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HCII Seminar Series - Abbie Jacobs

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Abbie Jacobs

Abbie Jacobs
Assistant Professor of Information, School of Information, University of Michigan; Assistant Professor of Complex Systems, College of Literature, Sciences and the Arts, University of Michigan


Newell-Simon Hall 1305



"From Cadavers to Categories: Unpacking Upstream Assumptions in AI"

All technical systems depend on assumptions: about how the world works, about what the system is expected to do, about how we know if the system works (or not). Thus understanding systems–and if they work, and if they cause harms–depends on unearthing the assumptions upon which they are built. I will discuss several projects around this perspective: using measurement theory as a framework to uncover how often-hidden decisions are enacted, including where existing 'fairness' interventions can fall short; how social categories are made real; and where assumptions get carried forward, stabilized, and entrenched over time. I will argue that this perspective gives us leverage into the hidden challenges in designing equitable artificial intelligence systems---and mitigating the harms already emerging from AI.

Speaker's Bio

Abigail Jacobs is an assistant professor of Information and of Complex Systems at the University of Michigan, where she is also affiliated with the Center for Ethics, Society, and Computing (ESC) and the Michigan Institute for Data Science (MIDAS). She is a 2024 Microsoft Research AI & Society Fellow. Previously she was a postdoctoral fellow at the Haas School of Business at UC Berkeley and a member of the Algorithmic Fairness and Opacity Working Group. She received a PhD in Computer Science at the University of Colorado Boulder and a BA in Mathematical Methods in the Social Sciences and Mathematics from Northwestern University. She previously spent time at Microsoft Research NYC and served on the Board of Directors for Women in Machine Learning, Inc.

Speaker's Website