUA is goodpractice because it establishes a clear understanding of the expectations and responsibilities of bothparties.
It is anticipated that an SAP Governing Board29 will be the primary Federal entity withresponsibility for overseeing the process by which secure access to protected Federal statistical
data is approved for both government and external users, taking into account aspects of privacy,
civil rights, and civil liberties. Rather than create a duplicative infrastructure, the Operating Entity

35

should coordinate closely with the SAP Governing Board to ensure that NAIRR users are awareof and have appropriate access to Federal statistical data provided through the SAP.

Co-Location of Resources
AI training datasets can be many terabytes in size. With current technology, moving thisvolume of data over the commercial internet would take many hours at typical network speeds.
Effective computing within a research cyberinfrastructure that handles high-volume data will
likely require the co-location of data with the hardware on which it will be processed. The
Operating Entity should facilitate the co-location of data and computational resources in two ways:(1) invest in the build-out of a NAIRR AI data commons infrastructure at the HPC centers coupledwith an expansion of computational capacity and (2) negotiate contracts with the public clouds
with educational discounts that provide access to the most popular computational and storage
solutions for AI researchers. The Operating Entity should also provide access to existing AIrelevant resources that co-locate computation and data.
The Operating Entity should additionally create and curate a searchable and discoverable
catalog of existing and available governmental and non-governmental datasets, including
providing information about their usage, that may be distributed across the United States. These
datasets, particularly the confidential data, need not be co-located with the computational resources
provided by the NAIRR, although some datasets could be copied to co-located storage to facilitate
better performance. Datasets created using the NAIRR infrastructure should be stored at co-located
NAIRR storage facilities. Thus, there should be a mix of distributed and co-located datasets as partof the NAIRR infrastructure with multiple mechanisms to support efficient use of those datasets,
including a partnership with the SAP Governing Board and eventual NSDS.

Educational Tools and Services
To lower the barriers to participation in the AI ecosystem and increase the diversity of AI
researchers, the NAIRR must be broadly accessible to a range of users and include educational
and technical information. The NAIRR access portal should provide catalogs and search anddiscovery tools to facilitate access to educational and training materials for a range of experiencelevels.
The NAIRR should provide a platform that can be used for educational and communitybuilding activities. This platform can provide facilitation functions for educational efforts, but the
Operating Entity should not be responsible for developing general or discipline-specificeducational content, because general education on AI and computational expertise is not the
primary mission of the NAIRR.
Technical training and support materials related to the use of the NAIRR are within scope,and the Operating Entity and resource providers should share the responsibility for training andsupport in the use of NAIRR resources.

36Tiered Technical Training and Support
The Operating Entity should provide technical training materials for users at different skill
levels (e.g., beginner, intermediate, and advanced). Training options should span a range offormats, including web pages, tutorials, webinars, online training, and customized remote
workshops. Training should include use of the portal itself, in addition to training and otherinformation on the particular resources available via the NAIRR portal, as well as NAIRR policies
and procedures.
Curation of Training Materials
To support the needs of a diverse set of users, the Operating Entity should build aconsolidated, searchable catalog of training materials generated by NAIRR resource providers sothat everything is listed in one place. Resource providers should provide context-based training
resources as well as just-in-time training. The Operating Entity should also facilitate identificationand curation of additional AI- and resource-related training materials by the user community. The
system should be instrumented to track highly used pages and tutorials to help resource providers
better understand how users are getting the information they need and to refine how the content isdelivered (e.g., static documentation versus interactive tutorials).
The level of training required should be commensurate with the nature of NAIRR usage. For
example, short-term, non-sensitive use of the NAIRR, such as a short classroom exercise, may
warrant less rigorous requirements. Because the user base for the NAIRR is intended to be broadand diverse, training should be tailored for various audiences. Tiered user training documentation(e.g., beginner, intermediate, and advanced) and interactive tutorials should be created and kept
current by resource providers.
Platform for Educational Activities
The NAIRR should provide user access to educational infrastructure made available byeducational resource providers. An example of this concept can be found in CloudBank, which
provides users with access to the Berkeley Data Stack, 38 a collection of tools and resources that
support data science research and education at the University of California, Berkeley. The
Berkeley Data Stack provides each student with an interactive learning environment via a Jupyter
notebook interface to Jupyter Books, integrating notebooks and computational content with
textbooks developed by the instructor.

Technical Integration
Software for Integration
Software will be needed to federate the diverse resources incorporated into the NAIRR.
Examples include grid toolkit software, an information-publishing framework, resourcedescription repository, accounting and account-management software, a common user
environment, a single sign-on hub, and file transport services. As an example that the NAIRR
could build from, many of these solutions are being used in the NSF ACCESS program (i.e., thefollow-on to XSEDE, which began in September 2022). 39 The NAIRR should leverage such37

developed software approaches, and the NAIRR Operating Entity (with advice from theTechnology Advisory Board) should evaluate existing integration software stacks such as that usedin ACCESS for possible adoption.
The NAIRR infrastructure should support distributed workflow orchestration software. 40 The
NAIRR user portal will need to be fully integrated with these software functions as part of
NAIRR’s full operational capabilities.
Integrating Data Resources
One approach that will facilitate NAIRR technical integration is incorporating Federal data
resources stored in commercial clouds. Several Federal agencies have placed large datasets ofpotential interest to external researchers in the commercial clouds, taking advantage of the publicdata hosting programs. A June 2022 National Science and Technology Council report entitled
Lessons Learned from Federal Use of Cloud Computing to Support AI R&D 41 notes that “use of
the cloud has simplified computational access to data owned and maintained by Federal agencies,
facilitating efficient use of and collaborative work with big data. For example, over 36 petabytes
of public and controlled access genomic sequencing data hosted by the NIH's National Library ofMedicine are now available on two commercial cloud computing platforms, 42 and 10 petabytes ofpublic weather and environmental data are now accessible through the National Oceanic andAtmospheric Administration (NOAA) Open Data Dissemination Program across three commercial
cloud computing platforms.41 NASA has taken similar steps, storing newly collected Earth Science
data in the cloud to make it easier for the public to access and reduce the requirement ofdownloading data to perform analytics.” 43 The Operating Entity should leverage and replicate thisapproach to enable effective use of large-scale data in the cloud.

Testbeds
AI testbeds are simulated, live, or blended environments that support research, prototyping,development, and testing of AI applications. Increasing access to testbeds via the NAIRR will
provide researchers without institutional testbeds the opportunity to explore new approaches forsolving important problems. Testbeds can be broadly defined as serving the purpose of either
comparison or validation. Comparison testbeds allow researchers to measure the effectiveness of
new engineering, math, or algorithmic developments. These testbeds can take the form of test
frameworks and competitions, simulated environments, or living laboratories and are useful forfoundational, use-inspired, and translational AI R&D. Validation testbeds allow developers todecide whether it is acceptable to move up the maturity cycle of an end-to-end system to a moreadvanced phase of development, and are useful for translational research. Note, however, thatvalidation testbeds supported through the NAIRR are intended for early-stage and translational
research, rather than for the purpose of validating commercial products.
The Operating Entity should facilitate connections to AI testbeds. It is likely that each AI
testbed will have unique requirements for connection and/or integration. The Operating Entity,
with consultation from the Science Advisory Board and Technology Advisory Board, should
determine which testbeds should be made accessible via the NAIRR as part of initial operational
38

capabilities, including through consideration of which interfaces, protocols, and controls arenecessary to facilitate access to each.
With an AI data commons model, testbeds can be reviewed and made available, maintainedby their creators with the incentive of exchange with other assets in the marketplace. The Operating
Entity should work with the Networking and Information Technology R&D (NITRD) Program,which catalogs Federal AI testbeds, to expand the inventory beyond federally funded resources.
NITRD may wish to transfer this responsibility to the Operating Entity.

39

5. Phased Buildout of NAIRR Organization and Resources
The NAIRR cyberinfrastructure should be established in a phased manner with a gradual
ramp-up of resources over time. Phasing can help ease the process of integration across thefederated NAIRR system, provide opportunities for users to transition as older resources age out
and new resources come online, provide value to users more quickly, and allow the NAIRR
Operating Entity to receive user feedback expeditiously. 44
This approach is also intended to avoid challenges associated with acquiring AI-relevantcyberinfrastructure, which develops at a rapid pace and can quickly become outdated. Agencies
that have already invested in AI should be part of a collaborative process for identifying thecomputational, data, and training needs. New agencies that are just beginning to invest in AI canwork with other agencies to identify gaps and capabilities that would be useful for those agencies'
missions.

NAIRR implementation has been divided into four phases, as indicated in the graphic above.
The timelines in this report assume that work will begin immediately after the publication of thisfinal report, but they may also be adapted as appropriate. To start, the federated NAIRR systemshould be built out from the baseline of existing computational and data resources, augmenting
their capacity and capability while
Box 7. NAIRR Pilot Option
making them discoverable andaccessible through the NAIRR user
The implementation plan presented in this reportportal. This should be accomplished intargets an initial operation of the NAIRR in late year 1.
parallel to investments in new
To expedite the availability of AI research resources to
the AI R&D communities as early as year 0, the NAIRR
computational and data resources to
Task Force proposes that the NAIRR Program
serve and grow the capacity of the AI
Management Office provide pilot-scale access toresearch community. A NAIRR Pilot
existing computational resources, software, datasets,
services, and user portals across the current national
Option could run in parallel to thiscyberinfrastructure
ecosystem,byprovidingbuildout, as described in Box 7.
NAIRR should achieve initial
operational capability—availability of
the core user portal and a basic
complement of computational and data
resources for users—no later than 21months from the U.S. Government
launch of the program. Steady-state
operations,
duringwhich
thesupplemental funds for this additional use by the
beginning of year 1 and issuing broad calls to the AI
R&D community to apply for this access. Setting upsuch a pilot would require rapid establishment ofinterim management and governance mechanisms.
The pilot would operate until the NAIRR is fully
operational in year 2, at which point it would ramp
down; the Program Management Office canincorporate its learning from this experience into its
implementation of the NAIRR.

40

cyberinfrastructure system has met target capacity and capabilities for all components, should beestablished by the fourth year, with the understanding that the system should evolve and grow on
an ongoing basis. Periodic evaluation and horizon scanning should inform changes to system
operations, governance, and technology components to keep the federated infrastructure current
and optimize utility.

Phase 1: Program Launch and Operating Entity Selection

The first steps to launching the NAIRR are the responsibility of the Federal Government.
Congress should authorize and appropriate funds to establish the NAIRR as soon as possible. The
NAIIO within OSTP, together with the agency that serves as the administrative home for the
NAIRR Program Management Office, should coordinate the formation of the Steering Committee,
and the agency that serves as the administrative home should stand up and staff the Program
Management Office. The Program Management Office and the Steering Committee should write
and release the funding opportunity for the Operating Entity within the first six months andestablish the criteria and process for selecting the awardee. The Steering Committee should work
toward developing necessary coordination processes for the selection and funding of NAIRR
resource providers.
During months 6–12, proposals for management of the Operating Entity should be received,
reviewed, and decided on by the Program Management Office, under the oversight of the Steering
Committee, using the defined selection process and criteria. By the end of this period, the contract
for the Operating Entity should be made, and the awardee should begin work.

