Policy and Governance
The MATS Policy & Governance Track supports research on how advanced AI is governed and how it should be governed. As AI capabilities accelerate, many of the hardest problems are no longer purely technical. They involve international coordination, governance under uncertainty, state capacity, regulatory design, and translating safety goals into real-world policies and institutions. Decisions made in the next 6–12 months will shape what labs build, what governments require, and what oversight looks like for years to come.
- Initial application: Submit 1-2 writing samples.
- Stage 2: Complete 1 assessment evaluating writing and analytical skills.
- Stream applications & follow-up: Apply to individual streams; follow-up includes interviews or additional assessments depending on the stream.
The track covers a wide range of research areas. Some streams focus on concrete governance mechanisms, including evaluations, standards, safeguards, monitoring systems, and enforcement structures. Others conduct policy and institutional analysis, such as comparative reviews of governance regimes, regulatory frameworks, and international coordination challenges. A third set engages broader questions, like how advanced AI may reshape global power dynamics or what governance approaches could meaningfully reduce catastrophic risk.
We are looking for fellows who can reason rigorously and write clearly about these topics. Strong candidates have come from policy, economics, law, political science, public administration, security studies, philosophy, computer science, forecasting, sociology, history, journalism, and science and technology studies, among other backgrounds.
Fellows are matched to mentors based on fit, and projects are scoped to produce concrete artifacts by program end e.g., policy memos, regulatory comments, technical specifications, comparative analyses, and peer-reviewed research. Target audiences span AISI staff, lab governance teams, regulators, standards bodies, and the broader research and policy communities shaping frontier AI governance.
In the face of disaster, I suspect the government will be forced to play insurer of last resort, whether for a particular lab, or society at large. (I'm not the only to suspect this – see e.g. here). Designed well, I believe a federal insurance backstop could internalize catastrophic negative externalities; designed poorly, it will simply be a subsidy for AI companies. I want to design the good version, so we have it ready.
I encourage people with mechanism design (a.k.a. reverse game theory) expertise to apply, but don't be deterred if you don't have this expertise.
Janet Egan will mentor scholars working on policy-relevant questions at the intersection of AI compute, geopolitics, and infrastructure. Potential projects include analyzing remote access to AI chips (e.g., via cloud providers in China), mapping and interpreting the global buildout of AI data centers and energy infrastructure, and developing politically informed strategies for US–China cooperation on AI risk. The mentee will lead their research project with weekly guidance, feedback, and optional career and policy insights.
Escalation risks from state perceptions of AI capability, AI-enabled targeting, AI-enabled decision manipulation, and the impact of AI integration into nuclear command and control.
This stream focuses on AI policy, especially technical governance topics. Tentative project options include: technical projects for verifying AI treaties, metascience for AI safety and governance, and proposals for tracking AI-caused job loss. Scholars can also propose their own projects.
Research papers (technical governance or ML) related to evaluating and mitigating dangerous AI capabilities, with a focus on what's actionable and relevant for AGI companies
Making society safe from AI doesn't just mean making safe AI: we're figuring out how to uplift human collective intelligence, manage a highly multiagent world, improve foresight and institutional competence, ideally learning how to make best positive use of frontier AI systems as we go. FLF has a small, sharp team of researchers with a wide network, and we're looking to nurture new and missing approaches to minimising large-scale risks while steering to a flourishing future.
Peter Henderson’s stream focuses on developing safe, aligned AI agents, with projects on scalable oversight rules informed by law and game theory, safe long-horizon exploration, and measuring “jagged” capability/safety frontiers. Scholars will join an independently driven, engineering-heavy research environment, collaborating with other MATS scholars and PhD students, with weekly 1:1s and active async mentorship.
International coordination to reduce frontier AI risks, with a focus on China and the West.
This stream will focus on impact-oriented technical AI governance research work, potentially including research on open-weight models, applied AI safeguards research, AI incidents, technically rigorous AI policy, etc.
I (Cas) work on a range of projects from technical safeguards to technical governance. This stream follows an academic collaboration model and will work will likely focus on technical topics in AI governance.