The United States faces intensifying international competition in Artificial Intelligence (AI). The Trump administration’s AI Action Plan places the Department of Commerce at the center of its agenda to strengthen international standards-setting, protect intellectual property, enforce export controls, and ensure the reliability of advanced AI systems. Yet no existing federal institution combines the flexibility, scale, and technical depth needed to fully support these functions.

To deliver on this agenda, Commerce should expand their AI capability by sponsoring a new Federally Funded Research and Development Center (FFRDC), the National AI Laboratory (NAIL). NAIL would:

1. Advance the science of AI,
2. Ensure that the United States leads in international AI standards and promotes the trusted adoption of U.S. AI products abroad, 
3. Identify and mitigate AI security risks, 
4. Protect U.S. technologies through effective export controls. 

While the National Institute of Standards and Technology’s (NIST’s) Center for AI Standards and Innovation (CAISI) within Commerce provides a base of expertise to advance these goals, a dedicated FFRDC offers Commerce the scale, flexibility, and talent recruitment necessary to deliver on this broader commercial and strategic agenda. Together with complementary efforts to strengthen CAISI and expand public-private partnerships, NAIL would serve as the backbone of a more capable AI ecosystem within Commerce. By aligning with Commerce’s broader mission, NAIL will give the Administration a powerful tool to advance exports, protect American leadership, and counter foreign competition.

Challenge

AI’s breakneck pace is having a real-world impact. The Trump administration has made clear that widespread adoption of AI, backed by strong export promotion and international standards leadership, is essential for maintaining America’s position as the world’s technology leader. The Department of Commerce sits at the center of this agenda: advancing AI trade, developing international standards, advancing the science of AI, promoting exports, and ensuring effective export controls on critical technology.

Even as companies and countries race to adopt AI, the U.S. lacks the capacity to fully characterize the behavior and risks of AI systems and ensure leadership across the AI stack. This gap has direct consequences for Commerce’s core missions. First, advances in the science of AI are necessary to ensure that AI systems are sufficiently robust and well understood to be widely adopted at home and abroad. Second, without trusted methods for evaluating AI, the U.S. cannot credibly lead the development of international standards, an area where allies are seeking American leadership and where adversaries are pushing their own approaches. Third, this deep understanding of AI models is needed to identify and mitigate security concerns present in both foreign and domestic models. Fourth, deep technical expertise within the federal government is required to properly create and enforce export controls, ensuring that sensitive AI technologies and underlying hardware are not misused abroad. A deep bench of subject matter experts in AI models and infrastructure is increasingly critical to these efforts.

As AI systems become more capable, the lack of predictable and understandable behavior risks further eroding public trust in AI and inhibiting beneficial AI adoption. Jailbreaking attacks, in which carefully crafted prompts get around Large Language Model (LLM) guardrails, can produce unexpected behavior of models. For example, jailbreaking can prime LLMs for use in cyberattacks, which can cause significant economic harms, or cause them to leak personal information, or produce toxic content, causing legal liability and reputational harm to companies using these models. As companies deploy custom models built on top of LLMs they need to know that medical assistants will not produce harmful recommendations, or that agentic AI systems will not misspend personal funds.  Addressing these concerns is an extremely challenging technical problem that requires more effective and consistent methods of evaluating and predicting model performance. 

The ability to effectively characterize these models is central to the Trump administration’s AI Action Plan, which highlights widespread adoption of AI as a major policy priority, while also recognizing that the government has a key role to play in managing emerging national security threats. The AI Action Plan gives Commerce a central role in addressing these concerns; nearly two fifths of the plan’s recommendations involve Commerce. Commerce’s responsibilities include:

• Creating methods of AI model evaluation and developing international standards.
• Identifying security risks.
• Promoting research on AI interpretability, control and robustness.
• Recruiting leading AI researchers.
• Promoting exports of AI technology.

For a full list of AI Action Plan recommendations involving Commerce, see Appendix A. 

While Commerce has an impressive track record in AI, including through its work at the National Institute of Standards and Technology and CAISI, it will face immense institutional challenges in delivering on the ambitions of the AI Action Plan, which require broad and deep expertise. Like other U.S. government entities, Commerce operates under federal hiring rules that make it difficult to quickly recruit and retain top technical talent. The government also struggles to match AI industry pay scales. For example, fresh PhDs joining AI companies frequently receive total compensation that is twice the cap set for the overwhelming majority of government workers, and senior researchers earn five times this cap or more. In some cases, top researchers may also hold equity in private companies, further complicating their employment by the government. Without a new institutional mechanism designed to attract and deploy world-class expertise, Commerce will struggle to execute on the ambitious goals of the AI Action Plan.

