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Andreas Terzis

Google DeepMind

Researcher

SCHOLARLY PAPERS

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Scholarly Papers (1)

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1.

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research

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Citation 2

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research

Stanford Public Law Working Paper
Number of pages: 30 Posted: 13 Jun 2025 Last Revised: 31 Oct 2025
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Niloofar Mireshghallah, Abigail Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, Danah Boyd, Yejin Choi, Sanmi Koyejo, Fernando Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Yangsibo Huang, Hanna Wallach, Miles Brundage, Amy Cyphert, David Bau, Mark A. Lemley, Seth Neel, Nicolas Papernot and Katherine Lee
Yale University, Google DeepMind, Independent, affiliation not provided to SSRN, Google DeepMind, Google DeepMind, Stanford University, Carnegie Mellon University - H. John Heinz III School of Public Policy and Management, Google DeepMind, Google DeepMind, Google Research, Google Research, University of Washington - Paul G. Allen School of Computer Science & Engineering, University of Michigan at Ann Arbor, Cornell Law School, Cornell University - Cornell Tech NYC, Cornell University - Department of Computer Science, Google DeepMind, Google DeepMind, Microsoft ResearchCornell University, Microsoft Research New York City, Microsoft Research, University of Washington, Stanford University, Cornell University, Stanford University - Department of Computer Science, Stanford University, University of California, Berkeley - School of Law, Google Research, Microsoft Research New York City, Independent, West Virginia University - College of Law, Northeastern Univeristy, Stanford Law School, University of Pennsylvania - Statistics Department, University of Toronto and Cornell University - Department of Computer Science
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machine learning, generative AI, copyright, privacy, safety, machine unlearning

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice

Yale Law & Economics Research Paper, Stanford Public Law Working Paper, WVU College of Law Research Paper No. 2025-004
Number of pages: 35 Posted: 06 Feb 2025 Last Revised: 14 Jun 2025
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, Danah Boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot and Katherine Lee
Yale University, Google DeepMind, Independent, Google DeepMind, Google DeepMind, Stanford University, Independent, Google DeepMind, Google Research, University of Washington - Paul G. Allen School of Computer Science & Engineering, Google DeepMind, Google DeepMind, Google Research, Google Research, Stanford University - Department of Computer Science, Stanford Law School, University of Washington, Stanford University, Cornell University, Cornell Law School, Cornell University - Cornell Tech NYC, Cornell University - Department of Computer Science, Microsoft ResearchCornell University, West Virginia University - College of Law, Stanford Law School, Microsoft Research, Microsoft Research New York City, Independent, Northeastern Univeristy, University of Pennsylvania - Statistics Department, University of Michigan at Ann Arbor, Google DeepMind, Microsoft Research New York City, University of Toronto and Cornell University - Department of Computer Science
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Citation 2

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machine unlearning, privacy, copyright, safety, Generative AI