Notice of Request for Information; Regulatory Reform on Artificial Intelligence

DATES:

Interested persons are invited to submit comments on or before 11:59 p.m. (ET) October 27, 2025.

Instructions

Response to this RFI is voluntary. Please note that all submissions received in response to this notice may be posted on https://www.regulations.gov/ or otherwise released in their entirety.

Do not include in your submissions any copyrighted material; information of a confidential nature, such as personal or proprietary information; or any information you would not like to be made publicly available.

OSTP will not respond to individual submissions. A response to this RFI will not be viewed as a binding commitment to develop or pursue the project or ideas discussed. This RFI is not accepting applications for financial assistance or financial incentives.

Responses containing references, studies, research, and other empirical data that are not widely published should include copies of or electronic links to the referenced materials. Responses from minors, or responses containing profanity, vulgarity, threats, or other inappropriate language or content will not be considered.

Comments submitted in response to this notice are subject to the Freedom of Information Act (FOIA). Please note that the United States Government will not pay for response preparation, or for the use of any information contained in a response.

SUPPLEMENTARY INFORMATION:

Artificial intelligence (AI) encompasses a broad range of computational techniques and systems that perform tasks traditionally requiring human judgment, such as perception, prediction, optimization, decision support, and autonomous operation. AI has applications across nearly every sector of the economy and public life, including healthcare, finance, transportation, manufacturing, education, agriculture, and national security. AI adoption is expected to yield significant benefits, including greater efficiency, improved safety and reliability, expanded access to services, and enhanced economic competitiveness. Realizing these benefits depends on continued AI innovation and public adoption.

On July 23, 2025, the White House issued America's AI Action Plan to achieve global dominance in AI. The AI Action Plan directed OSTP to "launch a Request for Information [RFI] from businesses and the public at large about current Federal regulations that hinder AI innovation or adoption, and work with relevant Federal agencies to take appropriate action." This RFI advances that directive by focusing on identifying the regulatory and procedural barriers that unnecessarily slow safe, beneficial AI deployment.

The realization of the benefits from AI applications cannot be done through complete de-regulation, but require policy frameworks, both regulatory and non-regulatory. Suitable policy frameworks enable innovation while safeguarding the public interest. This is critical to foster public trust in AI technologies, leading to broader deployment and faster adoption. Such policy frameworks may include statutory and regulatory requirements, technical standards, guidance documents, voluntary frameworks, and other instruments.

Most existing Federal regulatory regimes and policy mechanisms were developed before the rise of modern AI technologies. As a result, they often rest on assumptions about human-operated systems that are not appropriate for AI-enabled or AI-augmented systems. These assumptions include, but are not limited to:

• Decision-Making and Explainability-Decisions are made, documented, and explained, in ways where the processes and rationale are traceable to a human actor.
• Liability and Accountability-Allocation of legal responsibility and remedial frameworks rests with human actors or clearly identifiable organizational decision points.
• Human Oversight and Intervention-Prescriptive requirements for human oversight, review, intervention, or continuous supervision in operational processes.
• Data Practices-Data collection, retention, provenance, sharing, and permitted uses cases that do not account for the scale, reuse, or training dynamics characteristic of AI systems.
• Testing, Validation, and Certification-Approaches to testing, approval, and post-market oversight designed for static products or human-delivered services, rather than adaptive or continuously learning systems.

These assumptions manifest differently across sectors and their AI applications. For example, in healthcare, regulations for medical devices, telehealth, and patient privacy were designed around human clinicians and discrete medical device updates. It may create challenges to apply the same policy framework for overseeing continuously updating AI diagnostic tools and ensuring explainable clinical recommendations. In transportation, safety standards and certification processes are built for human drivers and operators. Similarly, this may raise questions around operational design domain limits and incident investigation for autonomous vehicles, unmanned systems, and other AI-enabled transportation technologies.