Opportunity

To deliver on the scope of the AI Action Plan, the Department of Commerce needs a dedicated institution with the resources, flexibility, and talent pipeline that existing structures cannot provide. A Federally Funded Research and Development Center (FFRDC) offers this capacity. Unlike traditional government offices, an FFRDC can recruit competitively from the same pools as industry, while remaining mission-driven and independent of commercial interests.

At its core, a new FFRDC, the National AI Laboratory (NAIL), would provide the technical expertise Commerce needs to carry out its central responsibilities. Specifically, NAIL would:

1. Advance the science of AI, including the measurement and evaluation of AI models.
2. Develop the methods and benchmarks that underpin international standards and ensure U.S. companies remain the trusted source for global AI solutions.
3. Identify and mitigate AI security risks, ensuring U.S. technologies are not exploited by adversaries.
4. Provide the technical expertise needed to support export promotion, export controls, and international trade negotiations.

NAIL would equip Commerce with the authoritative science and engineering base it needs to advance America’s commercial and strategic AI leadership.

FFRDCs are unique in combining the flexibility of private organizations with the mission focus of federal agencies. Their long-term partnership with a sponsoring agency ensures alignment with government priorities, while their independent status allows them to provide objective analysis and rapid technical response. This hybrid structure is particularly well-suited to the fast-moving and security-relevant domain of frontier AI. More background information on FFRDCs can be found in Appendix C. 

The current talent landscape underscores the value of the FFRDC model. While industry salaries are high, many senior researchers are constrained by proprietary agendas and limited opportunities to pursue foundational, publishable work. To obtain greater freedom in their research, many top industry researchers have been seeking positions at universities, despite drastically lower salaries. An FFRDC focused on frontier model understanding, interpretability, and security offers a rare combination: freedom to pursue scientifically important problems, the ability to publish, and a mission anchored in national competitiveness and public service. This environment can attract researchers who would not join the civil service but are motivated by high-impact scientific and policy goals.

FFRDCs have repeatedly demonstrated their ability to deliver large-scale technical capability for federal sponsors. For example, NASA’s Jet Propulsion Laboratory has successfully built and landed multiple rovers on Mars, among many other achievements. The Departments of Energy and Defense have led much of the U.S.’ efforts in science and technology assisted by more than two dozen FFRDCs. Their track record shows that FFRDCs are uniquely suited to problems where neither academia nor industry is structured to meet federal needs—exactly the situation Commerce now faces in AI. Commerce currently supports one FFRDC, the fourth smallest. As advanced AI technology grows even more central to Commerce’s mission, it makes sense to add to this capacity.

Plan of Action

Recommendation 1. Establish an FFRDC to support the AI Mission at Commerce.  

Commerce should establish a new FFRDC within two years with a mission to begin important research and timely evaluations. Establishing a new FFRDC requires the sponsoring organization (Commerce in this case) to satisfy the criteria laid out in the Federal Acquisition Regulations (48 CFR 35.017-2) for creating a new FFRDC. Key requirements involve demonstrating needs that are not met by existing sources and that Commerce has sufficient expertise to evaluate the FFRDC. It will require consistent government support through appropriations, and Commerce must identify an appropriate organization to manage it. The rapid pace of AI development makes it an urgent priority to move forward as soon as possible. Recent FFRDCs have taken about 18 months to establish after initial announcement, a significant length of time in the AI field. Further details related to establishing an FFRDC can be found in Appendix D. 

Recommendation 2. NAIL should focus on topics that will advance the Administration’s AI Agenda, including recommendations given to Commerce in the AI Action Plan. 

