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Disparate Impact in Big Data Policing

76 Pages Posted: 1 Oct 2016 Last revised: 6 Aug 2017

Andrew D. Selbst

Data & Society Research Institute; Yale Information Society Project

Date Written: February 25, 2017

Abstract

Police departments large and small have begun to use data mining techniques to predict, prevent, and investigate crime. But data mining systems have the potential for adverse impacts on vulnerable communities, and predictive policing is no different. Reviewing the technical process of predictive policing, the Article begins by illustrating how the different uses of this technology may result in disparate impact on communities of color. 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 minority neighborhoods. 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. This is particularly ironic because predictive policing, like a great deal of data mining solutions, is sold in part as a “neutral” method to counteract unconscious biases, when it is not simply sold to cash-strapped departments as a more cost-efficient way to do policing.

The degree to which predictive policing systems have these discriminatory results is unclear to the public and unclear to the police themselves, largely because there is no incentive in place for a department focused solely on “crime control” to even ask the question. This is a problem for which existing law does not provide a solution. Assuming no intent to discriminate, the Fourteenth Amendment is no help. After evaluating the possibilities for Fourth Amendment regulation and finding them wanting, the Article turns toward a new regulatory proposal centered on “discrimination impact assessments.”

Modeled on the environmental impact statements of the National Environmental Policy Act, discrimination impact assessments would require police departments to evaluate the efficacy and potential discriminatory effects of competing alternative algorithms and models and to consider mitigation procedures. The regulation would also allow the public to weigh in through a notice-and-comment process. Such a regulation would fill the knowledge gap that makes future policy discussions about the costs and benefits of predictive policing all but impossible. It balances the need for police expertise in the adoption of new crime control technologies with transparency and public input regarding the potential for harm. Such a public process will also serve to increase trust between police departments and the communities they serve.

Keywords: Civil Rights, Disparate Impact, Discrimination, Big Data, Data Mining, Algorithms, Machine Learning, Policing, Predictive Policing, Fourth Amendment, Criminal Procedure, Administrative Law

Suggested Citation

Selbst, Andrew D., Disparate Impact in Big Data Policing (February 25, 2017). Georgia Law Review, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2819182

Andrew D. Selbst (Contact Author)

Data & Society Research Institute ( email )

36 West 20th Street
11th Floor
New York,, NY 10011
United States

Yale Information Society Project ( email )

127 Wall Street
New Haven, CT 06511
United States

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