Artificial Intelligence and the Law
The Initiative on Artificial Intelligence and the Law (IAIL) is a Harvard Law School initiative based at the Berkman Klein Center. Directed by Oren Bar-Gill and Cass Sunstein, the initiative focuses on new challenges and opportunities for the law created by the rise of artificial intelligence.
While AI can make enforcement and adjudication more effective, potentially reduce discrimination, and make the drafting of contracts, briefs, laws, regulations, and court opinions faster and less costly, it also has serious implications for broad societal issues such as consumer protection; investor protection; false advertising; privacy; misinformation; and discrimination and civil rights.
The initiative will sponsor and promote new work on these topics by both faculty and students, hold conferences and symposia, and issue preliminary reports on emerging topics. A book by Bar-Gill and Sunstein on algorithms and consumer protection, developed at Harvard Law School, is slated to be one of the early products.
Advisory Board
The IAIL is overseen by an advisory board consisting of law school faculty including Chris Bavitz, Yochai Benkler, John Coates, Benjamin Eidelson, Noah Feldman, Lawrence Lessig, Martha Minow, Ruth Okediji, Holger Spamann, David Wilkins, Crystal Yang, and Jonathan Zittrain.
Subscribe to the IAIL mailing list to receive updates about the initiative.
Prior Work
Victoria Angelova, Will Dobbie, Crystal Yang, Algorithmic Recommendations and Human Discretion (2022).
Oren Bar-Gill, Cass Sunstein, Inbal Talgam-Cohen, Algorithmic Harm in Consumer Markets, Harvard Public Law Working Paper No. 23-05 (2023).
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, Cass Sunstein, Discrimination in the Age of Algorithms, Journal of Legal Analysis (2018).
Lawrence Lessig, The First Amendment Does Not Protect Replicants, Harvard Public Law Working Paper No. 21-34 (2021).
Cass Sunstein, The use of algorithms in society, Review of Austrian Economics (2023).
Crystal Yang, Will Dobbie, Equal Protection Under Algorithms: A New Statistical and Legal Framework, Michigan Law Review (2020).