These topics should include:

• Development of a standardized federal science of measurement that enables evaluation and comparison of models. These evaluations should be predictive of their performance on real-world tasks. NIST has already laid out how measurement science can advance AI innovation in this report.
• Use of these advances in the science of AI measurement for the development of unified AI standards. This would build greater confidence in models, promoting adoption and U.S. AI exports.
• Development of comprehensive methods to assess security implications of models. This includes security concerns in foreign models and vulnerabilities, such as jailbreaks, backdoors, and leakage of sensitive data, and their susceptibility to data poisoning attacks. Of particular note are attacks that can obtain dangerous information related to topics such as biological weapons. While much of this work can be done without access to classified information, NAIL workers may need security clearances, for example, to determine whether models could leak specific secure data. NAIL should also promote AI security by advancing technical work on AI interpretability, robustness, and control, which was highlighted as a priority in the AI Action Plan.
• Determination of whether AI models or hardware provide capabilities that might warrant export controls.

The proposed FFRDC should pursue activities that range from longer term, fundamental research to rapid response to new developments. Much of the knowledge needed to fulfill Commerce’s mandate lies at the heart of the most significant research questions in AI. This requires deep research, which is also important in attracting top tier talent. On a shorter time scale, it will be important for the FFRDC to provide regular evaluations of models as they progress, including the evaluation of security concerns in foreign models. NAIL can speed up these time critical security evaluations. It will also need to use these evaluations to help create and update procurement guidelines for federal agencies and assess the state of international AI competition. Finally, the FFRDC should be a source of expertise that can support Commerce in a wide range of topics such as export control and development of a workforce trained to appropriately take advantage of AI tools.

The FFRDC will also need to work closely with industry to develop standards for the evaluation of models, and support efforts to create international standards. For example, it may seek to facilitate an industry consensus on the evaluation of new models for security concerns. NIST is well known for similar efforts in many technical areas. Finally, the FFRDC should provide a capacity for rapid response to significant AI developments, including possible urgent security concerns.

Recommendation 3. Provide a sufficient budget to cover the necessary scale of work.

There are different possible scales at which NAIL might be created. It is important to note that creating industry scale models from scratch can cost tens or hundreds of millions of dollars. However, the task of evaluating models may be undertaken without this expense by experimenting on models that have already been trained. Much of the published work on model evaluation takes this course. Such evaluations and experiments still require access to significant computational resources, requiring millions of dollars a year in compute, depending on the size of the effort. The FFRDC’s research might also include experiments in which smaller models are built from scratch at a much smaller expense than what is required to train industry sized models.

We consider two alternatives as to the size and budget of the proposed FFRDC:

• Testbed for AI Competitiveness and Knowledge (TACK): A smaller, prototype effort involving dozens of researchers and support staff, including staff that will facilitate collaborations with industry and other agencies. Such a small-scale effort will not be able to address the full range of problems that Commerce has been tasked with, but will be able to contribute to important missions and demonstrate the value of such an FFRDC on an accelerated timeline. This might cost a few tens of millions of dollars per year, on the scale of Commerce’s current National Cybersecurity FFRDC (NCF).
• Full NAIL: A larger-scale effort could address the full range of tasks outlined above. At this scale, the FFRDC could also take the lead in shaping international standards. For comparison, the Software Engineering Institute (SEI) operates as an FFRDC with a staff of roughly 700 and an annual budget of about $130 million.

The figure in Appendix B lists all current FFRDCs and their annual budget in 2023. 

The budget of the FFRDC would need to cover several different costs:  

• Research staff. This would consist of experienced researchers who would lead fundamental research and oversee shorter term technical work.
• Research support staff. This would include experienced developers, many with experience in data collection and cleaning, model training and evaluation.
• Administrative support.
• Policy experts skilled in interfacing with industry and other government agencies.
• Computer staff, with experience in supporting large scale computing resources.
• Computing resources, including funds to purchase GPU clusters or to obtain them through cloud services. 
• Other expenses such as travel, office space, and miscellaneous overhead.

Recommendation 4. Make NAIL the Backbone of a Broader AI Ecosystem at Commerce.

While an FFRDC offers a unique combination of technical depth and recruiting flexibility, other institutional approaches could also expand Commerce’s AI expertise. One option is to expand the Center for AI Standards and Innovation (CAISI) within NIST, leveraging its standards and measurement mission, though it remains bound by federal hiring and funding rules that slow recruitment and limit pay competitiveness.

A separate proposal envisions a NIST Foundation—a congressionally authorized nonprofit akin to the CDC Foundation or the newly created Foundation for Energy Security and Innovation (FESI)—to mobilize philanthropic and private funding, convene stakeholders, and run fellowships supporting NIST’s mission. Such a foundation could strengthen public-private engagement but would not provide the sustained, large-scale technical capacity needed for Commerce’s AI responsibilities. 

