Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
By P. Jeffrey Brantingham, Matthew A. Valasik, George O. Mohler. 2018
110 citations recorded by the source corpus.
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.
Subfields: Political Science and International Relations, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/8e5989caf0d96931297bdd5cf5f857b78ce5e754
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In Political Science and International Relations
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Citations in the corpus
- Machine learning in medicine: a practical introduction (2019, background): “Though the evidence of whether predictive policing algorithms leads to biases in practice is unclear [35], it stands to reason that if biases exist in routine police work then models taught to recognize patterns in routinely collected data would have no means to exclude these biases when making…”
- Mining Big Data in Education: Affordances and Challenges (2020, background): “For example, much attention has been focused on the potential for racial bias in predictive algorithms used in policing (e.g., Brantingham et al., 2018).”