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Activity Number: 686
Type: Topic Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract #318754 View Presentation
Title: Testing for Discrimination in Police Searches
Author(s): Camelia Simoiu* and Sam Corbett-Davies and Sharad Goel
Companies: and Stanford University and Stanford University
Keywords: bayesian ; hierarchical ; police ; bias ; racial ; latent

In the course of conducting traffic stops, officers have discretion to search motorists for drugs and other contraband. Scholars and criminal justice advocates have raised concerns that search decisions are prone to racial bias, but it has proven difficult to empirically evaluate these claims. Here we develop a novel statistical method for testing for discrimination in such circumstances. Namely, we use a hierarchical Bayesian latent variable model to infer hidden race-specific thresholds of evidence that officers apply when deciding to search motorists. On a data set of six million police stops in North Carolina from 2009 to 2014, we find that the threshold for searching blacks and Hispanics is significantly lower than the threshold for searching whites, suggestive of racial discrimination in these interactions.

Authors who are presenting talks have a * after their name.

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