Taken together, these models could form a complementary ecosystem: an expanded CAISI to coordinate standards and technical policy within government as well as providing oversight over the FFRDC; a NIST Foundation to channel flexible funding and external partnerships; and an FFRDC to serve as the enduring research and engineering backbone capable of executing large-scale technical work.

Conclusion

The Trump administration has set ambitious goals for advancing U.S. leadership in artificial intelligence, with the Department of Commerce at the center of this effort. Ensuring America’s continued leadership in AI requires technical expertise that existing institutions cannot provide at scale.

NAIL, a new Federally Funded Research and Development Center (FFRDC) offers Commerce the capacity to:

• Push forward our fundamental understanding of frontier AI models along axes that are central to Commerce’s mission, including measurement and evaluation.
• Build the trusted benchmarks and standards that can become the global default.
• Rapidly respond to new technical and security challenges, ensuring the U.S. stays ahead of competitors.
• Provide authoritative analysis for export promotion and control, ensuring U.S. technologies are widely adopted abroad while protected from adversaries, and strengthening America’s hand in international negotiations and trade forums.

By sponsoring this FFRDC, Commerce can secure the talent, flexibility, and independence needed to deliver on the Administration’s commercial AI agenda. While CAISI provides the technical anchor within NIST, the FFRDC will enable Commerce to act at the necessary scale—ensuring the U.S. leads the world in AI innovation, standards, and exports.

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Appendix A. References to the Department of Commerce in America’s AI Action Plan

Appendix B. FFRDC Budgets

Appendix C. Further Background on FFRDCs

FFRDCs in Practice: Successes and Pitfalls

FFRDCs have been supporting US government institutions since World War II. Overviews can be found here and here. In this appendix we briefly describe the functioning of FFRDCs and lessons that can be drawn for the current proposal. 

In a paper by the Institute for Defense Analyses (IDA) a panel of experts “expressed their belief that high-quality technical expertise and a trusting relationship between laboratory leaders and their sponsor agencies were important to the success of FFRDC laboratories” and felt that “The most effective customers and sponsors set only ‘the what’ (research objectives to be met) and allow the laboratories to determine ‘the how’ (specific research projects and procedures).”  Frequent personnel exchange programs between the FFRDC and its sponsor are also suggested. 

This and the experience of successful FFRDCs suggests that the proposed FFRDC be closely linked to relevant ongoing efforts in NIST, especially CAISI, with frequent exchanges of information and even personnel. At the same time, the proposed FFRDC should have the freedom to explore very challenging research questions that lie at the heart of its mission. 

As an example of the relationship between agencies and associated FFRDCs, the Jet Propulsion Laboratory supports many of NASA’s priorities, addressing long-term goals such as understanding how life emerged on earth, along with more immediate goals such as catalyzing economic growth and contributing to national security. Caltech manages operations of JPL. In general, NASA sets strategic goals, and JPL aligns its long-term quests with these goals. NASA may solicit proposals and JPL may compete to lead or participate in appropriate missions. JPL may also propose missions to NASA. As an example, in 2011 the National Academies recommended that NASA begin a mission to return samples from Mars. NASA decided to launch a new Mars rover mission. NASA then tasked JPL to build and manage operations of Perseverance, to accomplish this mission. 

On a less positive note, after concerns about the Department of Energy’s (DOE) management of FFRDCs, DOE shifted from a “transactional model to a systems-based approach” offering greater oversight, but also leading to concerns of loss of flexibility and micromanagement. Concerns have also previously been raised about the level of transparency and assessment of alternatives when agencies renew FFRDC contracts, as well as mission creep of existing FFRDCs 

Existing FFRDCs Relevant to AI Work

One of the most important criteria for establishing a new FFRDC is to demonstrate that this will fill a need that cannot be filled by existing entities. Many current FFRDCs are conducting work on AI, but this work does not adequately address the needs of Commerce, especially in light of the requirements of the AI Action Plan. For example, the Software Engineering Institute (SEI) run by CMU has deep expertise in the development of AI systems, along with software development and acquisition. However, their mission is to  “execute applied research to drive systemic transition of new capabilities for the DoD.”  Its AI work focuses on defense related capabilities, and not on the comprehensive evaluation of frontier models needed by NIST. 

