A Computational Approach to Reducing Inequality in the Criminal Legal System
Alex Chohlas-Wood
Cosponsored by the School of Information and the Goldman School of Public Policy
In this talk, I will show how the responsible use of data science and information technology creates new avenues to improve outcomes in the criminal legal system. In particular, I will focus on two such interventions I designed: first, an algorithm that removes race-related information from crime reports, allowing prosecutors to make a race-blind decision about whether to charge someone with a crime; and second, a series of behavioral nudges designed to help low-income public defender clients avoid incarceration imposed for missed court dates. Each intervention has been validated in real-world pilot projects. I will explain how each intervention works, discuss findings from each pilot, and touch on my larger body of research that investigates how technology can improve policy outcomes for marginalized communities.
Speaker
Alex Chohlas-Wood is a research fellow at the Harvard Kennedy School and is the executive director of the Harvard Computational Policy Lab. His work focuses on using statistics, machine learning, and information technology to support criminal legal reform. He regularly collaborates with researchers and policymakers to reduce inequality and discrimination through technical innovation. Alex received a Ph.D. in computational social science from Stanford University, an M.S. in urban informatics from New York University's Center for Urban Science and Progress, and previously served as the director of analytics for the New York City Police Department.
Contact
If you have questions about this event, please contact Aaron Schiller at aaschiller@berkeley.edu or (510) 664-7494.