Introductory AI Policy Fellowship

Every semester, AISST runs an 8-week introductory reading group on the foundational policy and governance issues posed by the development of advanced AI systems. The fellowship aims to introduce students interested in AI policy and governance to risks from advanced AI.  It will discuss questions such as:

  • How much progress in AI should we expect over the next few years?

  • What are the risks associated with the misuse and misalignment of advanced AI systems?

  • How can regulators audit frontier AI systems for potentially dangerous capabilities?

  • How could novel hardware mechanisms prevent malicious or irresponsible actors from creating powerful AI models?

The fellowship meets weekly in small groups. Dinner is provided, and no additional work is required beyond meetings.

See here for the syllabus from last spring.

Applications for Spring 2025 have closed.

For those interested in the technical side of AI safety, we recommend applying to our introductory technical fellowship.  It is possible to participate in both programs.

  • We welcome applications from students with a range of previous experiences: previous work in AI or technology policy is helpful, but by no means required. In the past, fellows have had backgrounds in public policy, law, political science, as well as a variety of technical fields. This is not an exhaustive list, and we encourage those with other academic backgrounds to apply as well.

  • In the past, we’ve received over 100 applicants and accepted about half.

  • We attempt to accommodate people’s schedules when forming cohorts. Each cohort meets once a week for two hours, with dinner or lunch provided. We meet in our office in Harvard Square.

  • If you’re already familiar with the topics discussed in the syllabus, please email us at contact@haist.ai to discuss other ways of getting involved with AISST!

  • Yes. Previous fellows have included students in various SEAS/GSAS/MIT PhD programs, HLS, HKS, HBS, and Harvard College. Schedules permitting, we’ll form cohorts such that students are matched with others with similar backgrounds.