6 citations · 11 across the 5 of their papers we have counts for
5 papers
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…
Mutual Benefit: The Case for Sharing Autonomous Vehicle Data with the Public
David Goedicke, Natalie Chyi, Alexandra Bremers +3
Autonomous driving is a widely researched technology that is frequently tested on public roads. The data generated from these tests represent an essential competitive element for t…
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation
Tong Chen, Akari Asai, Niloofar Mireshghallah +6
Evaluating the degree of reproduction of copyright-protected content by language models (LMs) is of significant interest to the AI and legal communities. Although both literal and…
The Files are in the Computer: On Copyright, Memorization, and Generative AI
A. Feder Cooper, James Grimmelmann
The New York Times's copyright lawsuit against OpenAI and Microsoft alleges OpenAI's GPT models have "memorized" NYT articles. Other lawsuits make similar claims. But parties, cour…
Report of the 1st Workshop on Generative AI and Law
A. Feder Cooper, Katherine Lee, James Grimmelmann +32
This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw), held in July 2023. A cross-disciplinary group of practitioners and scholars from com…