When applied to AI-enabled or AI-augmented systems, policy frameworks that assume human-operated systems or fail to account for technological progress hinder the development, deployment, and adoption for AI across sectors. These barriers generally fall into five categories: (1) regulatory mismatch, where existing rules no longer aligns with AI capabilities, (2) structural incompatibility, where legal or operational requirements are fundamentally unsuitable for AI systems, (3) lack of regulatory clarity, where insufficient guidance and rules that plausibly cover AI systems delays adoption, increases compliance costs, and slows innovation, (4) direct hindrance, where regulations directly target AI development, deployment, and adoption, and (5) organizational factors, which influence how available policy frameworks and administrative tools are and are not used.

1. Regulatory Mismatches-Existing requirements are based on human-centered assumptions ( e.g., mandatory human supervision or documentation practices) that do not align with AI capabilities or operational models. In many cases, the underlying goals can still be met if the regulations are applied flexibly. Administrative tools such as waivers, exemptions, pilot programs, conditional approvals, or time-limited experimental authorities can enable lawful deployment while preserving regulatory objectives.

2. Structural Incompatibility-Certain statutory or regulatory frameworks are not just mismatched, but structurally unable to accommodate particular AI applications because key legal constructs or procedural prerequisites assume human actors ( e.g., statutory human decisionmakers, prohibitions on automated data practices). Where no administrative flexibility exists, meaningful AI adoption may require legislative change or comprehensive regulatory revision.

3. Lack of Regulatory Clarity-In some circumstances, existing laws plausibly cover AI activities, but insufficient interpretive guidance, standards, or objective criteria leaves compliance, risk management, and enforcement uncertain. This ambiguity can delay adoption, increase compliance costs, and hinder innovation. Remedies may include authoritative guidance, interpretive rules, sector-specific standards, or clarity on enforcement priorities.

4. Direct Hindrance-There are also a number of regulations that directly target AI and are a major hindrance to AI development, deployment, and adoption. For example, guidance that prevent Federal workers from using AI on their work computers for reasonable use cases fall under this category.

5. Organizational Factors-AI adoption may also be influenced by organizational factors, such as gaps in workforce readiness, institutional capacity, or cultural acceptance. While these are not barriers embedded in Federal governance mechanisms, they nonetheless influence how available policy frameworks and administrative tools are (or are not) used. For example, agencies may have the administrative flexibilities to overcome regulatory mismatches, but not fully utilize them due to a lack of awareness, hindering the pace and scope of AI adoption.

This RFI seeks to identify Federal regulations that hinder AI development, deployment, or adoption, particularly due to rules established before current AI capabilities were anticipated. OSTP is especially interested in regulations that, while serving important purposes, contain requirements or assumptions incompatible with how AI systems function or could function. Respondents are encouraged to identify regulations across all sectors where the underlying assumptions, technical requirements, or compliance frameworks may create unnecessary barriers to beneficial AI applications, even if the core policy objectives remain valid.

Specifically, OSTP invite responses to one or more of the following questions:

(i) What AI activities, innovations, or deployments are currently being inhibited, delayed, or otherwise constrained due to Federal statues, regulations, or policies? Please describe the specific barrier and the AI capability or application that would be enabled if it was addressed. The barriers may directly hinder AI development or adoption, or indirectly hinder through incompatible policy frameworks.

(ii) What specific Federal statutes, regulations, or policies present barriers to AI development, deployment, or adoption in your sector? Please identify the relevant rules and authority with specificity, including a cite to the Code of Federal Regulations (CFR) or the U.S. Code (U.S.C.) where applicable.

(iii) Where existing policy frameworks are not appropriate for AI applications, what administrative tools ( e.g., waivers, exemptions, experimental authorities) are available, but underutilized? Please identify the administrative tools with specificity, citing the CFR or U.S.C. where applicable.

(iv) Where specific statutory or regulatory regimes are structurally incompatible with AI applications, what modifications would be necessary to enable lawful deployment while preserving regulatory objectives?

(v) Where barriers arise from a lack of clarity or interpretive guidance on how existing rules cover AI activities, what forms of clarification ( e.g., standards, guidance documents, interpretive rules) would be most effective?

(vi) Are there barriers that arise from organizational factors that impact how Federal statues, regulations, or policies are used or not used? How might Federal action appropriately address them?