Phase 2: Operating Entity Startup

Internal Planning and Operations
The Operating Entity startup phase begins when the contract has been established. As soon
as possible, the Operating Entity should hire staff; establish strategies, policies, and procedures;charter and stand up the User Committee and advisory boards, establishing the Ethics andTechnology Advisory Boards as soon as possible, and the Science Advisory Board within six
months of the award; and conduct information-gathering and assessment to inform the design of
the NAIRR user portal, interface, security and access controls, and support services. The Operating
Entity should build in technical and policy tools to support privacy, civil rights, and civil liberties

41

considerations and NAIRR evaluation and assessment planning into its policies and procedures;
these considerations must begin as soon as possible. In its first six months, the NAIRR Operating
Entity should initiate biannual (or more frequent as needed) meetings of its boards and committees,develop governance policies and legal frameworks for constituent participation, and develop
business processes and policies.
Within six months of its award, the Operating Entity should have developed and publishednecessary operational plans and policies, with input from the Program Management Office,Steering and User Committees, advisory boards, and other constituents—including members of
the public and public interest groups. These include operational plans for the following:(1) Addressing privacy, civil rights, and civil liberties issues.
(2) Creating NAIRR scientific integrity policies, user policies, data use agreements, andother legal requirements.
(3) Developing specific user access controls and security architectures for both NAIRR-Open
and NAIRR-Secure.
(4) Supporting the process for selection of NAIRR resource providers.
(5) Incentivizing participation and resource contribution, including through establishment ofan AI data commons.
(6) Managing resource allocations and user onboarding, including procedures for soliciting,reviewing, and managing those research proposals for which it directly administers
resource allocations, and coordinating with agencies on allocations reserved for agencyfunded researchers.
(7) Providing transparent communication of information about how to access resources via
the NAIRR—along with catalogs of AI resources such as datasets, software, educational
tools, and testbeds—through a public-facing website.
(8) Gathering and providing information to the independent, external evaluator, to ensurethat NAIRR performance assessment can be planned early and infrastructure elements
can be designed and adapted to facilitate collection of key data for assessment of KPIs
across all NAIRR operational stages.
These plans should be reviewed periodically over the life cycle of the NAIRR and adapted
as needed for different phases of operation and to best achieve the NAIRR’s KPIs. Work should
be focused on meeting strategic objectives and goals as the research community needs evolve overtime.
Establishment of Initial NAIRR Resource Components
In its startup phase, the Operating Entity should federate the first resource providers, establish
an appropriate portal and user interface for accessing these resources, and identify its external
evaluator in coordination with the NAIRR Program Management Office. As part of these efforts,
the Operating Entity, Program Management Office, and Steering Committee should develop
coordinated, multi-agency funding opportunities for resource providers as soon as possible, ideally42within six months of the initial Operating Entity award. These opportunities should be funded by
the Steering Committee agencies and administered by the Program Management Office. The
funding opportunities should also (1) call for the inclusion of existing resources that could beincorporated into the NAIRR without the need for additional funds and (2) fund the expansion of
AI-capable computational and data resources at a subset of competitively selected existing
advanced cyberinfrastructure sites. In addition, the Operating Entity should negotiate one or more
public cloud contracts at discounted rates to provide researchers with access to the latest
technologies and cloud-resident datasets with minimal startup overhead.
Winners of the funding opportunities should be chosen based on the scientific and technicalmerit of the proposals, cost effectiveness, and the suitability of the proposed systems for advancing
and democratizing AI R&D. The first round of funding opportunities should allow additional time(not repeated in future opportunities) to bring the resources to a production state, as the technicalintegration process might still be under development for the first cohort. Subsequent opportunities
should be used to fund the procurement and operation of new AI-tailored resources, bothexperimental and production, cloud and on-premise, and shared and dedicated, at new or existingsites.
Staffing for user support should be included in the proposal of any resource provider.
Resource providers should be expected to provide competent technical support for users of theresources they provide, although the Operating Entity staff should provide help-desk functions.
User-training materials should be developed and made available before the launch of theinfrastructure. A separate resource provider for curation of education and training materials andcatalogs of testbeds and datasets (with metadata including history and deprecation status) could
also be funded if the Operating Entity does not manage this in-house.
The overhead cost for an open-data system is dramatically lower than that of a system that
holds sensitive data; the legal and user agreement requirements are less stringent for open data as
well. Both types of data will be necessary for a successful NAIRR, and providers will need to be
identified and funded if the Operating Entity does not develop this infrastructure in-house. Opendata can probably be made available prior to sensitive data, even if the resource providers begin
work simultaneously. The initial set of opportunities for data-resource providers should include
both providers of open data and providers of secure access to sensitive data. However, it isreasonable to expect that the initial roll-out to users will support only open data, because the legal
and regulatory issues associated with sensitive data likely require more time to address.
The Operating Entity should determine its approach to the design, construction, andmaintenance of an integrated user portal and interface to all resources that are part of NAIRROpen, establishing preliminary capabilities during the startup phase. The Operating Entity should
also immediately invest in building an evaluation data infrastructure sufficient to establish
benchmarks and track progress over time. The evaluation data should include internal data aboutawarded and declined research proposals, as well as resource allocation information from allparticipating agencies. Information about the publications and patents resulting from research andresearchers leveraging the NAIRR should be captured using automated methods. Administrative
data from Federal, State, and local government data sources, as well as the private sector, should43

be used to capture economic impact, leveraging the National Secure Data Service whereapplicable. (See Appendix H for illustrative examples of potential KPIs or evaluation metrics.) An
example of a successful approach is the Institute for Research on Innovation and Science (IRIS)
at the University of Michigan. 45
All of the Operating Entity’s startup activities should leverage the support of the User
Committee and advisory boards, to the extent possible, to gather information and assess R&D
community needs. The Operating Entity should consult frequently with the NAIRR Program
Management Office and Steering Committee throughout its startup activities.

Phase 3: NAIRR Initial Operational Capabilities

The goal of the initial operational phase is to establish policies, processes, and technicalresources that can be accessed in the near term by AI researchers and developers and that will
support further buildout and maturation of the NAIRR. Initial NAIRR operational capabilities
should be made available to researchers within nine months of the Operating Entity award. These
capabilities should consist of (1) a portal and associated user-support resources, including indexesof resources and training materials; (2) a mix of operational on-premise and cloud resource
providers, preferably with access to at least one ML supercomputer capable of training one trillionparameter models; (3) a workable allocation and identity-management system; and (4) a workabledata-publication system that allows datasets to be added to a catalog with a digital object identifier.
These elements are sufficient at launch, although there are more that should be added soon after(e.g., common software stack, automated monitoring, AI data commons). The NAIRR-Open portal
and at least some data sources should also be available.
The NAIRR’s initial operational capability should include a minimum complement of
resources for users in the near term. The NAIRR-Secure portal and enclave, sensitive datasets, andnew experimental and production AI-tailored computational resources may require additional time
to mature and enter use. These resources should continue to develop during initial operations, withthe goal of bringing all first-cohort resource providers into operational use by the end of the initial
operating phase.
Initial Computational Resources
To facilitate a federation of existing on-premise and commercial cloud resources, established
Federal agency programs could be leveraged. For example, the NIH Science and Technology
Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative
program provides access to Amazon Web Services, Microsoft Azure, and Google Cloud
resources. 46

44

The NSF pilot CloudBank 47 provides a portal with four commercial cloud resources (i.e.,
Amazon Web Services, Google Cloud, Microsoft Azure, IBM Cloud). The NSF Partnership to
Advance Throughput Computing 48 and National Research Platform23 programs provide access tofederated national infrastructure including commercial cloud services.
While it might not be feasible for the initial resource mix to provide a full complement ofarchitectures, it should include at least one “experimental” resource with something other thancommon CPU/GPU hardware (e.g., embedded or Internet of Things infrastructure, or new silicon
for AI). For example, NSF and DOE support several federated distributed computing
infrastructures that facilitate emerging AI experiments in a cyber-physical environment. When
crafting the multi-agency funding opportunities for new resource providers, the Steering
Committee, Project Management Office, and Operating Entity should consider how to complement
existing federally supported resources included as part of the NAIRR initial operational
capabilities.
Based on a Task Force analysis, the computing capacity goal for the NAIRR is 48–60 million
hours on quad-GPU nodes in its initial operational capability. This level of capacity would allow
50,000 researchers (including students) to have access to 1,000 hours per user. Alternatively,
25,000 researchers would have access to 1,000 hours per user and up to 40 teams per year could
solve a problem at the scale of OpenAI’s GPT-3 benchmark, 49 one of the largest (and mostexpensive to train) deep-learning models to date. By the time NAIRR reaches its full operating
capability near the end of year 2, three times this capacity (140–180 million hours on quad-GPU
nodes) should be available. This capacity corresponds to a NAIRR steady state supporting 150,000
researchers to have access to 1,000 hours per user of computing time, or, alternatively, 75,000
users and up to 120 teams per year could research GPT-3 benchmark-level problems.
Initial Data Resources
Data resources made available during the NAIRR’s initial operations should leverage existingFederal and commercial data repositories. Particular attention should be paid to existing largescale data infrastructures that have co-located data and computational resources, such as the NIH
All of Us 50 and National COVID Cohort Collaborative 51 programs, to develop approaches forlinking them into the NAIRR in alignment with the interoperability guidelines developed by the
Operating Entity.
An initial instantiation of the NAIRR AI data commons should be in place during the
NAIRR’s initial operations, with infrastructure and staff support for data hosting, data search anddiscovery, sharing, and community curation activities. Work to develop the tiered-accessinfrastructure to enable the provisioning of approved access to sensitive data should also be
underway, in coordination with the establishment of the SAP and NSDS.
Initial Research Using the NAIRR
Once the NAIRR is available to support research, the Operating Entity should initiate
processes to onboard users—both investigators who propose projects that would receive NAIRR
resource allocations from the Operating Entity directly and those who are funded by and receive
NAIRR credits or approvals from participating Federal agencies. To do so, the Operating Entity
45

must implement its policies and mechanisms for allocating computational credits and providinguser access and training. During the initial operations phase, the Operating Entity should monitor
system performance and resource utilization to learn lessons that can inform full operations. The
advisory boards should review initial operations and provide advice to the Operating Entity
regarding how the NAIRR can improve its performance during full operations.

Phase 4: NAIRR Ongoing (Steady-State) Operations

Evolution of NAIRR Resources
The NAIRR should evolve through periodic funding opportunities, developed in response touser uptake and demand. The first round should result in the selection of approximately one-thirdof the expected steady-state capacity of the NAIRR. Subsequent funding opportunities should beannounced every other year at the same funding level. The Operating Entity should continue tosolicit production and experimental resources and strive for architectural and resource diversity.
Capability should be added to support emerging areas of interest and need by the researchcommunity and industry.
By three years after the initial operational capabilities are available, the set of resources
needed to achieve full NAIRR capacity should have been funded by participating agencies, andtheir federation managed by the Operating Entity. Thereafter, approximately one-third of theresources may be replaced or updated every two years, while two-thirds of the resources should
remain in production operation, providing both continuity and the opportunity to incorporate
innovations. A minimum of 18 resource providers should be part of the NAIRR in the steady state,
across a balance of types and architectures. Beginning in year 6, resource providers should beallowed to recompete.
Full NAIRR capacity should include a vibrant AI data commons as well as access to sensitive
data through a secure, tiered-access system for vetted users and approved research projects. The
Operating Entity, in partnership with the Steering Committee, should work across the Federal
Government to make existing data repositories searchable, discoverable, and accessible via the
NAIRR.
The Operating Entity should continue to take input from the research community via its User
Committee and determine what capabilities should be added to the NAIRR infrastructure overtime. These additions should be vetted through the Science Advisory Board and Technology
Advisory Board, and inform the Operating Entity and the Program Management Office’s
development of new funding opportunities and decommissioning of older components. All newcapabilities should be added to the catalog of available infrastructure elements and made accessible
via the user portal. The Technology Advisory Board should also periodically survey the evolving
AI tool landscape and provide advice on additions or deletions from the NAIRR standard virtual46

machine. The goal should be to add capabilities to continually serve the AI R&D needs of the
NAIRR user community over time.
Partnership Engagement Operations
Partnerships will be important for providing the resources and expertise needed to maintaina cutting-edge NAIRR. The Operating Entity should establish public- and private-sector
partnership mechanisms to extend both the NAIRR’s scope and its user base. Relevant partnershipmechanisms would likely vary by sector and entity type. Since the NAIRR is envisioned as a large
federation for a defined user community, partnerships could hinge on the balance of benefits for
the NAIRR user community and the Nation writ large, and for the partner resource provider. In
some cases, this would mean contribution of resources—either co-funding or in-kind support—in
exchange for access to resource allocations to the partner or other benefits such as workforce
recruitment pipelines, opportunities for collaboration, or to learn from NAIRR user research thatleverages the resources provided. In the case of a public university, this might mean adding
resources from a campus compute cluster or campus data collections to the NAIRR federation in
exchange for additional time for that university’s researchers.
Private-sector partnerships could work similarly. Private entities can compete to become
resource providers within the NAIRR, in which case they would make resources available inexchange for funding. But other models could also be defined, where companies could make inkind contributions (e.g., tools, data, models, computational resources) in exchange for access to
NAIRR resources.
User Outreach, Engagement, and Support Operations
The Task Force envisions a NAIRR where resource providers deliver support for the
resources that they provision. Central operations functions, including support for the central portal
and technical interoperation of the resource providers—as well as significant efforts forbroadening participation, outreach, education, and training—should be the responsibility of the
Operating Entity.
Outreach and International Collaboration
The Operating Entity should establish a small team to represent the NAIRR organization atinternational conferences where constituents gather—AI conferences as well as domain-specificareas in the life sciences, physical sciences, and social and behavioral sciences, etc. 52 The teamshould document successes in science stories, ensure that NAIRR opportunities are disseminatedbroadly through domestic and international networks, and coordinate presentations and outreachin key forums.
Once the NAIRR has reached full operations, the Steering Committee and Operating Entity
should explore ways to leverage the NAIRR to advance AI research through international
cooperation with similar resource infrastructure efforts around the world. In doing so, theOperating Entity should follow the guidelines for international collaboration set by OSTP and U.S.
Government research funding agencies, and comply with relevant export controls. The Operating
Entity must also avoid activities or assigning access to its infrastructure to any embargoed or47sanctioned countries, institutions, organizations, or persons. Otherwise, the Operating Entity
should work to establish collaboration and the sharing of information between U.S. and non-U.S.
research entities. As it matures, NAIRR should leverage existing international forums such as theInternational Science Council’s Committee on Data (CODATA 53) and the Global Partnership on
AI to support ongoing international collaborations and foster new opportunities.

