21 Jul 2022

#DS4SocietySeminar 2022 <> Algorithms on Trial: Interrogating Evidentiary Statistical Software

Rediet Abebe

Talk Details


The U.S. criminal legal system increasingly relies on software output to convict and incarcerate people. In a large number of cases each year, the government makes these consequential decisions based on evidence from statistical software—such as probabilistic genotyping, environmental audio detection and toolmark analysis tools—that the defense counsel cannot fully cross-examine or scrutinize. This undermines the commitments of the adversarial criminal legal system, which relies on the defense’s ability to probe and test the prosecution’s case to safeguard individual rights.

Responding to this need to adversarially scrutinize output from such software, in this talk, we propose a novel framework for examining the validity of evidentiary statistical software called Robust Adversarial Testing. We define and operationalize this notion of robust adversarial testing for defense use by drawing on a large body of recent work in robust machine learning and algorithmic fairness. We demonstrate how this framework both standardizes the process for scrutinizing such tools and empowers defense lawyers to examine their validity for instances most relevant to the case at hand. We further discuss existing structural and institutional challenges within the U.S. criminal legal system which may create barriers for implementing this framework and close with a discussion on policy changes that could help address these concerns. We close with an outline of research directions in this burgeoning area of adversarial ML and adversarial scrutiny in the law.

This talk is based on joint and ongoing work with Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler, as well as conversations with numerous public defenders and forensic scientists including Nathan Adams, Clinton Hughes, Daniel Krane, and Richard Torres.

Speaker Bio

Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley. She is a 2022 Andrew Carnegie Fellow and on leave as a Junior Fellow at Harvard University. Her research examines the interaction of algorithms and inequality, with a focus on contributing to the mathematical and computational foundations of this emerging research area. Abebe co-launched and serves on the Executive Committee for the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO). She previously co-founded the related global research initiative, MD4SG. Abebe’s work has received recognitions including the MIT Technology Reviews’ 35 Innovators Under 35, the 2020 ACM SIGKDD Dissertation Award, an honorable mention by the ACM SIGEcom Dissertation Award, and the Bloomberg 50 list as a one to watch. Abebe co-founded and serves on the Board of Directors for Black in AI, a non-profit organization tackling equity issues in AI. Abebe holds a Ph.D. in computer science from Cornell University and masters degrees in mathematics from Harvard University and the University of Cambridge.

Video, Slides and Notes

Video - Talk + Q&A

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