Are Consumer GPUs a Problem for US Export Controls?
This report analyzes the potential impact of high-end consumer GPUs on the efficacy of US export controls on AI chips. It studies three stockpiling scenarios and what AI capabilities those may enable, and makes recommendations for policymakers.
Location Verification for AI Chips
Adding location verification features to AI chips could unlock new governance mechanisms for regulators, help enforce existing and future export controls by deterring and catching smuggling attempts, and enable post-sale verification of chip locations. This paper is meant to serve as an initial introduction to location verification use-cases for AI chips with comparison of different methods.
Spreadsheets vs. Smugglers: Modernizing the BIS for an Era of Tech Rivalry
This blog post by Erich Grunewald (IAPS) and Samuel Hammond (the Foundation for American Innovation) argues that Congress should increase the funding of the Bureau of Industry and Security.
Topics for Track IIs: What Can Be Discussed in Dialogues About Advanced AI Risks Without Leaking Sensitive Information?
This issue brief suggests agenda items for dialogues about advanced AI risks that minimize risk of leaking sensitive information.
Highlights for Responsible AI from the Biden Administration's FY2025 Budget Proposal
This issue brief analyzes key AI-related allocations from the Biden FY2025 Presidential Budget in terms of their potential impact on the responsible development of advanced AI.
Responsible Reporting for Frontier AI Development
Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems.
AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies
A systematic search for potential case studies relevant to advanced AI regulation in the United States, looking at all federal agencies for factors such as level of expertise, use of risk assessment, and analysis of uncertain phenomena.
Responsible Scaling: Comparing Government Guidance and Company Policy
This issue brief evaluates the original example of a Responsible Scaling Policy (RSP) – that of Anthropic – against guidance on responsible capability scaling from the UK Department for Science, Innovation and Technology (DSIT).
Response to the NIST RFI on Auditing, Evaluating, and Red-Teaming AI Systems
IAPS’s response to a NIST RFI, outlining specific guidelines and practices that could help AI actors better manage and mitigate risks from AI systems, particularly from dual-use foundation models.
Secure, Governable Chips
Today, the Center for a New American Security (CNAS), in collaboration with the Institute for AI Policy and Strategy, has released a new report, Secure, Governable Chips, by Onni Aarne, Tim Fist, and Caleb Withers.
The report introduces the concept of “on-chip governance,” detailing how security features on AI chips could help mitigate national security risks from the development of broadly capable dual-use AI systems, while protecting user privacy.
Catching Bugs: The Federal Select Agent Program and Lessons for AI Regulation
This paper examines the Federal Select Agent Program, the linchpin of US biosecurity regulations. It then draws out lessons for AI regulation regarding licensing, regulatory expertise, and the merits of “risk-based” vs. “list-based” systems.
Introduction to AI Chip Making in China
This primer introduces the topic of Chinese AI chip making, relevant to understanding and forecasting China's progress in producing AI chips indigenously.
Safeguarding the Safeguards: How Best to Promote Alignment in the Public Interest
With this paper, we aim to help actors who support alignment efforts to make these efforts as effective as possible, and to avoid potential adverse effects.
Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
This paper discusses how external scrutiny (such as third-party auditing, red-teaming, and researcher access) can bring public accountability to bear on decisions regarding the development and deployment of frontier AI models.
Preventing AI Chip Smuggling to China
We link to a working paper which was led by Tim Fist of the Center for a New American Security, and coauthored with IAPS researcher Erich Grunewald. It builds on IAPS's earlier report on AI chip smuggling into China.
International AI Safety Dialogues: Benefits, Risks, and Best Practices
Events that bring together international stakeholders to discuss AI safety are a promising way to reduce AI risks. This report recommends ways to make these events a success.
Managing AI Risks in an Era of Rapid Progress
This paper discusses risks from future AI systems and proposes priorities for AI R&D and governance. Its many authors include an IAPS researcher, Turing Prize winners, and a Nobel Memorial Prize winner.
Adapting Cybersecurity Frameworks to Manage Frontier AI Risks: a Defense-in-Depth Approach
The complex and evolving threat landscape of frontier AI development requires a multi-layered approach to risk management (“defense-in-depth”). By reviewing cybersecurity and AI frameworks, we outline three approaches that can help identify gaps in the management of AI-related risks.
How Expertise in AI hardware Can Help with AI Governance
This article was written for the organization 80,000 Hours by an IAPS researcher. It discusses why and how it may be valuable to build expertise in AI hardware and use that expertise to reduce risks and improve governance decisions.
AI Chip Smuggling into China: Potential Paths, Quantities, and Countermeasures
This report examines the prospect of large-scale smuggling of AI chips into China and proposes six interventions for mitigating that.