“The Accuracy, Fairness, and Limits of Machine Learning in Criminal Justice"

Hany Farid, UC Berkeley and Sharad Goel, Stanford “The Accuracy, Fairness, and Limits of Machine Learning in Criminal Justice”
Monday, April 12, 2021 / 3:30 PM
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Abstract: Algorithms have become a common tool for prediction and decision making in the criminal justice system. The greater use, at both federal and state levels, has generated greater scrutiny: with so much at stake, it is imperative to ensure that recommendations are accurate, transparent, and fair. Advocates for this approach argue that unbiased algorithms can outperform humans, while critics point to the many ways that data fed to algorithms can lead to unfair decisions. In this talk, Sharad Goel and Hany Farid will share their research findings and discuss the potential, limitations, and risks of algorithmic justice.

Farid BIO: 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, forensic science, misinformation, 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. 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.

Goel BIO: Sharad Goel is an assistant professor at Stanford University in the Department of Management Science & Engineering, with courtesy appointments in Computer Science, Sociology, and the Law School. He's the founder and faculty director of the Stanford Computational Policy Lab, a group that develops technology to tackle pressing issues in criminal justice, education, voting rights, and beyond. In his research, Sharad looks at public policy through the lens of computer science, bringing a new, computational perspective to a diverse range of contemporary social issues, including policing practices, electoral integrity, online privacy, and media bias. Before joining the Stanford faculty, Sharad completed a Ph.D. in applied mathematics at Cornell University, and worked as a senior researcher at Microsoft.

This event is co-sponsored by the Center for Information Technology and Society, the Center for Responsible Machine Learning and the Sage Center for the Study of the Mind.