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Identifying racial bias in policing: current shortcomings and new approaches

Over 20 million traffic stops are conducted annually in the U.S., making this the primary way in which the public interacts with law enforcement. Although there is widespread concern of racial bias in these interactions, accurately identifying and measuring bias is difficult — existing tests can often give misleading results in practice. Here’s why, and how a new test to identify bias in officers’ decisions to search drivers overcomes some of the current limitations.

Camelia is a PhD candidate at Stanford University in the Management Science and Engineering Department. Previously, she was a Fellow of the University of Chicago’s Data Science for Social Good program, and a visiting researcher at the MIT Media Lab in the Human Dynamics group. She received a B.S. in applied statistics (actuarial science) from the University of Toronto and a M.S. in Artificial Intelligence from the University of Amsterdam. Prior to graduate school, she worked as a consulting actuarial analyst at Mercer, focusing on quantitative risk modeling.


About the Speaker

Camelia Simoiu

Camelia Simoiu

Camelia Simoiu is a Ph.D. candidate at Stanford University in the Management Science & Engineering Department. Her research interests are in developing statistical methods and data-driven tools to evaluate and design effective public policy. Application areas of particular interest include criminal justice and cyber security. Previously, Camelia was a Fellow of the University of Chicago’s Data Science for Social Good program, and a visiting researcher at the MIT Media Lab. She received a M.S. in Artificial Intelligence from the University of Amsterdam, and a B.S. in applied statistics from the University of Toronto.