Stephen Casper
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.
Stream overview
Our stream projects will generally focus on a few types of topics:
- Open-weight model safeguards: working to make AI systems with publicly downloadable weights more resistant to misuse, including by making them more tamper-resistant.
- Applied AI safeguards research: studying if and how AI safeguards are applied in the real world, and analyzing the connections that exist between company choices and downstream consequences.
- AI incidents: studying AI incidents and how they could be prevented.
- Technical rigor of AI policy: auditing laws for technical ambiguities, challenges, and loopholes.
- Miscellaneous AI governance research: guerrilla-style research to help policymakers make informed choices about emerging challenges in AI.
Boston
Mentors
Stephen "Cas" Casper is a computer scientist and an Assistant Professor of Public Policy at the Harvard Kennedy School and a Faculty Affiliate of the Harvard School of Engineering and Applied Sciences. Prior to joining Harvard, he completed his PhD at MIT and did a research residency with the UK AI Security Institute. He is a writer for the International AI Safety Report and a lead writer for the Singapore Consensus. His research has been recognized with a Hoopes Prize, an ML Safety Workshop best paper award, a BioSafeGenAI best paper runner-up, a GenLaw spotlight paper award, a TMLR outstanding paper finalist distinction, and a handful of mentions in news articles and newsletters. Find him on Google Scholar, Twitter (sorry), BlueSky, and LinkedIn.
Fellows we are looking for
Green flags include:
- Research tenacity: demonstrated ability to pursue self-directed work, make things happen through determination, teach oneself whatever skills are required for a project, and succeed even when not set up to succeed. As an example, I think it is a strong green flag when an undergrad pursues side projects in a self-directed manner rather than only pursuing projects under classes, internships, jobs, etc.
- Research taste: the AI research space is noisier than ever, and almost all AI research has little to no practical value. Putting impact over interest and designing projects around a specific plan for impact is the most important single skill needed for good AI work.
- Experience across most or all of the full project stack: ideating, planning, experimenting, writing, and publishing.
Project selection
I will work with MATS scholars to iteratively refine project ideas in whatever area our interests and skills overlap. Above all, project selection will hinge on having a clear (and good) theory of impact.