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Predict and SurveilData, Discretion, and the Future of Policing$
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Sarah Brayne

Print publication date: 2020

Print ISBN-13: 9780190684099

Published to Oxford Scholarship Online: October 2020

DOI: 10.1093/oso/9780190684099.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 08 December 2021

Coding Inequality

Coding Inequality

How the Use of Big Data Reduces, Obscures, and Amplifies Inequalities

Chapter:
(p.100) Chapter 6 Coding Inequality
Source:
Predict and Surveil
Author(s):

Sarah Brayne

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190684099.003.0006

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

Keywords:   big data analytics, inequality, racial minorities, police surveillance, data-driven decision-making, social inequalities, system avoidance

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