NIST does support the National Cybersecurity FFRDC (NCF) operated by MITRE. This unit focuses on security needs, not on general model evaluation (although it will be important to clearly delineate the scopes of a new Commerce FFRDC and the NCF). Other FFRDCs, such as Los Alamos or Lawrence Berkeley have significant AI efforts aimed at using AI to enhance scientific discovery. Industry AI labs address some of the questions central to the proposed FFRDC, but it is important that the government have access to deep technical expertise that is able to act in the public interest.

Establishing a New FFRDC

A precedent on the establishment of FFRDCs comes from the Department of Homeland Security (DHS). Under Section 305 of the Homeland Security Act of 2002, DHS was authorized to establish one or more FFRDCs to provide independent technical analysis and systems engineering for critical homeland security missions. In April 2004, DHS created its first FFRDC, the Homeland Security Institute. Four years later, on April 3, 2008, it issued a notice of intent to establish a successor organization, the Homeland Security Systems Engineering and Development Institute (HSSEDI), and in 2009 selected the MITRE Corporation to operate it. HSSEDI—along with DHS’s other FFRDC, the Homeland Security Operational Analysis Center—is overseen by the Department’s FFRDC Program Management Office. This case illustrates both a procedural pathway (statutory authorization, public notice, operator selection) and the typical timeline for standing up such an entity: roughly 12–18 months from notice of intent to full operation. Similarly, the National Cybersecurity FFRDC had its first notice of intent filed April 22, 2013, with the final contract to operate the FFRDC awarded to MITRE on September 24, 2014, about 17 months later. 

Appendix D. Requirements for Establishing an FFRDC

Establishing a new FFRDC requires the sponsoring organization (Commerce in this case) to satisfy the criteria laid out in the Federal Acquisition Regulations (48 CFR 35.017-2) for creating a new FFRDC.

These include:

• Requirement: Existing alternative sources for satisfying agency requirements cannot effectively meet the special research or development needs.
  • Meeting the Requirement: The special research or development need for improved understanding, measurement and reliability of AI models is clearly highlighted by the Trump Administration’s AI Action plan, and will only increase with more capable AI systems. As detailed in Appendix C, existing FFRDCs do not focus on problems central to Commerce’s mission, including the promotion of AI exports through model understanding and evaluation, model measurement to promote international standards, and identifying security issues central to export controls. Some work on this is done in industry and universities, but as noted, this is not comprehensive or sufficient to address Commerce’s mandate and the goals of the AI Action Plan.
• Requirement: There is sufficient Government expertise available to adequately and objectively evaluate the work to be performed by the FFRDC.
  • Meeting the Requirement: CAISI would serve as a source of expertise within the government that can evaluate the work performed by the FFRDC.
• Requirement: A reasonable continuity in the level of support to the FFRDC is maintained, consistent with the agency’s need for the FFRDC and the terms of the sponsoring agreement.
  • Meeting the Requirement: Satisfying this requirement may require ongoing support from Congressional appropriations committees, depending on the level of support needed.
• Requirement: The FFRDC is operated, managed, or administered by an autonomous organization or as an identifiably separate operating unit of a parent organization, and is required to operate in the public interest, free from organizational conflict of interest, and to disclose its affairs (as an FFRDC) to the primary sponsor.
  • Meeting the Requirement: The DOC and NIST must identify the appropriate contractor to run the FFRDC. There are many non-profits and universities with relevant expertise, including non-profits devoted to AI measurement. 

The establishment of an FFRDC must follow the notification process laid out in 48 CFR 5.205(b). The sponsoring agency must transmit at least three notices over a 90-day period to the GPE (Governmentwide point of entry) and the Federal Register, indicating the agency’s intention to sponsor an FFRDC, and its scope and nature, requesting comments. This plan must be reviewed by the Office of Federal Procurement Policy (OFPP) within the White House Office of Management and Budget (OMB). 

A sponsoring agreement (described in 48 CFR 35.017-1) must be generated by Commerce for the new FFRDC. This agreement is required by regulations (48 CFR 35.017-1(e)) to last for no more than five years, but may be renewed. It outlines conditions for awarding contracts and methods of ensuring independence and integrity of the FFRDC. FFRDCs initiate work at the request of federal entities, which would then be approved by appropriate units within DOC. The proposed FFRDC should align its mission closely with Commerce and NIST, obtaining contracts from these sponsoring agencies that will determine its priorities. The FFRDC would hire top tier researchers who can both execute this research and provide bottom-up identification of important new research topics.

