4 citations · 8 across the 5 of their papers we have counts for
5 papers
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
A. Feder Cooper, Mark A. Lemley, Christopher De Sa +6
Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -…
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…
An Abundance of Katherines: The Game Theory of Baby Naming
Katy Blumer, Kate Donahue, Katie Fritz +5
In this paper, we study the highly competitive arena of baby naming. Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeab…
Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain
Katherine Lee, A. Feder Cooper, James Grimmelmann
"Does generative AI infringe copyright?" is an urgent question. It is also a difficult question, for two reasons. First, "generative AI" is not just one product from one company. I…
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi +6
Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examp…