NAIRR Budget
Based on a Task Force analysis of the estimated number of users and recent historical information
regarding the cost of high-performance computing capacity, NAIRR costs are estimated at$2.6 billion over a six-year period (see Table 1). 54 In estimating the budget for the NAIRR, theTask Force (1) focused initially on only the advanced computing resources that would be providedby (or through) the NAIRR, based on costs for existing advanced computing resources, and then(2) supplemented that estimate with estimates for other requisite NAIRR capabilities such as data,
software workflows, and education and training. The Task Force assumed that all federally funded
AI researchers throughout the United States from the targeted user communities would use the
NAIRR to some extent. The Task Force further assumed that the average computing used by a
NAIRR user would be comparable to that of a typical researcher using advanced computing
resources. For additional context on the cost of training large ML models, see Box 8.
Table 1. NAIRR Six-Year Budget Summary
Year

Resource Providers

Operating Entity

Evaluation

Total

1

$375M

$70M

$5M

$450M

2

$375M

$60M

$5M

$440M

3

$375M

$60M

$5M

$440M

4

$375M

$60M

$5M

$440M

5

$375M

$60M

$5M

$440M

6

$375M

$60M

$5M

$440M

6-year total$2.25B

$370M

$30M

~ $2.6B

Specifically, using one agency’s current advanced computing investments during the period
FY 2016–FY 2021 as a proxy and considering known oversubscription of about 125 percent, theTask Force identified that an investment of over $1 billion would have been necessary during thisperiod. These investments would have provided advanced computing resources to a community of
about 19,000 users spanning about 2,300 active projects totaling about $6 billion in Federal R&D
investment. Put another way, the average advanced computing investment needed per 1,000 users
is about $53 million, and the average advanced computing investment needed per $1 billion ofFederal R&D funding is about $169 million.

48

In arriving at the final budget
estimate, the Task Force took the above
Many recent breakthroughs in AI capabilities haveinvestments and estimates into account,been achieved through the creation of large,
along with an assumption that the scale ofcomputationally-intensive deep learning models. In
investment and size of the AI community
the pursuit of more generalizable capabilities, such
will continue to grow rapidly in the years
models have been growing in size: OpenAI’s GPT-3
in 2020 broke barriers at 175 billion parameters.
ahead. The bulk of the estimated budget
Google followed suit in 2021 with a 1.6 trillionof $2.6 billion (i.e., $2.25 billion) funds
parameter model, and the Beijing Academy of
the NAIRR resource providers. Resource
Artificial Intelligence with a 1.75 trillion-parametermodel soon after. Published cost estimates ballpark
providers should be brought online every
that training a 110 million-parameter language model
two years with a six-year lifetime,
costs about $50,000, a 340 million-parameter model
requiring a new $750 million investment
costs about $200,000, and a 1.5 billion-parameter55
to be made every two years to ensure thatmodel costs about $1.6 million. Overall, the costdepends on multiple factors, including size of the
NAIRR resources remain at the state oftraining dataset, model architecture, and the number
the art. The Operating Entity budget isof training runs.
estimated at approximately $60 million
per year to support the coordination and management of NAIRR activities (see Table 2). An
additional $5 million per year is needed to support the Operating Entity’s external evaluation
process. The budget for the Operating Entity is based upon historical experience that the annual
cost of operations for complex cyberinfrastructure is approximately 20 percent of the cost of thecyberinfrastructure resources themselves. Funding for the Operating Entity and external evaluation
should be appropriated by Congress to the administrative home of the NAIRR, with suitablelanguage to permit funds to be used to initiate and staff the Program Management Office. Funding
for the NAIRR resource providers should be appropriated by Congress to the agencies that willfund them.
Box 8.Training Large AI Models

Resource providers should receive awards that allow them to provide services for up to
six years to the NAIRR user community. Resource providers can fall into several categories, andin some cases the operation and acquisition costs for the resource may be blended. For example, aresource provider may have an initial cost in the acquisition phase for the hardware, followed byan annual cost in an operational phase to cover support personnel, maintenance, power, and soforth. A resource provider whose resource is providing training to the NAIRR may have almost no
acquisition-phase costs and substantial operational costs. As a result, any funding opportunity forresource providers should include a mix of acquisition and operations funds for the resources
themselves. Based on the experience of other federally funded computing operations, annual
operations should not exceed 20 percent of the acquisition cost. Following this model, a six-year
award should budget roughly 45 percent of the total for acquisition and 55 percent for operations.
Resource provider awards should be capped at $200 million, corresponding to a $90 million
acquisition with $110 million for operations. To ensure a diversity of providers, the largest awards
should be reserved for large computing investments, with smaller caps defined for data and service
awards. A minimum of six awards should be made per cohort.

49

Table 2. Operating Entity Costs
Cost Category

Base Cost

Year One Startup Cost

Central Portal and Resource Integration$10M/year$15M

Training and User Support$15M/year$15M

Data Integration and Curation$5M/year$10M

Internal R&D and Technology Development Efforts$15M/year$15M

Other Operating Entity Allocations (e.g., advisory boards,
governance activities)$15M/year

$15M

Total

$60M/year$70M

NAIRR Evaluation (Phases 1–4)
The NAIRR system should be designed to achieve its objective and goals in a deliberatemanner. A “theory of change” for the NAIRR—that is, a causal model or map of how the goals ofa program are intended to be achieved—can inform this process and provide a framework for its
planning and evaluation. This includes articulating the inputs (i.e., available resources to leverage),
activities (i.e., actions or work conducted to advance the program), outputs (i.e., the immediate,
practical benefits of the program), outcomes (i.e., medium-term results), and longer-term impactsof the overall NAIRR effort, and how each successively feeds into the next (see Figure 6 forillustrative examples). The NAIRR is envisioned as a complex system with numerous entities
responsible for creating, operating, and overseeing its components, integration, services, andpolicies.

Figure 6. Example Elements of a Theory of Change for the NAIRR

NAIRR governance entities should adopt a standard evaluation framework predicated on aclearly defined theory of change. The Steering Committee and Operating Entity, in collaboration
with other NAIRR entities, should develop and publish appropriate KPIs based on this frameworkduring NAIRR Implementation Phases 1–2, and adapt them as needed as the system matures. KPIs
should be developed early with input from experts in program evaluation to ensure that datacollection mechanisms are built into NAIRR processes in a timely and reproducible manner (i.e.,
specific, measurable, attributable, realistic, and targeted).
50

To ensure objective and rigorous evaluation of the Operating Entity, resource providers, andoverall NAIRR performance, the governance entities must enlist an expert and independent entity
to act as its evaluator. Evaluation should be conducted against appropriate baseline measures and“counterfactuals” (i.e., scenarios or proxies for a particular outcome or metric that would occur inthe absence of the NAIRR).
To assess the performance of the NAIRR system and its progress toward achieving its four
goals, its cognizant entities must plan for and participate in periodic, independent evaluation.
Ideally, the NAIRR system should be designed and established deliberately, using appropriate
inputs to its activities for achieving near-term outputs, longer-term outcomes, and high-level
impacts. Evaluation should be conducted at the level of (1) all aspects of the NAIRR system as awhole, (2) the Operating Entity, (3) the resource providers, and (4) individual research projects
and users making use of the NAIRR. All four assessments may be conducted via one evaluation
process conducted by the external evaluator. The NAIRR Steering Committee should develop
KPIs for each entity in collaboration with NAIRR constituents and in alignment with NAIRR
goals. KPIs can be technical, such as total computational power; usage-related, such as accesscounts for datasets or training tools; or human-centered, such as number of users. The NAIRR
should be architected to facilitate the capture of KPIs that can be readily accessed through adashboard and made available to the Steering Committee, Program Management Office, andOperating Entity. KPIs should also address diversity and equity—for example, not only the numberof users, but also the demographics and institutional diversity of users.
The Operating Entity should develop clear expectations for each resource provider, including
milestones and deliverables, tied to the KPIs and consistent with the mission of the NAIRR. The
expectations should be reviewed by the Program Management Office, the Steering Committee, theUser Committee, and the advisory boards and posted on the NAIRR website. There should be a
mid-term evaluation of each resource provider by an external evaluator selected by the Operating
Entity and approved by the Program Management Office. Failure of a resource provider to perform
according to expectations should trigger a probationary period. Continued or longer-term failure
to perform should result in decommissioning a resource provider.
The KPIs for the NAIRR resource providers and Operating Entity should be a limited set ofhigh-level metrics that the Program Management Office can initially use to monitor and evaluate
the operational effectiveness of the research resources coordinated and the services provided by
the Operating Entity to the user community. These metrics should be clearly stated and published.
KPIs should be vetted by the Steering Committee, the Program Management Office, and User
Committee, and published for public comment. These metrics should form the basis of the RFPs
for resource providers and for subsequent program calls. Responsibility for defining KPIs for key
NAIRR units is summarized in Table 3.
Table 3. Roles in KPI Definition and Frequency of External Evaluation
NAIRR Unit to Be EvaluatedOverall NAIRR performance

KPIs Defined by
Steering Committee, with input from Program
Management Office, Operating Entity, User
Committee, advisory boards

51

Frequency of Reporting byExternal Evaluator
AnnualNAIRR Unit to Be Evaluated

KPIs Defined byFrequency of Reporting byExternal Evaluator

Operating Entity

Steering Committee, with input from Program
Management Office, Operating Entity, User
Committee, advisory boards

AnnualResource Providers

Operating Entity, with input from User Committee
and advisory boards

Mid-term and Annual

Evaluation activities include planning and preparation, information gathering andassessment, and release of and response to evaluator findings. Since the NAIRR requires
substantial startup time, the evaluation itself should be phased in over several years. In Phases 2–
3, for example, the evaluation should focus on NAIRR’s inputs, activities, and outputs, which
would be primarily process-driven, and on establishing baselines for longer-term outputs andoutcomes. These initial evaluations should focus on implementation by the NAIRR and the
Operating Entity as well as the resource providers. Subsequent evaluations should begin toevaluate progress towards the intended goals and outcomes of the NAIRR itself. The evaluation
should expand in years 4–6 to include outcomes, while years 7–9 should also evaluate and measureprogress toward the broader impacts.

Roadmap for Implementation
An infrastructure as complex as the NAIRR would require several years before it is fully
operational, although the NAIRR is expected to reach its initial operational capability, in which it
can begin to serve its envisioned user base, approximately two years after program initiation.
Detailed implementation steps for key actors are summarized in Figure 7 for the four phases
defined for establishment of the NAIRR: (1) Program Initiation and Operating Entity Selection,(2) Operating Entity Startup, (3) NAIRR Initial Operational Capabilities, and (4) NAIRR SteadyState Operations.

52

Figure 7. NAIRR Implementation Roadmap

53

Steps to Initiate the NAIRR in 2023: Actions for the U. S. Government
Congress and the Federal agencies should take the following actions in 2023, as part ofPhase 1: NAIRR Program Initiation, to begin establishing the NAIRR.
For the President and Executive Branch Departments and Agencies
The development and sustainment of the NAIRR will require active involvement by many
Federal agencies, which will need to participate in the Steering Committee and the Program
Management Office, allocate funds for the resource providers, and oversee the NAIRR’s
execution. The agency serving as the administrative home will need to establish a Program
Management Office and allocate funds for the Operating Entity.
For the NAIRR to be successful, it will need to reach all major AI-using researchcommunities—and for that to occur, all of the Federal research agencies that invest in AI R&D
will need to participate in the management and funding of the NAIRR.
For Congress
Congressional legislation has continually reaffirmed the Federal Government’s commitment
to funding cutting-edge information technology R&D. The success of the NAIRR initiative will
depend on similar commitments from the Federal Government using similar legislative tools andauthorities. The long-term continuation of U.S. strategic advancement and leadership in AI
depends on guidance and commitment from Congress. (See Appendix I for proposed NAIRR
authorizing legislation drafted by the Task Force.)54

6. Conclusion
AI is an engine of innovation that is already driving scientific discovery and economic
growth, and is an integral component of solutions that stand to impact everything from routine
daily tasks to societal-level challenges. To realize this promise, we must provide opportunities forresearchers throughout the Nation to pursue cutting-edge AI research. As a Nation, we must cometogether to expand access to the resources that fuel AI, providing pathways for more Americans
to pursue AI R&D and to access state-of-the-art resources. The NAIRR can help to broaden therange of researchers involved in AI, growing and diversifying approaches to and applications of
AI. The NAIRR can help create opportunities for progress across all scientific fields anddisciplines, including in critical areas such as AI auditing, testing, and evaluation; trustworthy AI;
bias mitigation; and AI safety. Increased access and diversity of perspectives would, in turn, leadto new ideas that would not otherwise materialize and set the conditions for developing AI systems
that are inclusive by design. The vision for a NAIRR laid out in this final report of the NAIRR
Task Force can help meet this national need through a shared research cyberinfrastructure
connecting researchers to the resources and tools that fuel AI R&D. The Task Force has presented
a roadmap for doing so in a manner that builds from existing Federal investments; designs-in
protections for privacy, civil rights, and civil liberties; and promotes diversity and equitable access.
The NAIRR can help transform the U.S. national AI research ecosystem by strengthening anddemocratizing foundational, use-inspired, and translational AI R&D in the United States.