Washington, D.C. – March 7, 2025 – The Federation of American Scientists (FAS) is pleased to welcome Dr. Yong-Bee Lim as the new Associate Director of Global Risk. In this role, Dr. Lim will help develop, organize, and implement FAS’s growing contribution in the area of catastrophic risk prevention, including on core areas of nuclear weapons, AI and national security, space and other emerging technologies.  

“The role of informed, credible and engaging organizations in support of sound public policy is more important than ever” said Jon Wolfsthal, FAS Director of Global Risk. “Yong-Bee embodies what it means to be an effective policy entrepreneur and to make meaningful contributions to US and global security. We are really excited that he is now part of the FAS team.”

Dr. Lim is a recognized expert in biosecurity, emerging technologies, and converging risks through his former roles as Deputy Director of both the the Converging Risks Lab and the Janne E. Nolan Center at the Council on Strategic Risks, his research and leadership roles in academia, and through his work at key agencies (DoD, HHS/ASPR, and DoE) in the United States. He completed his Ph.D. in Biodefense from George Mason University’s Biodefense program, where he conducted critical work on understanding the safety, security, and cultural dimensions of the U.S.-based Do-It-Yourself Biology (DIYBio) community. His recent accolades include being in the inaugural fellowship class of the Editorial Fellows program at the Bulletin of the Atomic Scientists and his selection and involvement in the Emerging Leaders in Biosecurity Initiative hosted by the Johns Hopkins Center for Health Security. 

“As emerging capabilities change the very contours of safety, security, and innovation, FAS has positioned itself to both highlight the global opportunities we must seize and address the global risks we must mitigate,” Lim said. “Founded in 1945, FAS continues to display thought leadership and impact because it has not forgotten its core mission: to ensure that scientific and technical expertise continue to have a seat at the policymaking table. I am honored to be part of an organization with a legacy and mission like FAS.”

ABOUT FAS

The Federation of American Scientists (FAS) works to advance progress on a broad suite of issues where science, technology, and innovation policy can deliver transformative impact, and seeks to ensure that scientific and technical expertise have a seat at the policymaking table. Established in 1945 by scientists in response to the atomic bomb, FAS continues to bring scientific rigor and analysis to address contemporary challenges. More information about FAS work at fas.org and Global Risk, here.

The Ranking Member of the Senate Committee on Health, Education, Labor, & Pensions (HELP) recently requested information regarding AI in our healthcare system, in the classroom, and in the workplace. The Federation of American Scientists was happy to provide feedback on the Committee’s questions. Targeted investments and a clear-eyed vision of the future of AI in these domains will allow the U.S. to reap more of the potential benefits of AI while preventing some of the costs.

This response provides recommendations on leveraging AI to improve education, healthcare, and the future of work. Key points include:

• In education, the federal government should fund more AI research and establish initiatives like the National Center for Advanced Development in Education. 
• For healthcare, Congress should update regulations to enable safe innovation and establish AI Centers of Excellence, starting with maternal health. These can coordinate data sharing and develop AI tools to tackle disparities.
• Regarding labor, AI creates opportunities for job growth and workforce transition. The NIST AI Risk Framework enables responsible adoption. Industry can self-regulate using shared standards to ensure worker protections.

Overall, with thoughtful oversight and human-centric design, AI promises immense benefits across these sectors. But responsible governance is crucial, as is inclusive development and ongoing risk assessment. By bringing together stakeholders, the U.S. can lead in advancing ethical, high-impact applications of AI.

Education

The Federation of American Scientists (FAS) co-leads the Alliance for Learning Innovation (ALI), a coalition of cross-sector organizations seeking to build a stronger, more competitive research and development (R&D) infrastructure in U.S. education. As was noted in the ALI Coalition’s response to White House Office of Science & Technology Policy’s “Request for Information: National Priorities for Artificial Intelligence,” FAS sees great promise and opportunity for artificial intelligence to improve education, equity, economic opportunity, and national security. In order to realize this opportunity and mitigate risks, we must ensure that the U.S. has a robust, inclusive, and updated education R&D ecosystem that crosscuts federal agencies.

