AI Evaluation and the Standards Metaphor

Join us for a discussion with Abigail Z. Jacobs on her recent essay with Amina A. Abdu in the Yale Journal of Law & Technology!

Paper: https://yjolt.org/ai-evaluation-and-standards-metaphor

Abstract: Significant attention has been devoted to the question of how best to govern artificial intelligence (AI). In addition to legislation, many policy proposals focus on extra-legal regulatory instruments. Notably, AI evaluations provide a particularly attractive solution, imposing seemingly neutral measurements across the widespread contexts in which AI operates. Because AI evaluations are driven by a wide range of actors, their adoption as a governance tool is shifting power in AI policymaking. In particular, the companies that create AI are also key players in designing and marketing AI evaluations. This Essay examines how large technology companies and government actors conceptualize self-regulation by technology companies as a legitimate policy intervention. We note that AI evaluations are often described using the language of standards, another more established soft law regulatory instrument. Drawing on the history of standards, we discuss how AI companies leverage the metaphor of standards to describe benchmarks and evaluations in order to legitimate corporate expertise. We then examine the implications of this metaphor, describing where it is useful in the context of AI and where it obscures important policy decisions.

Bonus reading: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5160457

Bio:

Abigail Jacobs is an Assistant Professor of Information and of Complex Systems at the University of Michigan. Abigail Jacobs is a 2024 Microsoft Research AI & Society Fellow and was selected for the 2025 Schmidt Sciences Humanities & AI Virtual Institute. At Michigan, she is affiliated with the Center for Ethics, Society, and Computing and the Michigan Institute for Data & AI in Society. She received a B.A. in Mathematical Methods in the Social Sciences and Mathematics at Northwestern University and a Ph.D. in Computer Science from the University of Colorado Boulder, and she previously was a postdoc at UC Berkeley; a NSF GRFP fellow; and on the board of Women in Machine Learning, Inc.

With social scientists, humanists, and legal scholars, she adopts a sociotechnical approach to AI to understand the hidden assumptions built into seemingly objective machine learning systems and their technical and social implications. With computer scientists, her work uses the lens of measurement to improve AI evaluation and governance.

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