55

Appendix A. Definitions
Artificial intelligence (AI): See Box 1.
Cyberinfrastructure: Refers to infrastructure based on distributed computer, information, andcommunication technologies, including the enabling hardware, algorithms, software and services,
communications, institutions, and expertise.
Experimental System/Resource: A system or resource that is exploring a new hardware orsoftware capability and may provide an immature or rapidly evolving environment for the user to
run in. Users may expect additional efforts to port applications to properly use the capabilities ofthe system, rather than a “turnkey” environment, and not all use cases may be well-supported.
Federated system: A set of semi-autonomous, decentralized resources that use a standard set of
protocols allowing for integration, interoperability, and information sharing.
Foundational AI research: Discovery-oriented fundamental research that seeks to advance thefrontiers of AI, including knowledge representation, reasoning, planning, learning, language
processing, perception, vision, motion and manipulation, and so on.
Fundamental Research: Also known as basic research; spans the full spectrum from foundational,
discovery-oriented to use-inspired, solution-oriented research.
National AI Research Resource (NAIRR): See Box 2.
On-premise: Computational hardware that is physically located on the premises of theorganization making use of it, in contrast to remote hardware such as a commercial cloud.
Research on AI: Foundational, use-inspired, and translational research that advances scientificunderstanding of the nature of intelligence, mathematical understanding of the behavior ofadaptive/autonomous systems, or algorithmic understanding of techniques in the component areasof AI (which include perception, learning, planning, and robotics) as well as research related torobustness, scalability, reliability, safety, security, privacy, interpretability, and equity of AI
systems.
Testbeds: Platforms used to conduct research and validate theories, tools, or technologies in arigorous, replicable manner. AI testbeds may take the form of simulated, live, or blended
environments that support prototyping, development, and testing of AI applications that are robust
and trustworthy. The concept of a testbed can encompass the environment itself—hardware andsoftware—as well as the datasets and frameworks that support evaluation and the talent needed to
manage the resource. AI testbeds may take the form of comparison testbeds (allowing researchers
to measure the effectiveness of new engineering, math, or algorithmic developments) or validation
testbeds (allowing developers to decide whether an end-to-end system is acceptable to move up
the maturity cycle to a more advanced phase of development).

A-1

Translational AI research: Research that bridges foundational and use-inspired research with thedelivery and deployment of its outcomes to the target community, and that supports essential bidirection interplays where the delivery and deployment process informs the research; as in,translating research results from the lab to the market and society.
Use-inspired AI research: Fundamental research in AI that is motivated or inspired by particularuse cases, and seeks to advance both the frontiers of AI and the specific use cases.

A-2

Appendix B. Details of NAIRR Task Force Establishment andApproach to Roadmap Development
Charge to the NAIRR Task Force
Congress charged the Task Force with proposing a national solution to provide researchersand students across scientific fields and disciplines with access to data and computing resources
for AI R&D, along with appropriate educational tools and user support. Specifically, Congressdirected the Task Force to develop a roadmap and implementation plan for establishing the
NAIRR. The Task Force was launched on June 10, 2021, as a Federal Advisory Committee cochaired by the National Science Foundation and the White House Office of Science andTechnology Policy, and includes representatives from the U.S. Government, academia, and theprivate sector. Its members’ expertise spans foundational, use-inspired, and trustworthy AI R&D,
as well as research cyberinfrastructure. This report constitutes the Task Force’s final deliverable,
pursuant to its Congressional mandate. Congress specified that the NAIRR roadmap andimplementation plan address nine key dimensions, as stated in Box B.1. The Task Force activities
were bounded to developing recommendations and proposing a roadmap and implementation plan
for a NAIRR to the President and to Congress. The Task Force will conclude its work within 90
days after submission of this final report; the Task Force itself will not execute any of its
recommendations, nor will it be involved in the administration of a future NAIRR.

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Box B.1. Required Elements of the NAIRR Roadmap and Implementation Plan3(1) IN GENERAL—The Task Force shall develop a coordinated roadmap and implementation planfor creating and sustaining a National Artificial Intelligence Research Resource.
(2) CONTENTS—The roadmap and plan required by paragraph (1) shall include the following:
A. Goals for establishment and sustainment of a National Artificial Intelligence Research
Resource, and metrics for success.
B. A plan for ownership and administration of the National Artificial Intelligence Research
Resource, including i. an appropriate agency or organization responsible for the
implementation, deployment, and administration of the Resource; and ii. a governancestructure for the Resource, including oversight and decision-making authorities.
C. A model for governance and oversight to establish strategic direction, make programmatic
decisions, and manage the allocation of resources.
D. Capabilities required to create and maintain a shared computing infrastructure to facilitate
access to computing resources for researchers across the country, including scalability,
secured access control, resident data engineering and curation expertise, provision of
curated datasets, computational resources, educational tools and services, and a userinterface portal.
E. An assessment of, and recommended solutions to, barriers to the dissemination and use ofhigh-quality government datasets as part of the National Artificial Intelligence Research
Resource.
F. An assessment of security requirements associated with the National Artificial Intelligence
Research Resource and its research and a recommendation for a framework for the
management of access controls.
G. An assessment of privacy and civil rights and civil liberties requirements associated with the
National Artificial Intelligence Research Resource and its research.
H. A plan for sustaining the Resource, including through Federal funding and partnerships withthe private sector.
I. Parameters for the establishment and sustainment of the National Artificial Intelligence
Research Resource, including agency roles and responsibilities and milestones to implement
the Resource.

Task Force Approach
The Task Force’s work was divided into phases. The first phase began with the Task Force’s
first convening in July 2021 and culminated with the release of the interim NAIRR report in May
2022. The second phase, between May 2022 and January 2023, was devoted to the developmentof the final report and roadmap.
Initial Phase
During the initial phase, the Task Force convened seven virtual public meetings to discussand deliberate on key NAIRR uses, potential impacts, system requirements, and design elements.
At these meetings, the Task Force heard from expert briefers and panelists to augment themembers’ own expertise, and to ensure that multiple perspectives and experiences were consideredin Task Force discussions and deliberations. A complete list of invited panelists as well as
respondents to the first request for information (RFI), published in July 2021, can be found in
Appendix E.

B-2

The NAIRR Task Force submitted its interim report to the President and Congress in May
2022.14 It set forth the Task Force’s vision for the NAIRR, along with preliminary
recommendations on the nine key areas identified by Congress (see Box B.1).
Final Phase
During the final phase, the Task Force convened four virtual public meetings to discuss anddeliberate on how best to implement the plan published in the interim report. At these meetings,
the Task Force heard from several expert briefers and panelists. Topics addressed by these invited
experts included international perspectives and associated Federal efforts for the provisioning ofdata and computing.
The Task Force also reviewed 23 public responses to a May 2022 RFI asking for commenton the interim report and potential approaches to implementation. These responses reflect feedback
from individuals (ranging from academics to interested members of the public), groups, andorganizations (spanning non-profits, civil society groups, research organizations, and small andlarge businesses). For a full list of respondents and a link to these RFI responses, see Appendix C.
In the course of their deliberations during this phase, Task Force members also engaged withadditional outside subject matter experts (see Appendix D for a complete list of experts consulted)
in support of their considerations toward this final report. A public listening session was held on
June 23, 2022, to provide another opportunity for the public to provide input. Seventy-fourindividuals registered to participate in the listening session, of whom 48 attended. Eight of thoseindividuals spoke at the meeting, including three from civil society or advocacy groups, one fromacademia, one from an industry or industry association group, one from government, one private
citizen, and one other. See Appendix E for a complete list of participants and speakers at thissession.

B-3

Appendix C. Briefers to the Task ForceThe Task Force held eleven public meetings between its launch in July 2021 and the releaseof this final report. At these meetings, the Task Force discussed and developed a vision for the
NAIRR, heard input from invited expert speakers and panelists, and deliberated on key findings
and preliminary recommendations for the design of the NAIRR and its roadmap andimplementation. These outside expert briefers and panelists, along with their affiliations, are listed
here.

July 28, 2021
The STRIDES program
Andrea Norris & Nick Weber, National Institutes of HealthAugust 20, 2021
Value proposition and intended outcomes of a NAIRR
Damian Clarke, Chief Information Officer and Computer Science Faculty, Alabama A&M
University
James Deaton, Executive Director, Great Plains Network
Deborah Dent, Chief Information Officer, Jackson State University
Tripti Sinha, Assistant Vice President and Chief Technology Officer, University of
Maryland, and Executive Director of the Mid-Atlantic Crossroads (MAX)
Talitha Washington, Director, Atlanta University Center Consortium Data Science Initiative
Ownership, governance, and administration models
Sharon Broude Geva, Director for Innovation and Computational Research, University ofMichigan
Manish Parashar, Office Director, Office of Advanced Cyberinfrastructure, National Science
Foundation 56
Gina Tourassi, Director, National Center of Computational Sciences and the Oak Ridge
Leadership Computing Facility, Oak Ridge National Laboratory
John Towns, Executive Associate Director for Engagement, National Center for
Supercomputing Applications and Deputy CIO for Research IT, University of Illinois
at Urbana-Champaign
Frank Würthwein, Interim Executive Director, San Diego Supercomputer Center

C-1

October 25, 2021
Data resources
Ian Foster, Director, Data Science and Learning Division, Argonne National Laboratory;
Professor of Computer Science, University of Chicago
Robert L. Grossman, Professor of Medicine and Computer Science, University of Chicago
Ron Hutchins, Vice Provost for Academic Technologies, University of Virginia
Anita Nikolich, Research Scientist and Director of Research and Technology Innovation,
University of Illinois at Urbana-Champaign
Nancy Potok, CEO, NAPx Consulting; former Chief Statistician of the United States
Andrew Trask, Leader, OpenMined
User resources: portal interface, educational tools
Tiziana Ferrari, Director, EGI Foundation
Kimberly Greene Starks, Global Lead, Infrastructure and Technology Strategy, IBM
University Programs
Ana Hunsinger, Vice President for Community Engagement, Internet2
Ed Lazowska, Professor and Bill & Melinda Gates Chair Emeritus, Paul G. Allen School ofComputer Science & Engineering, University of Washington

December 13, 2021
Privacy, civil rights, and civil liberties requirements
Solon Barocas, Principal Researcher, Microsoft Research; Adjunct Assistant Professor,
Information Science, Cornell University
Lujo Bauer, Professor, Electrical & Computer Engineering and Computer Science,
Carnegie Mellon University
danah boyd, Partner Researcher, Microsoft Research; and Founder/President, Data &Society
Deborah Raji, Fellow, Mozilla Foundation
Nicol Turner Lee, Senior Fellow and Director of the Center for Technology Innovation,
Brookings Institution
Hannah Quay-de la Vallee, Senior Technologist, Center for Democracy and Technology

February 16, 2022
User perspectives on the NAIRR
Tom Dietterich, Distinguished Professor Emeritus in Computer Science, Oregon State
University

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Susanta Ghosh, Assistant Professor in Mechanical Engineering-Engineering Mechanics,
Michigan Technological University
Kinnis Gosha, Hortinius I. Chenault Endowed Associate Professor of Computer Science,
Morehouse College
Gail Rosen, Professor, Drexel University
Rima Seiilova-Olson, Co-Founder and Chief AI Scientist, Kintsugi
Carlos Theran, Research Associate, Florida A&M University

April 8, 2022
Building responsible AI review processes for the NAIRR
Beena Ammanath, Author, Trustworthy AI and Head of Global Deloitte AI InstituteMichael Bernstein, Associate Professor of Computer Science, Stanford University
Arvind Narayanan, Associate Professor of Computer Science, Princeton University
Beth Plale, Professor and Director of the Data to Insight Center, Indiana University
Bloomington
Christo Wilson, Associate Professor of Computer Science, Northeastern University

May 20, 2022
No external speakers; the only agenda item was for the Task Force to vote on the interim
report.

