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“Big Data Surveillance: The Case of Policing

Brayne, Sarah·2017·American Sociological Review
DOI 10.1177/0003122417725865
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Abstract

This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

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Metadata

Authors
Brayne, S.
Affiliations
The University of Texas at Austin
Venue
American Sociological Review
Publisher
SAGE Publications
Published
2017-08-29
Volume
82
Issue
5
Pages
977-1008
ISSN
  • 0003-1224
  • 1939-8271
DOI
10.1177/0003122417725865
Cited by
596
References
14
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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