Disparate Impact in Big Data Policing
DISPARATE IMPACT IN BIG DATA POLICING Andrew D. Selbst* Data-driven decision systems are taking over. No institution in society seems immune from the enthusiasm that automated decision-making generates, including—and perhaps especially—the police. Police departments are increasingly deploying data mining techniques to predict, prevent, and investigate crime. But all data mining systems have the potential for adverse impacts on vulnerable communities, and predictive policing is no different. Determining individuals’ threat levels by reference to commercial and social data can improperly link dark skin to higher threat levels or to greater suspicion of having committed a particular crime. Crime mapping based on historical data can lead to more arrests for nuisance crimes in neighborhoods primarily populated by people of color. These effects are an artifact of the technology itself, and will likely occur even assuming good faith on the part of the police departments using it. Meanwhile, predictive policing is sold in part as a “neutral” method to counteract unconscious biases when it is not simply * Postdoctoral Scholar, Data & Society Research Institute; Visiting Fellow, Yale Information Society Project. J.D. 2011, University of Michigan. For helpful comments and insights on earlier drafts, I would like to thank Micah Altman, Jack Balkin, Jane Bambauer, Solon Barocas, Rabia Belt, Kiel Brennan-Marquez, William Buzbee, Danielle Citron, Julie Cohen, Jessica Eaglin, Gautam Hans, Don Herzog, Mark MacCarthy, Deirdre Mulligan, Leonard Niehoff, Paul Ohm, Christopher Slobogin, Katherine Strandburg, David Super, Michael Veale, David Vladeck, Robin West, and the participants at the Georgetown Fellows Colloquium, Georgetown Summer Faculty Workshop, Michigan Future Law Professors Workshop, and 2017 Privacy Law Scholars’ Conference.
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