July 25, 2022
International perspectives on the NAIRR
Karine Perset, Head, AI Unit, Division for Digital Economy Policy, OECDMark Leggott, Director of International Relations, Digital Research Alliance of Canada
Renaud Vedel, Chief of Staff to the Minister for the Digital Economy, France
Kazuyuki Takada, Director, Industrial Science and Technology Project Promotion Office,Ministry of Economy, Trade and Industry (METI), Japan
Alison Kennedy, Strategic Adviser, Science and Technology Facilities Council, UK
Research and Innovation
Eliana Cardoso Emediato de Azambuja, General Coordinator of Digital Transformation,
Department of Science, Technology and Digital Innovation, Secretariat of
Entrepreneurship and Innovation, Ministry of Science, Technology and Innovation,
BrazilC-3

September 12, 2022
Associated Federal efforts for provision of data and computing
Shelly Martinez, Senior Statistician, Office of Management and Budget
Vipin Arora, Deputy Director, National Center for Science and Engineering Statistics,
National Science Foundation
Kamie Roberts, Director, National Coordination Office for the Networking and Information
Technology Research and Development Program
Jerry Sheehan, Deputy Director for Policy and External Affairs, National Library ofMedicine, National Institutes of Health

October 21, 2022
No external speakers; the only agenda item was for the Task Force to deliberate on the final
report.

January 13, 2023
No external speakers; the only agenda item was for the Task Force to vote on the final report.

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Appendix D. Public Input Provided on the Interim Report in
Response to the Federal Request for Information
Concurrently with the publication of the interim report, the Task Force issued a Request for
Information (RFI) to solicit public feedback on the Task Force’s preliminary findings andrecommendations outlined in the interim report, and particularly, on how the recommendations
could be successfully implemented. The RFI was open for comments from May 25, 2022, throughJune 30, 2022. This RFI received 24 responses. The list of respondents to this RFI follows. The
full texts of the responses are available at https://www.ai.gov/87-fr-31914-responses/.
 Shavit, Yonadav; Kaushik, Divyansh;
Lipton, Zachary C.; Bowman, Samuel R.;
and Goldner, Kira Sheehan, Matt; Critch, Andrew; Jackson,
Krystal; and Feldgoise, Jacob Software & Information Industry
Association (SIIA)
 Stanford Institute for Human-Centered
Artificial Intelligence (HAI) The MITRE Corporation
 U.S. Chamber of Commerce Technology
Engagement Center University of Arizona, CODATA Centerof Excellence in Data for Society
 University of Southern California (USC)
Information Sciences Institute (ISI) Wehbe, Joseph Wieder, Robin ACT | The App Association
 American Psychological Association(APA) Anthropic Centre for the Governance of AI (GovAI)
 Consumer Reports
 Data Foundation
 Dreifus, Greg and Caso, Luis Videgaray
 Electronic Privacy Information Center(EPIC) Engine Hugging Face
 IBM
 IEEE – USA
 Internet2
 SeedAI

D-1

Appendix E. Public Input Provided on the Initial Federal
Request for Information on Designing the NAIRR
A Request for Information on the design of a NAIRR was posted in the Federal Register (86FR 39081) on July 23, 2021; the comment period closed on October 1, 2021. The Task Force
received 84 responses. The list of respondents to this Request for Information follows; the full textof the responses is available at https://www.ai.gov/nairrtf/86-fr-39081-responses/.
 Abdoli, Abas; Coffee, Ryan N.; Edelen,
Auralee; Kagan, Michael; Ratner, Daniel;
Reddy, Sohail; and Terao, Kazuhiro Accenture ACM U.S. Technology Policy Committee
 The Aerospace Corporation
 AI Now Institute of New York University
and Data & Society Research Institute AI Redefined, Inc.
 The Alexandria Archive Institute (OpenContext)
 Amazon Web Services
 American Civil Liberties Union (ACLU)
 American Psychological Association(APA) Anthropic Argonne National Laboratory
 Atlantic Council GeoTech Center August, Michael BeeHero Booz Allen Hamilton
 C-2
 Cadence CalypsoAI Corp.
 Carnegie Mellon University
 Center for Data Innovation
 Center for Democracy and Technology
 Center for Security and Emerging
Technology
 Cerner Corporation

 Computing Community Consortium,
Computing Research AssociationIndustry, and the Association for the
Advancement of Artificial Intelligence Consumer Reports
 CrowdAI
 Deloitte
 Digital Diagnostics
 Domalpally, Amitha and Channa,
Roomasa
 Ekins, Sean
 Electronic Privacy Information Center(EPIC) Engine The Enterprise Neurosystem
 FABRIC Testbed
 Feddema, John T.; Stracuzzi, David J.;
and Steward, James R.
 Freed, Ben and Choset, Howie
 Freeman, Jared; Leins, Drew; andGaffney, Niall
 Ghosh, Aishik Gilmore, Wayne; Goodhue, John; Hill,
Christopher N.; Kaelli, David; Kolaczyk,
Eric; Kurose, Jim; and Yackel, Scott Google
 Hewlett Packard Enterprise Hyperion Research
 IBM
 Indiana University
 Infiltron
 Information Technology Industry Council

E-1

 Institute of Electrical and Electronics
Engineers (IEEE) Standards Association
 Internet2
 Kapoor, Savash; Kshirsagar, Mihir; andNarayanan, Arvind
 Kubitz, Kermit Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory
Machine Learning Group Lawrence Livermore National Laboratory
 Mathematica
 Medical Imaging and Resource Center,
University of Chicago
 Microsoft
 The MITRE Corporation
 Moffitt Cancer Center NASA NSF AI Institute for Artificial Intelligenceand Fundamental Interactions
 NSF AI Institutes
 NVIDIA
 National Center for Atmospheric Research National Center for Supercomputing
Applications at the University of Illinois at
Urbana-Champaign
 National Energy Technology Laboratory

 NiyamIT, Inc.
 Noblis Northeastern University
 Open Commons Consortium at the Centerfor Computational Science Research, Inc.
 Oracle America, Inc.
 Ossorio, Pila
 Palantir Technologies, Inc.
 Partnership on AI
 Patterson, Maria Representatives from the National
Oceanic and Atmospheric Administration(NOAA) Artificial Intelligence Executive
Committee (NAIEC) and the Center for
Artificial Intelligence (NCAI)
 SAS
 Sirintrapun, Joseph S.
 Stanford Libraries
 Stanford University Institute for HumanCentered Artificial Intelligence (HAI) U.S. Chamber of Commerce Technology
Engagement Center University of Florida
 University of Illinois, Chicago
 Xiao, Steve Yankeelov, Thomas

E-2

Appendix F. Subject Matter Experts Consulted by Task Force
Members
Pete Beckman
Argonne National Laboratory

Christine Kirkpatrick
San Diego Supercomputer Center
Tony LaVoi
National Oceanicand Atmospheric Administration

Jim Brase
COVID-19 HPC Consortium
and Lawrence Livermore National
Laboratory

Aaminah Norris
Algorithmic Justice League

Sandeep Chandra
San Diego Supercomputer Center

Jason Owen-Smith
University of Michigan

Kate Crawford
AINow (NYU)

Joris Poort
ReScale, Inc.

Ian FerreiraCore Scientific, Inc.

Nancy Potok
NAPx Consulting

Brett Goldstein
Vanderbilt University

Catherine Schuman
University of Tennessee, Knoxville

Julie Haney
National Instituteof Standards and Technology

Adam Schwartz
Ames Laboratory

Nick Hart
Data Foundation

Brock Webb
U.S. Census Bureau

Robert Jackson
Spherecom Enterprises

Harlan Yu
UpturnSuzette Kent
Kent Advisory Services

F-1

Appendix G. NAIRR Public Listening Session
On Thursday, June 23, 2022, the Task Force hosted a listening session to collect public input
on the initial findings and recommendations of the interim report. A notice in the Federal Registerannouncing the session was released on May 25, 2022. The notice is available athttps://www.federalregister.gov/documents/2022/05/25/2022-11222/public-listening-session-onimplementing-initial-findings-and-recommendations-of-the-national.
The 74 registrants for the session indicated affiliation with academia, civil society or
advocacy groups, government, industry or industry association groups, private citizens, and others.
Of these registrants, 13 indicated a desire to speak.
Science and Technology Policy Institute researchers opened the meeting by introducing theagenda and goals of the session. NAIRR Task Force Co-Chairs Dr. Manish Parashar and Dr. Lynne
Parker then provided a short briefing to participants on the NAIRR Task Force’s work to provide
context in advance of public comments. During the session, 48 individuals attended, and there
were eight speakers, including three from civil society or advocacy groups, one from academia,
one from an industry or industry association group, one from government, one private citizen, andone other. The session had been scheduled to last for two hours with the possibility of ending early
if all interested speakers had been heard. With a limited number of participants interested in
speaking, the session lasted about 55 minutes and ended early.

G-1

Appendix H. NAIRR Task Force Staff and Contributors
Emily Grumbling
IDA Science and Technology Policy InstituteLisa Van Pay
IDA Science and Technology Policy Institute

Matthew Christman
IDA Science and Technology Policy Institute

Taylor WhiteIDA Science and Technology Policy Institute

Matthew Ishimaru
IDA Science and Technology Policy InstituteBrian Zuckerman
IDA Science and Technology Policy Institute

Morgan Livingston
IDA Science and Technology Policy InstituteKevin Garrison
Institute for Defense Analyses

Logan Practico
IDA Science and Technology Policy Institute

Patricia Sadiq
Institute for Defense Analyses

Michelle Tolbert
Networking and Information Technology
Research and Development Program

Geoff Holdridge
National Nanotechnology Coordination
OfficeH-2

Appendix I. Examples of NAIRR Evaluation Metrics
Data and evidence will be needed for both monitoring and evaluation of the NAIRR system
and activities. A budget must be established for the external evaluator and paid for from Operating
Entity funds. The Operating Entity and Program Management Office, with input from NAIRR
advisory boards and the NAIRR Steering Committee, must agree to a theory of change fordesigning the NAIRR activities and infrastructure that will serve as the basis for evaluation. To
ensure rigor and objectivity, the evaluation should be conducted by an independent, external entitywith expertise in program evaluation.
The following tables provide examples of potential evaluation metrics that might beassociated with the inputs, activities, outputs, and outcomes for a NAIRR theory of change (Table
H.1), as well as for measuring progress toward the four goals of the NAIRR (Table H.2). Where
possible, metrics should be automatically collected and made available in real time to the entities
involved in NAIRR governance. All metrics should be assessed relative to a counterfactual to theextent possible. This could include pre-NAIRR baseline metrics and associated projections, or
metrics for an analogous discipline or research community that has not had the same intervention(that is, does not have a dedicated, federally funded, R&D cyberinfrastructure) over the same
period.
In additional to overall NAIRR performance, the Operating Entity and individual resource
providers must be evaluated. Resource providers should be evaluated for operational efficiency on
the following high-level performance metrics: Customer support, queue times, consultant response
time, computational time and services, allocated time limits, and quality and completeness ofresource documentation. The characteristics of provisioned resources and associated needs mayvary, including by user community. Additional specific metrics for each major category ofprovisioned resources should complement the high-level performance metrics. The overall
portfolio of research supported via the NAIRR should also be evaluated as part of NAIRR
evaluation to support strategic adjustments.

I-1

Table H.1. Examples of Metrics that Could be Associated with a NAIRR Theory of Change
Input Metrics
•

•
•

•

•
•

Number and type ofcomputational and data
resources leveraged
Amount of funding fromFederal agencies
Amount of funding and inkind support provided byphilanthropic
organizations
Amount of funding and inkind support provided byindustry
Staff time (in full-time
employment equivalents)
Expertise included among
staff

Activity Metrics
•
•

•
•
•

•

Outcome Metrics

Output Metrics

Number of RFPs drafted for
NAIRR resources
Number of cross-agency
NAIRR competitions
launched; proposal andacceptance rates
Number of and variety ofworkshops held
Frequency and extent ofoutreach activities
Amount of funding
allocated to eachresource/service type(compute, data, user
training and support,
testbeds)
Number and diversity of
individuals working on
research conducted on the
NAIRR

•

•

•
•

•

•
•
•

•

•

I-1

Number and variety ofresources available tousers via NAIRR
Computational capacity
available for allocation via
NAIRR
Number of high-quality
data sets available
Number of key
information and training
resources available overtime
Resource access statistics,
including processor hours
allocated
Consistency of resource
availability
Number and diversity of
new NAIRR users
Number and diversity of
NAIRR users newly
engaging with AI
Number and diversity of
NAIRR-mediatedcollaborations
Number and diversity ofusers leveraging training
materials

•

•

•
•

Number and diversity of
NAIRR users working inacademia, the private
sector, and non-profits
Earnings and employmentoutcomes of NAIRR users
working in academia, theprivate sector, and nonprofits
Number of startups
established by NAIRR users
Productivity and growth offirms associated with
NAIRR users or as vendors(including new vendor
startups) to research
conducted on the NAIRR

Table H.2. Examples of Metrics for Assessing Progress toward NAIRR Goals
Innovation
•
•

•

•

•

Number of startups
established by NAIRR users
Number of startups
emerging from research
conducted on the NAIRR
Productivity and growth offirms associated with
NAIRR users
Productivity and growth ofvendors (including newvendor startups
established) to support
NAIRR resource/service
providersNumber of“groundbreaking”
publications and patents
across S&E that can betraced to NAIRR users

Diversity
•

•

•

•

Trustworthy AI

Capacity

Number and share of AI“research-involved”
individuals fromunderrepresented orunderserved populations
Earnings and employmentoutcomes of AI “researchinvolved” individuals fromunderrepresented orunderserved populations
when placed
Institutional demographics
of AI researchers and
NAIRR users
Demographics of NAIRR
users, Operating Entity
leaders, and governanceentities

•

•

•

•

•

•

Number of AI “research
involved” individuals(defined as individuals
paid on AI grants or in AI
jobs)
Earnings of AI “research
involved” individuals whenplacedNumber of individuals
leveraging NAIRR for
education and training
Number of AI-intensive
firms with establishment
linkable to NAIRR
Employment in AIintensive firms withestablishment linkable toresearch conducted on the
NAIRR
Number of research
publications, patents, andawards in AI and at theintersection of AI andother fields traceable to
NAIRR users

•

•

•

•

•

•

•

•

I-2

Number of tools
developed for trustworthy
AI leveraging NAIRR
Access statistics for
NAIRR’s AI ethics
education and training
tools
Number and impact of
papers published on AI
ethics/trustworthy AI and
citing NAIRR
Share of AI publications
that address AI ethics,
trustworthiness, andsocietal implications
Number, use statistics, andefficacy of NAIRR ethics
tools and trainings
Extent of NAIRR
engagement with AI ethics
experts
Representation of social
science and AI ethics
expertise in NAIRR
governance entities
Expenditures on tools,
trainings, services, andconsultations related to AI
ethicsAppendix J. Draft Legislative Language for NAIRR
Authorization
The following text represents the NAIRR Task Force’s best efforts to capture its
recommendations in legislative text, with annotations to explain the intent of the Task Force.

