Peter Henderson
Assistant Professor
Princeton University
About Me
I’m an Assistant Professor at Princeton University with appointments in the Department of Computer Science and the School of Public and International Affairs. I’m also affiliated with the Center for Information Technology Policy (CITP), Princeton Language and Intelligence Initiative (PLI), Center for Statistics and Machine Learning (CSML), and Program in Law & Public Policy (PLAW) at Princeton.
I tackle research problems at the intersection of AI and law; I’m motivated by making sure that we have safe, capable AI systems that work for the good of the people. Some of my research has received coverage by TechCrunch, Science, New York Times, The Wall Street Journal, Bloomberg, and more.
I’m particularly interested in:
building semi-autonomous agents that can act and learn efficiently in complex environments for positive impact & public good;
Selected Work
- Developing sequential decision-making systems that can help with government efficiency and equity (Henderson et al., 2023; Henderson & Chugg et al., 2022)
- Creating foundation models capable of reasoning about the law, such that they can meaningfully improve access to justice (Guha et al., 2023; Henderson & Krass et al., 2022; Zheng et al., 2021)
- Improving and evaluating the efficiency & reusability of systems (Henderson et al., 2020; Romoff and Henderson et al., 2020; Henderson et al., 2018; Chugg et al., 2023)
improving safety of on-device, adaptable, and open AI systems;
Selected Work
- Evaluating and improving the robustness and alignment of AI systems, particularly systems that can be customized and fine-tuned (Qi et al., 2023; Henderson & Mitchell et al., 2023; Wei et al., 2024; Qi et al., 2024; Qi et al., 2024; Xie et al., 2024; Łucki et al., 2024; He et al., 2024)
- Strengthening the rigor of evaluation practices in machine learning (Narayanan et al., 2023; Henderson et al., 2018; Liang et al., 2023; Henderson et al., 2024; Henderson and Islam et al., 2018; Card et al., 2020)
leveraging law and evidence-based policy to steer toward positive outcomes for AI.
Selected Work
- Examining the interplay of intellectual property law and the development of AI (Henderson & Li et al., 2023; He et al., 2024; Wei et al., 2024)
- Finding a regulatory pathway that constrains the harms of AI, while maintaining open development of foundation models, decentralizing power, and preserving key rights like freedom of speech (Kapoor and Bommasani et al., 2024; Bateman et al., 2024; Longpre et al., 2024; Henderson, Hashimoto, & Lemley, 2023; Volokh, Lemley, and Henderson, 2023)
- Understanding the role of law in shaping responsible deployment of AI, particularly in government and the legal system (Henderson & Krass, 2023; Guha et al., 2022; Henderson et al., 2024)
- In the coming few years, I'm also interested in identifying AI policy that meaningfully helps decentralize power and address the labor impacts of AI.
Previously, I received a JD from Stanford Law School where I was advised by Dan Ho and Mariano-Florentino Cuéllar, as well as worked with many other wonderful colleagues. I also received my PhD in Computer Science (AI) at Stanford University where I am lucky enough to be advised by Dan Jurafsky.
Before that, I was advised by David Meger and Joelle Pineau for my M.Sc. at McGill University and the Montréal Institute for Learning Algorithms. I also spent time as a Software Engineer and Applied Scientist at Amazon AWS/Alexa, worked with Justice Cuéllar at the California Supreme Court, and more.