Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Brantingham, P. Jeffrey; Valasik, Matthew; Mohler, George O.
• Is Part Of: Statistics and public policy. Volume 5:Issue 1(2018)
• Page Start: 1
• Page End: 6
• Publication Date: 2018-01-01
• Subjects: Civil liberties -- Crime forecasting -- Police bias; Policy sciences -- Methodology -- Periodicals; Social sciences -- Statistical methods -- Periodicals; Medical statistics -- Methodology -- Periodicals; Statistics -- Periodicals; Medical statistics -- Methodology; Policy sciences -- Methodology; Social sciences -- Statistical methods; Statistics; Periodicals; 320.60727
• DOI: 10.1080/2330443X.2018.1438940
• Languages: English
• ISSNs: 2330-443X

Abstract

ABSTRACT: Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulation-based research. There is reasonable concern that predictive algorithms encourage directed police patrols to target minority communities with discriminatory consequences for minority individuals. However, to date there have been no empirical studies on the bias of predictive algorithms used for police patrol. Here, we test for such biases using arrest data from the Los Angeles predictive policing experiments. We find that there were no significant differences in the proportion of arrests by racial-ethnic group between control and treatment conditions. We find that the total numbers of arrests at the division level declined or remained unchanged during predictive policing deployments. Arrests were numerically higher at the algorithmically predicted locations. When adjusted for the higher overall crime rate at algorithmically predicted locations, however, arrests were lower or unchanged.