SECTION 1. SHORT TITLE.
This Act may be cited as the “National Artificial Intelligence Research Resource Act” or the“NAIRR Act.”
SEC. 2. NATIONAL ARTIFICIAL INTELLIGENCE RESEARCH RESOURCE.
The National Artificial Intelligence Initiative Act of 2020 (15 U.S.C. 9411 et seq.) 1 isamended by adding at the end the following:“TITLE LVI—NATIONAL ARTIFICIAL INTELLIGENCERESEARCH RESOURCE“SEC. 5601. FINDINGS.
“The Congress finds the following:“(1) Much of today’s cutting-edge artificial intelligence research relies on access tocomputational resources and large datasets.
“(2) Access to the computational resources and datasets necessary for artificial
intelligence research and development is often limited to very large technology companies
and well-resourced universities.
“(3) The lack of access to computational and data resources has resulted in insufficient
diversity in the artificial intelligence research and development community.

1

The National Artificial Intelligence Initiative Act of 2020 (NAIIA) appears as division E of the William M. (Mac) Thornberry National
Defense Authorization Act for Fiscal Year 2021 (Pub. L. 116–283) (FY21 NDAA). This draft legislation does not include potential technicalconforming amendments (e.g., to the table of sections in the NAIIA or the FY21 NDAA) necessary to execute the substantive amendment
recommended.

J-1

“(4) Engaging the full and diverse talent of the United States is critical for maintaining
United States leadership in artificial intelligence and ensuring that artificial intelligence isdeveloped in a manner that benefits all Americans.
“(5) The National Artificial Intelligence Research Resource Task Force, authorizedunder section 5106, recommended the establishment of a National Artificial Intelligence
Research Resource in a report entitled “Strengthening and Democratizing the U.S. Artificial
Intelligence Ecosystem: An Implementation Plan for a National Artificial Intelligence
Research Resource” on January 24, 2023.
“SEC. 5602. DEFINITIONS. 2“In this title:“(1) ADVISORY BOARDS.—The term ‘Advisory Boards’ means the advisory boards
established in section 5603(d).
“(2) AI TESTBED.—The term ‘AI testbed’ means a simulated, live, or blended
environment that support prototyping, development, and testing of an artificial intelligence
application, including—“(A) the hardware or software for the environment required for an artificial
intelligence application;“(B) data sets and frameworks that support evaluation of an artificial intelligence
application; and“(C) the individuals required to manage an artificial intelligence application.
“(3) ETHICS ADVISORY BOARD.—The term ‘Ethics Advisory Board’ means theadvisory board described in section 5603(d)(2)(C).
“(4) EXECUTIVE AGENCY.—The term ‘Executive agency’ has the meaning given suchterm in section 105 of title 5, United States Code.
“(5) NATIONAL ARTIFICIAL INTELLIGENCE RESEARCH RESOURCE AND NAIRR.—Theterms ‘National Artificial Intelligence Research Resource’ and ‘NAIRR’ have the meaning
given the term ‘National Artificial Intelligence Research Resource’ in section 5106(g).
“(6) NATIONAL SECURE DATA SERVICE.—The term ‘National Secure Data Service’means the demonstration project established in section 10375 of the Research and2

Because the new title LVI is added to the NAIIA, the definitions located in section 5002 of NAIIA (15 USC 9401) that apply across theentirety of the NAIIA apply in this title as well without explicit reference in this new title. Those defined terms include: (1) Advisory
Committee; (2) agency head; (3) artificial intelligence; (4) community college; (5) Initiative; (6) Initiative Office; (7) Institute; (8) institutionof higher education; (9) Interagency Committee; (10) K-12 education; and (11) machine learning, though not all of those terms are used in thisnew title.

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Development, Competition, and Innovation Act 3 (42 U.S.C. 19085) or any successor
program.
“(7) OPEN SOURCE SOFTWARE.—The term ‘open source software’ has the meaning
given such term in section 2201 of the Homeland Security Act of 2002 (6 U.S.C. 651).
“(8) OPERATING ENTITY.—The term ‘Operating Entity’ means the Operating Entity
selected by the Program Management Office as described in section 5603(b)(4)(A).
“(9) PROGRAM MANAGEMENT OFFICE.—The term ‘Program Management Office’means the Program Management Office established in section 5603(b).
“(10) RESEARCHER.— The term ‘researcher’ means a person who conducts research.
“(11) RESOURCE OF THE NAIRR.—The term ‘Resource of the NAIRR’ means aresource described in section 5604(b).
“(12) SCIENCE ADVISORY BOARD.— The term ‘Science Advisory Board’ means theadvisory board described in section 5603(d)(2)(A).
“(13) STEERING COMMITTEE.—The term ‘Steering Committee’ means the committee
described in section 5603(c).
“(14) STUDENT.— The term ‘student,’ when used with respect to an institution ofhigher education, means an individual who is—“(A) registered as a student with the institution;“(B) enrolled in not less than 1 class of the institution; or“(C) otherwise considered a student in good standing by the institution.
“(15) TECHNOLOGY ADVISORY BOARD.—The term ‘Technology Advisory Board’means the advisory board described in section 5603(d)(2)(B).
“(16) USER COMMITTEE.—The term ‘User Committee’ means the advisory board
established in section 5603(d)(2)(D).
“SEC. 5603. ESTABLISHMENT; GOVERNANCE.
“(a) ESTABLISHMENT.—Not later than 12 months after the date of the enactment of the
National Artificial Intelligence Research Resource Act, the Director of the National Science

3

Division B of what is commonly known as the CHIPS and Science Act (Pub. L. 117-167).

J-3

Foundation, in coordination with the Steering Committee, shall establish the National Artificial
Intelligence Research Resource to—“(1) spur innovation in artificial intelligence research and development;“(2) increase diversity among researchers and students of artificial intelligence;“(3) improve capacity for artificial intelligence research in the United States; and“(4) advance the development of trustworthy artificial intelligence.
“(b) PROGRAM MANAGEMENT OFFICE.—“(1) ESTABLISHMENT.—The Director of the National Science Foundation shallestablish within the National Science Foundation a Program Management Office to oversee
the day-to-day functions of NAIRR and shall appoint an individual, who may be fromanother Federal agency, to head the Program Management Office.
“(3) STAFF.—The head of the Program Management Office may identify staff anddirect all employees of the Program Management Office, in accordance with the applicableprovisions of title 5, United States Code.
“(4) DUTIES.—The duties of the Program Management Office shall include—“(A) in coordination with the Steering Committee and Advisory Boards asappropriate—“(i) developing the funding opportunity and solicit bids for the Operating
Entity;“(ii) selecting through a competitive and transparent process an organization
to be designated the Operating Entity;“(iii) overseeing the appointment of the Director and senior staff of the
Operating Entity;“(iv) overseeing compliance with the contractual obligations of the
Operating Entity;“(v) establishing evaluation criteria for the NAIRR;“(vi) overseeing asset allocation and utilization;“(vii) identifying an external independent evaluation entity; and

J-4

and

“(viii) assessing the performance of the Operating Entity on a periodic basis;“(B) delegating, with appropriate oversight, operational tasks to the Operating
Entity, including—“(i) coordinating the provisioning of Resources of the NAIRR;“(ii) maintaining a portal and associated services for users to accessResources of the NAIRR;“(iii) developing NAIRR policies and procedures;“(iv) hiring and managing a staff (including experts in cyber infrastructure
management, data science, research design, privacy, ethics, civil rights and civil
liberties, legal and policy matters) to support NAIRR operations;“(v) continually modernizing NAIRR infrastructure;“(vi) ensuring diversity, equity, inclusion, and accessibility in all aspects of
the NAIRR, including operations;“(vii) conducting ongoing evaluation and assessment of the NAIRR;“(viii) establishing key performance indicators for the NAIRR, in
coordination with the Steering Committee and Advisory Boards;“(ix) publishing publicly-available annual reports reviewing the performanceof the NAIRR, Resources of the NAIRR, and NAIRR governance structures;“(x) establishing and administering training to new users on accessing a
Resource of the NAIRR; research design; and issues related to privacy, ethics,
civil rights and civil liberties, safety, and trustworthiness of artificial intelligencesystems; and“(xi) facilitating connections to AI testbeds.
“(c) STEERING COMMITTEE.—“(1) ESTABLISHMENT AND MEMBERSHIP.—The Director of the Initiative Office shallestablish a Steering Committee comprising agencies from the Interagency Committee as
determined by the co-chairs of the Interagency Committee to have substantial expertise,
have substantially funded or conducted artificial intelligence research and development, or
have some other significant relationship with the NAIRR.

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“(2) CHAIR AND CO-CHAIRS.—The Steering Committee shall be chaired by the Directorof the Initiative Office. The Director of the Initiative Office may establish co-chairs of theSteering Committee based on members of the Steering Committee rotating on a predetermined schedule.
“(3) CHANGES TO STEERING COMMITTEE COMPOSITION.—The Director of the Initiative
Office shall review the composition of the Steering Committee and update the compositionof the Steering Committee if necessary, not less frequently than every three years. A
member of the Steering Committee may leave the Steering Committee as part of such a
review.
“(4) SUBCOMMITTEES AND WORKING GROUPS.—“(A) IN GENERAL.—The Steering Committee may establish subcommittees,
working groups, or other permanent or temporary bodies of certain members of theSteering Committee.
“(B) WORKING GROUP ON COLLABORATING WITH THE FEDERAL INTERAGENCYCOUNCIL ON STATISTICAL POLICY.—The Steering Committee shall establish a working
group to assess options for establishing a secure node for the NAIRR to enable largescale analysis of government data for statistical purposes in accordance with theStandard Application Process and, as practicable, as part of the National Secure Data
Service.
“(5) DUTIES.—The Steering Committee shall—“(A) coordinate with the National Science Foundation and the Program
Management Office to oversee and approve the operating plan for NAIRR, request thebudget for the NAIRR, develop and release a request for proposals to solicit bids for
the Operating Entity, including establishing the terms and conditions and functions of
the Operating Entity;“(B) work with the Program Management Office to review candidates and selectan entity to act as the Operating Entity;“(C) identify resources that could be federated, participate in resource provider
selection, and provide direction to the Operating Entity about resource allocation andhow those resources should be made accessible via the NAIRR;“(D) define key performance indicators for the NAIRR, in conjunction with theProgram Management Office, User Committee, and Advisory Boards;“(E) evaluate NAIRR performance against the key performance indicators definedin subparagraph (D) on a periodic basis and not less frequently than once every year;

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“(F) develop an annual report transmitted to the Initiative Office and publicly
released on the progress of the NAIRR that includes a summary of the evaluation
concluded in subparagraph (E) and any recommendations for changes to NAIRR; and“(G) oversee a periodic independent assessment of the NAIRR.
“(6) PROVISION OF RESOURCES OF THE NAIRR.—The agencies comprising the Steering
Committee are authorized to provide the Operating Entity with a Resource of the NAIRR or
funding for a Resource of the NAIRR.
“(d) ADVISORY BOARDS.—“(1) IN GENERAL.—The head of the Program Management Office, acting through the
Director of the Operating Entity, may establish Advisory Boards to provide advice to the
Operating Entity and Program Management Office.
“(2) INITIAL ADVISORY BOARDS.—Not later than 3 months after the date ofestablishment of the NAIRR under subsection (a) the head of the Program Management
Office, acting through the Director of the Operating Entity, shall establish the following
Advisory Boards:“(A) The Science Advisory Board comprising representatives from the scientificcommunity, the public, public interest groups, the private sector, and other large-scale
cyberinfrastructure projects to provide advice on the rapidly changing needs acrossmultiple scientific domains.
“(B) The Technology Advisory Board comprising information technology experts
from the private sector, government, and academia to provide advice on technologicaldevelopments to aid the provisioning and use of Resources of the NAIRR and privacy
and security technologies.
“(C) The Ethics Advisory Board comprising representatives of scientific societies,
public interest groups, and government agencies to provide advice on ethics, fairness,bias, risks, privacy, civil rights, and civil liberties related to artificial intelligence.
“(D) The User Committee comprising representatives of different types of
NAIRR users to provide recommendations on—“(i) possible future directions for artificial intelligence research and training;“(ii) user needs and requirements; and“(iii) NAIRR policies and governance.
“(3) MEETING FREQUENCY.—Each Advisory Board shall meet not less frequently than
twice per year.

