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Abigail Z. Jacobs

2 accepted papers

2025

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

NeurIPS 2025oral

"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, copyright, safety, and more. For example, unlearning is often invoked as a solution for removing the effects of specific infor…

Cited by 0SourceScholar
2025

Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge

ICML 2025poster

The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as "a tangle of sloppy tests [and] apples-to-oranges comparisons" (Roose, 2024). In this position paper, we argue that the ML community would benefit from l…

Cited by 0SourcePDFScholar