Priorities for Frontier AI Policy
A policy agenda for frontier AI, proposing lifecycle oversight including internal models, American AI leadership measures, and national resilience against AI-enabled cyber, bio and distillation threats.
Research / Sources
IAPS
Policy research on advanced AI security and governance. www.iaps.ai
Research Report (25)Compute governance (23)Frontier Security (22)International governance & China (18)Policy and Standards (18)Erich Grunewald (16)Jam Kraprayoon (14)Link Post (14)Oliver Guest (11)Linkpost (10)Joe O'Brien (9)Chris Covino (8)Issue Brief (8)Oscar Delaney (7)Bill Anderson-Samways (6)Responses to RFIs/RFCs (6)Matthew Mittelsteadt (5)Renan Araujo (5)Theo Bearman (5)Blog Post (4)Cara Labrador (4)Shaun Ee (4)Asher Brass (3)International Strategy (3)Onni Aarne (3)Brianna Rosen (2)Clarissa Koh (2)Hamish Low (2)Karson Elmgren (2)Sambhav Maheshwari (2)Sarah Godek (2)Article (1)Cassia King (1)Commentary (1)Dave Banerjee (1)Jan Wehner (1)Request for Information (RFI) Response (1)Sumaya Nur Adan (1)
Focus All
Topic Regulation & law
Date Any time
Remote Access Security Act (RASA)
Proposes closing a gap in US export controls so advanced AI chips rented through cloud services remain covered, and recommends broadening the Senate bill's definition of cloud infrastructure.
IAPS Reacts to the White House Executive Order on AI Innovation and Security
IAPS reacts to a White House executive order on frontier AI security, assessing its cyber-defense provisions, voluntary developer collaboration, and unresolved implementation questions.
AI Distillation Attacks: Executive and Congressional Action Can Go Further
A policy memo recommending follow-up U.S. executive and congressional measures against AI distillation attacks, linking model theft to semiconductor export controls and national security.
Risk Reporting for Developers’ Internal AI Model Use
Proposes a harmonized standard for frontier developers' internal-use risk reports, aligned with California SB 53, New York RAISE Act, and the EU GPAI Code of Practice.
AI Decision Support Systems: A Neglected Source of Military AI Risk
A policy memo arguing that AI decision-support systems in military targeting fall outside DoD lethal-autonomy rules, and recommending safeguards for reliability and meaningful human oversight.
AI Distillation Attacks: The Case for Targeted Government Intervention
A policy memo arguing for government action against AI distillation attacks, including Entity List additions, sanctions, and a NIST-led defensive framework for frontier model capability extraction.
Issue Brief: The Stop Stealing Our Chips Act
Explains the Stop Stealing Our Chips Act, a bill that would create a whistleblower reward program to strengthen US enforcement of AI chip export controls against diversion to China.
Policy Options for Preserving Chain of Thought Monitorability
A policy report arguing that competitive pressure could push AI developers away from human-readable chain-of-thought reasoning, undermining monitoring, and proposing governance options to preserve CoT monitorability.
A National Center for Advanced AI Reliability and Security
A policy memo proposing a national CAISI+ center in the US Commerce Department for frontier model evaluations of catastrophic risks, emergency assessment, and AI security research, with budget and infrastructure recommendations.
A Whistleblower Incentive Program to Enforce U.S. Export Controls
Proposes a whistleblower incentive program modeled on the SEC's to improve enforcement of U.S. export controls on AI chips and other compute-related technology.
Comment on the Bureau of Industry and Security’s Framework for Artificial Intelligence Diffusion
A policy comment to the US Bureau of Industry and Security recommending how to steer global AI compute and chip diffusion through export controls to preserve American AI leadership.
Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence
Compares US and Chinese policy approaches to governing general-purpose AI, identifying divergences in regulation focus, principles, and international governance, and their implications for cooperation on AI safety and security.
Response to BIS RFC on Establishment of Reporting Requirements for the Development of Advanced Artificial Intelligence Models and Computing Clusters
IAPS's response to a US BIS proposed rule recommends expanding reporting of frontier AI models and compute clusters to third-party evaluators, civil society, and public bodies, with ad hoc reporting and information sharing.
Assuring Growth: Making the UK a Global Leader in AI Assurance Technology
A UK policy memo recommending government market-shaping to grow the AI assurance technology industry, covering AI alignment, security and auditing tools, and the regulatory and demand uncertainties that hold it back.
An Early Warning System For AI-Powered Threats To National Security And Public Safety
A policy memo proposing Congress create an early warning system for novel AI-enabled threats, with BIS as a clearinghouse for dual-use capability evaluation reports from frontier developers.
Coordinated Disclosure of Dual-Use Capabilities: An Early Warning System for Advanced AI
Proposes a Coordinated Disclosure of Dual-Use Capabilities process, with a government clearinghouse receiving reports of dangerous AI capabilities so defenders get early warning and time to respond.
Topics for Track IIs: What Can Be Discussed in Dialogues About Advanced AI Risks Without Leaking Sensitive Information?
Suggests agenda topics for US-China track II dialogues on advanced AI risks, such as developer best practices, national and international governance, that avoid leaking proliferation-sensitive information.
Responsible Reporting for Frontier AI Development
Proposes responsible reporting of safety-critical information from frontier AI developers to government, industry and civil society, with voluntary and regulatory mechanisms for implementing it.
AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies
A systematic search of US federal agencies identifies regulatory case studies, using measures like expertise, risk assessment and enforcement, that could inform how advanced AI should be regulated.
Catching Bugs: The Federal Select Agent Program and Lessons for AI Regulation
Examines the US Federal Select Agent Program as a biosecurity precedent and draws lessons for AI regulation, covering development-phase licensing, regulatory expertise, and risk-based versus list-based approaches.
Managing AI Risks in an Era of Rapid Progress
A consensus paper from many researchers outlining risks from advanced AI, including loss of human control, and proposing urgent priorities for AI R&D and governance.