AI Seminar sponsored by Fortive

Hany Farid -- The Accuracy, Fairness, and Limits of Predicting Recidivism

Abstract: Predictive algorithms are commonly used in the criminal justice system. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these predictions more accurate and less biased than humans. Opponents, however, argue that predictive algorithms may lead to further bias in the criminal justice system. I will discuss an in-depth analysis of one widely used commercial predictive algorithm to determine its appropriateness for use in our courts.

Bio: Hany Farid is a Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering and Computer Sciences and the School of Information. His research focuses on digital forensics, image analysis, and human perception. He received his undergraduate degree in Computer Science and Applied Mathematics from the University of Rochester in 1989, his M.S. in Computer Science from SUNY Albany, and his Ph.D. in Computer Science from the University of Pennsylvania in 1997. Following a two-year post-doctoral fellowship in Brain and Cognitive Sciences at MIT, he joined the faculty at Dartmouth College in 1999 where he remained until 2019. He is the recipient of an Alfred P. Sloan Fellowship, a John Simon Guggenheim Fellowship, and is a Fellow of the National Academy of Inventors.