What Should The Federal Role Be In Supporting AI In Education?

Research And Development

The U.S. government should prioritize funding and supporting R&D in the field of AI to ensure that the U.S. is on the cutting edge of this technology. One strong existing federal example are the AI Institutes supported by the National Science Foundation (NSF) and the U.S. Department of Education (ED). Earlier this year, NSF and the Institute of Education Sciences (IES) established the AI Institute for Exceptional Children, which capitalizes on the latest AI research to serve children with speech and language pathology needs. Communities would benefit from additional AI Institutes that meet the moment and deliver solutions for today’s teaching and learning challenges.

Expanding Research Grant Programs

Federal agencies, and specifically IES, should build upon the training programs it has for broadening participation and create specific research grant programs for minority-serving institutions with an emphasis on AI research. While the IES Pathways program has had success in diversifying education research training programs, more needs to be done at the predoctoral and postdoctoral level.

National Center For Advanced Development In Education

Another key opportunity to support transformational AI research and development in the United States is to establish a National Center for Advanced Development in Education (NCADE). Modeled after the Defense Advanced Research Projects Agency (DARPA), NCADE would support large-scale, innovative projects that require a more nimble and responsive program management approach than currently in place. The Center would focus on breakthrough technologies, new pedagogical approaches, innovative learning models, and more efficient, reliable, and valid forms of assessments. By creating NCADE, Congress can seed the development and use of artificial intelligence to support teaching, personalize learning, support ELL students, and analyze speech and reading.

How Can We Ensure That AI Systems Are Designed, Developed, And Deployed In A Manner That Protects People’s Rights And Safety?

First and foremost, we need to ensure that underserved communities, minors, individuals with disabilities and the civil rights organizations that support them are at the table throughout the design process for AI tools and products. In particular, we need to ensure that research is led and driven locally and by those who are closest to the challenges, namely educators, parents, students, and local and state leaders.

When thoughtfully and inclusively designed, AI has the potential to enhance equity by providing more personalized learning for students and by supporting educators to address the individual and diverse needs in their classrooms. For example, AI could be utilized in teacher preparation programs to ensure that educators have access to more diverse experiences during their pre-service experiences. AI can also provide benefits and services to students and families who currently do not have access to those resources due to a lack of human capital.

Labor

What Role Will AI Play In Creating New Jobs?

AI can serve as a powerful tool for workforce systems, employers, and employees alike in order to drive job creation and upskilling. For instance, investment in large language learning models that scrape and synthesize real-time labor market information (LMI) can be used to better inform employers and industry consortia about pervasive skills gaps. Currently, most advanced real-time LMI products exist behind paywalls, but Congress should consider investing in the power of this information as a public good to forge a more competitive labor market.

The wide-scale commercialization of AI/ML-based products and services will also create new types of jobs and occupations for workers. Contrary to popular belief, many industries that face some level of automation will still require trained employees to pivot to emerging needs in a way that offsets the obsoletion of other roles. Through place-based partnerships between employers and training institutions (e.g., community colleges, work-based learning programs, etc.), localities can reinvest in their workers to provide transition opportunities and close labor market gaps.

What Role Will AI Standards Play In Regulatory And Self-Regulatory Efforts?

AI standards will serve as a crucial foundation as the U.S. government and industries navigate AI’s impacts on the workforce. The NIST AI Risk Management Framework provides a methodology for organizations to assess and mitigate risks across the AI lifecycle. This could enable more responsible automation in HR contexts—for example, helping ensure bias mitigation in algorithmic hiring tools. On the policy side, lawmakers drafting regulations around AI and employment will likely reference and even codify elements of the Framework.

On the industry side, responsible technology leaders are already using the NIST AI RMF for self-regulation. By proactively auditing and mitigating risks in internal AI systems, companies can build public trust and reduce the need for excessive government intervention. Though policymakers still have an oversight role, widespread self-regulation using shared frameworks is at this point the most efficient path for safe and responsible AI across the labor market.

Healthcare

What Updates To The Regulatory Frameworks For Drugs And Biologics Should Congress Consider To Facilitate Innovation In AI Applications?

Congress has an opportunity to update regulations to enable responsible innovation and oversight for AI applications in biopharma. For example, Congress could consider expanding the FDA’s mandate and capacity to require upfront risk assessments before deployment of particularly high-risk or dual-use bio-AI systems. This approach is currently used by DARPA for some autonomous and biological technologies.

