About the Book

The casebook originated in Paul Ohm’s AI Law course at Georgetown Law in Fall 2023.

The book has three co-authors:

• Margot Kaminski, Professor of Law, University of Colorado Law School
• Paul Ohm, Professor of Law, Georgetown University Law Center
• Andrew Selbst, Assistant Professor of Law, UCLA School of Law

We start from the premise that the field of AI Law is sufficiently well-developed to justify a fully fleshed out casebook that can be used in doctrinal, lecture-style classes, with a final exam at the end. We will emphasize primary sources of laws–cases, statutes, and rules–over theory and law review articles.

The book will emphasize the practice of AI Law, hoping this will be a useful reference text for practitioners and policymakers, in the U.S. and around the world.

We will also emphasize the technical details, giving students and professors who use this book primers that explain how the various technologies of AI (e.g. neural networks, decision trees, large language models) work. These primers will try to strike the balance between overwhelming with unnecessary detail and giving the material only an insufficiently superficial gloss.

We will be working on the book throughout 2024 and hope to see it on shelves before too long. If you want to learn more about the book, email any of the three authors.