The Accuracy, Fairness, and Limits of Predicting Recidivism

Dr. Hany Farid
Professor, School of Information and EECS, University of California, Berkeley

Date and Time: Tuesday, Jan 19 at 12:00 PM - 1:30 PM
GoToMeeting Link:
Phone Dial-in: +1 (872) 240-3212      Access Code: 169-903-213

Synopsis. 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. Professor Hany Farid will discuss an in depth analysis of one widely used commercial predictive algorithm to determine its appropriateness for use in courts

Bio. Dr. Hany Farid is a Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering & 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, 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. Professor Farid is the recipient of an Alfred P. Sloan Fellowship, a John Simon Guggenheim Fellowship, and is a Fellow of the National Academy of Inventors.


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United States: +1 (872) 240-3212


Access Code: 169-903-213