Policing by Numbers: Big Data and the Fourth Amendment
Elizabeth E. Joh
U.C. Davis School of Law
February 1, 2014
89 Wash. L. Rev. 35 (2014)
The age of “big data” has come to policing. In Chicago, police officers are paying particular attention to members of a “heat list”: those identified by a risk analysis as most likely to be involved in future violence. In Charlotte, North Carolina, the police have compiled foreclosure data to generate a map of high-risk areas that are likely to be hit by crime. In New York City, the N.Y.P.D. has partnered with Microsoft to employ a “Domain Awareness System” that collects and links information from sources like CCTVs, license plate readers, radiation sensors, and informational databases. In Santa Cruz, California, the police have reported a dramatic reduction in burglaries after relying upon computer algorithms that predict where new burglaries are likely to occur. Unlike the data crunching performed by Target, Walmart, or Amazon, the introduction of big data to police work raises new and significant challenges to the regulatory framework that governs conventional policing. This article identifies three uses of big data and the questions that these tools raise about conventional Fourth Amendment analysis. Two of these examples, predictive policing and mass surveillance systems, have already been adopted by a small number of police departments around the country. A third example — the potential use of DNA databank samples — presents an untapped source of big data analysis. While seemingly quite distinct, these three examples of big data policing suggest the need to draw new Fourth Amendment lines now that the government has the capability and desire to collect and manipulate large amounts of digitized information.
Number of Pages in PDF File: 34
Keywords: police, Fourth Amendment, surveillance, technology, big data, DNA, domain awareness system, predictive policing, risk analysis, software, algorithm, social network analysis, risk terrain modeling, data driven policing
JEL Classification: K42Accepted Paper Series
Date posted: March 2, 2014 ; Last revised: April 4, 2014
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