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“(4) COMPOSITION.—Each Advisory Board shall comprise members from government
agencies, the private sector, academia, and public interest groups.
“(5) SELECTION.—The Director of the Operating Entity shall recommend individuals
for the head of the Program Management Office to select from, after consultation with theSteering Committee.
“(6) TERMS.—Each member of an Advisory Board shall serve for a period of not morethan three years. The terms of initial appointments to any Advisory Board may be staggered
to allow for rotating members.
“(7) REPORTING.—The head of the Program Management Office shall, not lessfrequently than once per year, publicly report the following information for each Advisory
Board:“(A) Name of board.
“(B) Date of establishment.
“(C) Dates of meetings in the preceding 12 months.
“(D) Names and affiliations of members.
“(E) A list of formal reports or other documents produced and summaries ofrecommendations provided.
“(8) NONAPPLICABILITY OF FEDERAL ADVISORY COMMITTEE ACT.—The Federal
Advisory Committee Act (5 U.S.C. App.) shall not apply to Advisory Boards.
“SEC. 5604. RESOURCES OF THE NAIRR.
“(a) IN GENERAL.—The head of the Program Management Office, acting through the
Director of the Operating Entity and in coordination with the Steering Committee, Advisory
Boards and User Committee, shall—“(1) federate, coordinate, and allocate the provisioning of Resources of the NAIRR;“(2) establish policies to govern the procurement and intake of Resources of the
NAIRR;“(3) establish policies on and review Resources of the NAIRR for concerns related to
ethics, privacy, civil rights, and civil liberties, in coordination with the Ethics Advisory
Board;“(4) retire Resources of the NAIRR no longer available or needed; andJ-8

“(5) publicly report a summary of categories of available Resources of the NAIRR,
categories of sources of such Resources of the NAIRR, and issues related to Resources of
the NAIRR.
“(b) RESOURCES OF THE NAIRR.—The NAIRR shall offer at least the following resources:“(1) A mix of computational resources, including—“(A) on-premises, cloud-based, hybrid, and emergent resources;“(B) not less than 1 large-scale machine-learning supercomputer;“(C) public cloud providers providing access to popular computational andstorage services for NAIRR users; and“(D) specifying an open-source software environment for the NAIRR.
“(2) Data, including by—“(A) publishing interoperability standards for data repositories and selecting anddeveloping, through a competitive bidding process, repositories to be available to
NAIRR users;“(B) establishing acceptable criteria for datasets to be used as Resources of the
NAIRR;“(C) identifying and providing access to existing curated datasets of value andinterest to the NAIRR user community;“(D) setting up an artificial intelligence data commons to facilitate community
sharing and curation of data, code, and models; and“(E) coordinating as practicable with the National Secure Data Service to makeFederal statistical data available to NAIRR users.
“(3) Educational tools and services, including by—“(A) facilitating and curating educational and training materials; and“(B) providing technical training and user support.
“(4) AI testbeds, including by—“(A) facilitating access to artificial intelligence testbeds through which
researchers can measure and benchmark engineering or algorithmic developments; andJ-9

“(B) developing a comprehensive catalog of open AI testbeds.
“SEC. 5605. NAIRR PROCESSES AND PROCEDURES.
“(a) USER SELECTION.—“(1) ELIGIBLE USERS.—A researcher, educator, or student based in the United States
and affiliated with the following types of entities, if such entity is based in the UnitedStates, shall be eligible for access to the NAIRR:“(A) An institution of higher education.
“(B) A nonprofit institution (as such term is defined in section 4 of the StevensonWydler Technology Innovation Act of 1980 (15 U.S.C. 3703)).
“(C) An Executive agency.
“(D) A federally funded research and development center.
“(E) A small business concern (as such term is defined in section 3 of the Small
Business Act (15 U.S.C. 632), notwithstanding section 121.103 of title 13, Code ofFederal Regulations) 4) that has received funding from an Executive agency, including
through the Small Business Innovation Research Program or the Small BusinessTechnology Transfer Program (as described in section 9 of the Small Business Act (15U.S.C. 638)).
“(F) A category of entity that the Director of the National Science Foundation andthe Director of the Initiative Office, after consultation with the Steering Committee,
appropriate Advisory Boards, and the public, determine shall be eligible.
(F).

“(G) A consortium composed of entities described in subparagraphs (A) through“(2) USER ACCESS SELECTION.—The head of the Program Management Office, acting
through the Director of the Operating Entity and in consultation with the Steering
Committee, shall establish—“(A) an application for eligible users to request access to the NAIRR; and“(B) multiple selection processes, with increased scrutiny for an application basedon the value or type of Resources of the NAIRR requested.

4

The “notwithstanding” provision waives a requirement that often otherwise exempts from the definition of small business concern, as appliedby regulation, startups funded by certain private funders (e.g., venture capitalists).

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“(b) PRIVACY, ETHICS, CIVIL RIGHTS AND CIVIL LIBERTIES, SAFETY, AND
TRUSTWORTHINESS.—The head of the Program Management Office, acting through the Directorof the Operating Entity and in consultation with the Ethics Advisory Board, User Committee,
Steering Committee, and heads of relevant Executive agencies, shall establish requirements, areview process for applications, and a process for auditing Resources of the NAIRR and research
conducted using Resources of the NAIRR on matters related to privacy, ethics, civil rights andcivil liberties, safety, and trustworthiness of artificial intelligence systems developed using
Resources of the NAIRR. The head of the Program Management Office shall ensure suchrequirements and process are consistent with policies of relevant Executive agencies.
“(c) SCIENTIFIC INTEGRITY.—“(1) IN GENERAL.—The head of the Program Management Office, acting through the
Director of the Operating Entity and in consultation with the Steering Committee, the Ethics
Advisory Board, the Director of the Office of Science and Technology Policy, and the
public, shall develop—“(A) policies for addressing concerns related to matters of scientific integrity,
including matters related to the effects or impacts of research and potential researchenabled by NAIRR; and“(B) mechanisms for an employee of the Operating Entity, an employee of theProgram Management Office, a member of the Steering Committee or an Advisory
Board, a researcher or student affiliated with a NAIRR user, an employee of a NAIRR
resource provider, an employee of a NAIRR funding agency, or a member of the
public to report violations of the policies established under subparagraph (A),including by confidential and anonymous means;“(2) CONSISTENCY WITH GOVERNMENT POLICIES ON SCIENTIFIC INTEGRITY.—The
policies developed in paragraph (1)(A) shall be published in a publicly accessible locationon the website of the NAIRR. Such policies shall, to the degree practicable, be consistent
with the Presidential memorandum entitled “Restoring Trust in Government ThroughScientific Integrity and Evidence-Based Policymaking,” dated January 27, 2021, or
successor document, and reports produced pursuant to such Presidential memorandum(including the report entitled “Protecting the Integrity of Government Science” published by
the National Science and Technology Council and dated January 2022, or successor
document).
“(3) PUBLIC REPORTING.—The head of the Program Management Office, acting
through the Director of the Operating Entity, shall publicly list, and update not lessfrequently than once every 3 months, the following information about each project
receiving any support from NAIRR:“(A) Project name, description, and anticipated value to the public.

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“(B) Names and affiliations of each researcher or student associated with the
project.
“(C) A description of data being used for the project.
“(D) Research questions and methods.
“(E) Anticipated reports or other deliverables and associated expected dates forsuch reports or deliverables.
“(d) SYSTEM SECURITY AND USER ACCESS CONTROLS.— The head of the Program
Management Office, acting through the Director of the Operating Entity and in consultation with
the Steering Committee, Director of the National Institute of Standards and Technology, and the
Director of the Cybersecurity and Infrastructure Security Agency—“(1) shall establish minimum security requirements for all persons interacting with the
NAIRR, consistent with the most recent version of the Cybersecurity Framework, or
successor document, maintained by National Institute of Standards and Technology; and“(2) may establish tiers of security requirements and user access controls beyond theminimum requirements relative to security risks;“(e) FEE SCHEDULE.—The head of the Program Management Office, acting through the
Director of the Operating Entity, may establish a fee schedule for access to NAIRR. The
Operating Entity may only charge fees in such fee schedule. Such fee schedule—“(1) may differ by type of eligible user;“(2) shall include a free tier of access based on appropriated funds and anticipatedcosts and demand; and“(3) may include cost-based charges for—“(A) persons not otherwise considered eligible users to purchase; and“(B) eligible users to purchase Resources of the NAIRR beyond those included ina free or subsidized tier;“(f) OPEN SOURCE AND PUBLIC ACCESS.—The head of the Program Management Office,acting through the Director of the Operating Entity and in consultation with the Science
Advisory Board, Technology Advisory Board, the Director of the Office of Science andTechnology Policy, and the Director of the Cybersecurity and Infrastructure Security Agency,
shall establish policies to encourage—

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“(1) principles of open source, including by encouraging software developed for theadministration of the NAIRR or using Resources of the NAIRR to be open-source software;and“(2) to ensure public access of research conducted using Resources of the NAIRR,
consistent with the principles outlined in Memorandum on “Ensuring Free, Immediate, andEquitable Access to Federally Funded Research” released by the Office of Science andTechnology Policy and dated August 25, 2022, or successor document.
“(g) ENVIRONMENTAL SUSTAINABILITY.—The head of the Program Management Office,acting through the Director of the Operating Entity and in consultation with the Administrator of
the Environmental Protection Agency, may establish policies to—“(1) measure and manage discarded hardware and other electronic waste;“(2) consider environmental impact of hardware when acquiring, developing, or
promoting hardware;“(3) identify or develop application-development tools that assist NAIRR users increating energy-efficient applications; and“(4) research and fund research to study environmental impacts of artificial
intelligence systems.
“SEC. 5606. AUTHORIZATION OF APPROPRIATIONS.
“There are authorized to be appropriated to carry out the activities described in this title$440,000,000 for each of the fiscal years 2023, 2024, 2025, 2026, 2027, and 2028.”