Additionally, thoughtful reporting requirements could be instituted for entities developing advanced bio-AI models above a certain capability threshold. This transparency would allow for monitoring of dual-use risks while avoiding overregulation of basic research. 

How Can The FDA Improve The Use Of AI In Medical Devices? 

Ensuring That Analysis Of Subpopulation Performance Is A Key Component Of The Review Process For AI Tools

Analyzing data on the subpopulation performance of medical devices should be one key component of any comprehensive effort to advance equity in medical innovation. We appreciate the recommendations in the GOP HELP white paper asking developers to document the performance of their devices on various subpopulations when considering updates and modifications. It will be essential to assess subpopulation performance to mitigate harms that may otherwise arise—especially if an argument for equity is made for a certain product. 

Clarifying The Role Of Real-World Evidence In Approvals

Locating concerns regarding performance in subpopulations and within different medical environments will most likely involve the collection of real-world evidence regarding the performance of these tools in the wild. The role of real-world evidence in the regulatory approval process for market surveillance and updates should be defined more clearly in this guidance. 

How Can AI Be Best Adopted To Not Inappropriately Deny Patients Care?

AI Centers of Excellence could be established to develop demonstration AI tools for specific care populations and care environments. For example, FAS has published a Day One Memo proposing an AI Center of Excellence for Maternal Health to bring together data sources, then analyze, diagnose, and address maternal health disparities, all while demonstrating trustworthy and responsible AI principles. The benefits of AI Centers of Excellence are two-fold: they provide an opportunity for coordination across the federal government, and they can evaluate existing datasets to establish high-priority, high-impact applications of AI-enabled research for improving clinical care guidelines and tools for healthcare providers. 

The AI Center of Excellence model demonstrates the power of coordinating and thoughtfully applying AI tools across disparate federal data sources to address urgent public health needs. Similar centers could be established to tackle other complex challenges at the intersection of health, environmental, socioeconomic, and demographic factors. For example, an AI Center focused on childhood asthma could integrate housing data, EPA air quality data, Medicaid records, and school absenteeism data to understand and predict asthma triggers.

Harnessing the Promise of AI

Artificial intelligence holds tremendous potential to transform education, healthcare, and work for the better. But realizing these benefits in an equitable, ethical way requires proactive collaboration between policymakers, researchers, civil society groups, and industry.

The recommendations outlined here aim to strike a balance—enabling innovation and growth while centering human needs and mitigating risks. This requires robust funding for R&D, modernized regulations, voluntary standards, and inclusive design principles. Ongoing oversight and impact evaluation will be key, as will coordination across agencies and stakeholders.

The Department of Defense needs to take several steps in order to avoid “strategic surprise” by an adversary over the coming decade, according to a new study from the Defense Science Board, a Pentagon advisory body.

Among those steps, “Counterintelligence must be enhanced with urgency.” See DSB Summer Study Report on Strategic Surprise, July 2015.

The Board called for “continuous monitoring” of cleared personnel who have access to particularly sensitive information. “The use of big data analytics could allow DoD to track anomalies in the behaviors of cleared personnel in order to thwart the insider threat.”

“Continuous monitoring” involves constant surveillance of an employee’s activities (especially online activities), and it goes beyond the “continuous evaluation” of potentially derogatory information that is an emerging part of the current insider threat program.

“Insider actions often generate suspicious indicators in multiple and organizationally separate domains–physical, personnel, and cyber security. The use of big data and creative analytics can be carefully tuned to the style and workflow of the particular organization and can help to audit for integrity as well as individual user legitimacy,” the DSB report said.

The DSB report broadly addressed opportunities and vulnerabilities in eight domains: countering nuclear proliferation; ballistic and cruise missile defense; space security; undersea warfare; cyber (“The Department should treat cyber as a military capability of the highest priority”); communications and positioning, navigation, and timing (PNT); counterintelligence; and logistics resilience.

To an outside reader, the DSB report seems one-dimensional and oddly disconnected from current realities. It does not consider whether the pursuit of any of its recommended courses of actions could have unintended consequences. It does not inquire whether there are high-level national policies that would make strategic surprise more or less likely. And it does not acknowledge the recurring failure of the budget process to produce a defense budget that is responsive to national requirements in a timely fashion.