Summary of Machine Unlearning Doesn’t Do What You Think: Lessons For Generative Ai Policy, Research, and Practice, by A. Feder Cooper et al.

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

by A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu 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 Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee

First submitted to arxiv on: 9 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed paper investigates the mismatch between technical approaches to machine unlearning in Generative AI and the aspirations for broader impact on law and policy. The study highlights various documented concerns about privacy, copyright, safety, and more that these methods could address. For instance, unlearning is often suggested as a solution to remove personal data or copyrighted content from a model’s parameters or outputs. However, both goals – targeted removal of information and suppression of specific types of information – present technical and substantive challenges. The authors provide a framework for rigorously addressing these challenges, emphasizing the limitations of machine unlearning in achieving broader positive impact. They aim to promote conceptual clarity and foster more thoughtful communication among experts in machine learning, law, and policy.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper looks at how AI can be used to remove unwanted information from its training data or outputs. Some people think this could help with privacy concerns, by removing personal data like a person’s name or face. Others want to use unlearning to prevent AI models from generating certain types of content, like copyrighted material. The problem is that these goals are hard to achieve technically and may not have the desired effect. The authors try to make sense of these challenges and how they can be addressed.