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Appendix K. AbbreviationsAI

artificial intelligenceCPUcentral processing unit

DEIA

diversity, equity, inclusion, and accessibility

DOE

Department of Energy

DUA

data use agreement

FedRAMP

Federal Risk and Authorization Management Program

FFRDC

federally funded research and development center

FY

Fiscal Year

GIS

geographic information system

GPUgraphics processing unit

HPC

high-performance computing

JSON

JavaScript Object Notation

KPI

key performance indicatorMLmachine learning

NAIIO

National Artificial Intelligence Initiative Office

NAIRR

National Artificial Intelligence Research Resource

NASA

National Aeronautics and Space Administration

NIH

National Institutes of Health

NIST

National Institute of Standards and Technology

NITRD

Networking and Information Technology Research andDevelopment

NOAA

National Oceanic and Atmospheric Administration

NSDS

National Secure Data Service

K-1

NSF

National Science Foundation

OSS

open source software

OSTP

Office of Science and Technology Policy

R&D

research and development

RFI

request for information

RFP

request for proposal

SAP

Standard Application Process

SBIR

Small Business Innovation Research

STEM

science, technology, engineering, and mathematics

STTR

Small Business Technology Transfer

K-2

Appendix L. Notes
1

2

3

4

5

6

7

8
9

Throughout this report, “AI R&D” is inclusive of foundational AI R&D, use-inspired AI R&D, and translational
AI R&D. That is, the NAIRR is relevant not only for researchers advancing the field of AI itself (i.e.,foundational research) but also for those who are advancing AI with a use case in mind (i.e., use-inspired
research), as well as for those translating AI discoveries and innovations to the market and society (i.e.,translational research).
For an alternative, yet compatible, definition of AI, please see the John S. McCain National Defense
Authorization Act for Fiscal Year 2019, Pub. L. No. 115- 232, 132 Stat. 1697, (2018).
National Artificial Intelligence Initiative Act of 2020 (Pub.L. 116-283) § 5106(a)(1)(A), 15 U.S.C. §9415(a)(1)(A).
Center for Security and Emerging Technology, “AI Faculty Shortages,” (July 2022),
https://cset.georgetown.edu/wp-content/uploads/CSET-AI-Faculty-Shortages.pdf.
Additional analysis conducted for the Task Force by the Science and Technology Policy Institute identified
404,858 unique researchers affiliated with U.S. institutions who had published at least one AI-related publication
between 2016 and 2021; of these, 14,619 were identified (from any academic department) with at least five AIrelated publications.
The percentages listed correspond to the share of computer science, computer engineering, and information PhD
recipients in North America whose specialties are known that specialized in “Artificial Intelligence/Machine
Learning” or “Robotics/Vision,” as reported in: Stuart Zweben and Betsy Bizot, “2021 Taulbee Survey,” (May2022), https://cra.org/wp-content/uploads/2022/05/2021-Taulbee-Survey.pdf.
Kate Crawford et al., “The AI Now Report: The Social and Economic Implications of Artificial Intelligence
Technologies in the Near-Term,” (July 2016), https://ainowinstitute.org/AI_Now_2016_Report.pdf.
Stuart Zweben and Betsy Bizot, “2020 Taulbee Survey,” (May 2021), https://cra.org/wpcontent/uploads/2021/05/2020-CRA-Taulbee-Survey.pdf;
U.S. Census Bureau, “U.S. Census Bureau QuickFacts: United States,” Accessed November 11, 2022,
https://www.census.gov/quickfacts/fact/table/US/RHI725221.
Daniel Zhang et al., “The AI index 2022 annual report,” (March 2022), https://aiindex.stanford.edu/report/.
Ruha Benjamin, “Race after Technology: Abolitionist Tools for the New Jim Code,” Polity, (2019);
Joy Buolamwini and Timnit Gebru, Gender Shades: Intersectional Accuracy Disparities in Commercial GenderClassification, in Conference on Fairness, Accountability and Transparency, pp. 77–91, Proceedings of Machine
Learning Research, 2018, https://proceedings.mlr.press/v81/buolamwini18a.html;
Kate Crawford, The Atlas of AI, Yale University Press, 2021;
Kate Crawford and Trevor Paglen, Excavating AI: The Politics of Images in Machine Learning Training Sets,
Liverpool Biennial, 9, (2019), https://www.biennial.com/journal/issue-9/excavating-ai-the-politics-of-imagesinmachine-learning-training-sets;
Catherine D'Ignazio and Lauren F. Klein, “Data Feminism,” MIT press, 2020;
Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, St.
Martin's Press, 2018;
Su Lin Blodgett et al., “Language (Technology) is Power: A Critical Survey of 'Bias' in NLP,” arXiv preprint
arXiv:2005.14050, (2020), https://arxiv.org/abs/2005.14050;
Safiya Umoja Noble, Algorithms of Oppression, New York University Press, 2018;
Meredith Whittaker et al., “Disability, Bias, and AI,” AI Now Institute, (2019),
https://ainowinstitute.org/disabilitybiasai-2019.pdf.

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National Security Commission on Artificial Intelligence, “Final Report,” (March 2021),
https://www.nscai.gov/wp-content/uploads/2021/03/Full-Report-Digital-1.pdf.
11
Nathan Benaich and Ian Hogan, “State of AI Report 2022,” (October 2022), https://www.stateof.ai/.
12
For example, the White House Office of Science and Technology Policy’s October 2022 Blueprint for an AI Billof Rights defines a path forward for American leadership in the responsible use of AI; see reference 18 and theaccompanying announcement of relevant actions across the Federal Government:
https://www.whitehouse.gov/ostp/news-updates/2022/10/04/fact-sheet-biden-harris-administration-announceskey-actions-to-advance-tech-accountability-and-protect-the-rights-of-the-american-public/.
13
CHIPS Act of 2022, Pub. L. 117-167, 136 Stat. 1372 (2022).
14
National Artificial Intelligence Research Resource Task Force, “Envisioning a National Artificial Intelligence
Research Resource (NAIRR): Preliminary Findings and Recommendations,” (May 2022),
https://www.ai.gov/wp-content/uploads/2022/05/NAIRR-TF-Interim-Report-2022.pdf.
15
The Networking & Information Technology R&D Program and the National Artificial Intelligence Initiative
Office, “Supplement to the President's FY 2023 Budget,” (November 2022), https://www.nitrd.gov/pubs/FY2023NITRD-NAIIO-Supplement.pdf.
16
As described in: National Research Council, Cooperative Stewardship: Managing the Nation's Multidisciplinary
User Facilities for Research with Synchrotron Radiation, Neutrons, and High Magnetic Fields, The National
Academies Press, (1999), https://doi.org/10.17226/9705.
17
Note: The Task Force’s interim report proposed that the NAIRR be governed by a Board of Directors. The Task
Force has since decided that the NAIRR Operating Entity and resource providers should be primarily responsible
to the Steering Committee, and that oversight from separate groups—the Steering Committee and a Board of
Directors—could result in conflicting direction to the Operating Entity. Thus, the Operating Entity will be led by
its executive staff, answerable to the Steering Committee via the Program Management Office, and with external
input from the User Committee, advisory boards, and external evaluator.
18
White House Office of Science and Technology Policy, “Blueprint for an AI Bill of Rights: Making AutomatedSystems Work for the American People.” (October 2022), https://www.whitehouse.gov/ostp/ai-bill-of-rights.
19
National Institute of Standards and Technology, “AI Risk Management Framework,” (September 2022),
https://www.nist.gov/itl/ai-risk-management-framework.
20
The White House, “Memorandum on Restoring Trust in Government through Scientific Integrity and Evidencebased Policymaking,” (January 2021), https://www.whitehouse.gov/briefing-room/presidentialactions/2021/01/27/memorandum-on-restoring-trust-in-government-through-scientific-integrity-and-evidencebased-policymaking/.
21
National Science and Technology Council, “A Framework for Federal Scientific Integrity Policy and Practice,”(January 2023), https://www.whitehouse.gov/wp-content/uploads/2023/01/01-2023-Framework-for-FederalScientific-Integrity-Policy-and-Practice.pdf .
22White House Office of Science and Technology Policy, “Memorandum for the Heads of Executive Departments
and Agencies,” (August 2022), https://www.whitehouse.gov/wp-content/uploads/2022/08/08-2022-OSTP-PublicAccess-Memo.pdf.
23
National Research Platform, “Designed for Growth and Inclusion,” NRP, accessed January 10, 2023,
https://pacificresearchplatform.org/updates/national-research-platform/.
24
Such as FedRAMP, FISMA moderate, or CMMC level 3. See:
General Services Administration, “FedRAMP,” accessed January 10, 2023, https://www.fedramp.gov/;
Cybersecurity & Infrastructure Security Agency, “Federal Information Security Modernization Act,” accessed
January 10, 2023, https://www.cisa.gov/federal-information-security-modernization-act;
Acquisition & Sustainment Office of the Under Secretary of Defense, “Cybersecurity Maturity Model
Certification CMMC 2.0,” accessed January 10, 2023, https://www.acq.osd.mil/cmmc/.
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Department of Health and Human Services Office for Civil Rights, “HIPAA Administrative Simplification,”(March, 2013), https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/administrative/combined/hipaasimplification-201303.pdf;
Defense Counterintelligence and Security Agency, “Controlled Unclassified Information,” accessed January 10,2023, https://www.dcsa.mil/mc/isd/cui/.
26
National Science Foundation, “Cyberinfrastructure for Sustained Scientific Innovation (CSSI),” (September2022), https://beta.nsf.gov/funding/opportunities/cyberinfrastructure-sustained-scientific-innovation-cssi.
27
National Science Foundation “Pathways Enable Open Source Ecosystems (POSE),” (February 2022),
https://beta.nsf.gov/funding/opportunities/pathways-enable-open-source-ecosystems-pose.
28
Energy Star, “Data Centers,” accessed January 17, 2023, https://www.energystar.gov/products/data_centers
29
National Science Foundation, “Standard Application Process,” National Center for Science and Engineering
Statistics, https://ncses.nsf.gov/about/standard-application-process;
Congress, “H.R.4174 - Foundations for Evidence-Based Policymaking Act of 2018,” (January 2019),
https://www.congress.gov/bill/115th-congress/house-bill/4174.
30
A recent census of the machine learning, artificial intelligence, and data landscape lists more than 500 companies,
startups, and tools—many of them open source. Matt Turck, “Red Hot: The 2021 Machine Learning, AI and
DATA (MAD) Landscape,” (September 2021), https://mattturck.com/data2021/.
31
For example, the Extreme Scale Software Scientific Software Stack developed by DOE’s Exascale Computing
Initiative. E4S, “Home,” accessed January 10, 2023, https://e4s-project.github.io/.
32
Cnvrg.io, “Blueprints,” accessed January 10, 2023, https://cnvrg.io/blueprints/.
33
National Science and Technology Council, "Desirable Characteristics of Data Repositories for Federally Funded
Research," (May 2022), https://www.whitehouse.gov/wp-content/uploads/2022/05/05-2022-DesirableCharacteristics-of-Data-Repositories.pdf.
34
ISO, “ISO/IEC 21778:2017 Information technology — The JSON data interchange syntax,” accessed December
21, 2022, https://www.iso.org/standard/71616.html.
35
Neo4j, “Neo4j Graph Data Platform,” accessed December 21, 2022, https://neo4j.com/.
36
This is required by the Confidential Information Protection and Statistical Efficiency Act (CIPSEA), Pub. L. No.
107–347, 116 Stat. 2962 (2002).
37
Roughly half of the 326,000 entries in data.gov are from State and local governments.
38
The Berkeley Data Stack is a collection of open source tools provided to faculty and students across UC
Berkeley. University of California, Berkeley, “Berkeley Data Stack,” accessed January 10, 2023,
https://data.berkeley.edu/academics/campus-resources/berkeley-data-stack.
39
National Science Foundation, “Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support,”
ACCESS, accessed January 10, 2023, https://access-ci.org/.
40
Such as Kubernetes, SAGE, HTCondor, Kepler, and Pegasus.
41
National Science and Technology Council, “Lessons Learned from Federal Use of Cloud Computing to Support
Artificial Intelligence Research and Development,” (July 2022), https://www.whitehouse.gov/wpcontent/uploads/2022/07/07-2022-Lessons-Learned-Cloud-for-AI-July2022.pdf.
42
National Institutes of Health, “SRA in the Cloud,” National Library of Medicine, accessed January 10, 2023,
https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/.
43
National Aeronautics and Space Administration, “Earth Data Cloud Evolution,” Earth Data, (March 2022),
https://earthdata.nasa.gov/eosdis/cloud-evolution.
44
U.S. Digital Service, “Digital Services Playbook,” accessed January 10, 2023, https://playbook.cio.gov/.
45
VanEseltine et al. “Report on the Scientific and Economic Effects of XSEDE” (August, 2020), Institute for
Research on Innovation & Science (IRIS), University of Michigan, https://iris.isr.umich.edu/wpcontent/uploads/2022/12/IRIS_XSEDE_pilot_report_12August2020_for_comment.pdf.
25

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National Institutes of Health, “STRIDES Initiative,” Office of Data Science Strategy, accessed January 10, 2023,
https://datascience.nih.gov/strides.
47
Cloudbank, “Managed Services to Simplify Cloud Access for Computer Science Research and Education,”
accessed January 10, 2023, https://www.cloudbank.org/.
48
National Science Foundation, “PATh,” accessed January 10, 2023, https://path-cc.io/.
49
OpenAI, “OpenAI API,” accessed January 10, 2023, https://openai.com/api/.
50
National Institutes of Health, “All of Us Research Program,” https://allofus.nih.gov/.
51
National Institutes of Health, “National COVID Cohort Collaborative,” accessed January 10, 2023,
https://ncats.nih.gov/n3c.
52
These are the shorthand names of major international AI conferences: the annual conference of the Association
for the Advancement of Artificial Intelligence (AAAI), Conference on Neural Information Processing Systems(NeurIPS), the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data
Mining Conference (KDD), The Association for Computing Machinery and IEEE Computer Society’s
Supercomputing conference (SC), and the International Conference on Machine Learning (ICML).
53
International Science Council Committee on Data, “Home- CODATA, The Committee on Data for Science andTechnology,” accessed January 10, 2023, https://codata.org/.
54
These figures are in 2022 dollars and do not account for inflation or decline in graphics processing unit (GPU)prices over time.
55
Or Sharir et al., “The Cost of Training NLP Models,” arXiv preprint, arXiv:2004.08900v1, (2020),
https://arxiv.org/pdf/2004.08900.pdf.
46

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