Super Session: Machine Learning and Public Policy
(Methods and Tools of Analysis)

Thursday, November 7, 2019: 1:45 PM-3:15 PM
Plaza Building: Concourse Level, Plaza Ballroom A & B (Sheraton Denver Downtown)

*Names in bold indicate Presenter

Organizer:  Jens Ludwig, University of Chicago
Moderator:  Jens Ludwig, University of Chicago
Speakers:  Sendhil Mullainathan, University of Chicago, Janey Rountree, California Policy Lab at UCLA, Elizabeth Glazer, Mayor’s Office of Criminal Justice, New York City and Candice Jones, Public Welfare Foundation

This session discusses the promise of machine learning tools for addressing public policy problems as well as the potential pitfalls (and how to manage them). Policy analysts have long contributed to social good by helping answer causal questions. But countless decisions important for policy hinge not on a causal inference, but on a prediction: Which students are most likely to drop out? Which defendants are most likely to recidivate? Who is most likely to repay a mortgage or some other loan? Which candidate teachers, social workers, public defenders, police, etc. would be most effective if hired? The growing availability of large-scale government administrative records (‘big data’) together with powerful new prediction tools from the field of machine learning make it possible to make substantial progress on these problems, and policy analysis has an important role to play, but these new tools also create important risks that must be managed.

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