Big Data Surveillance: the Case of Policing
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ASRXXX10.1177/0003122417725865American Sociological ReviewBrayne 7258652017 American Sociological Review 2017, Vol. 82(5) 977 –1008 Big Data Surveillance: The © American Sociological Association 2017 https://doi.org/10.1177/0003122417725865DOI: 10.1177/0003122417725865 Case of Policing journals.sagepub.com/home/asr Sarah Braynea 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. Keywords police, big data, inequality, crime, law In the past decade, two major structural devel- subject of contentious debate in policy, media, opments intersected: the proliferation of sur- legal, regulatory, and academic circles. How- veillance in everyday life and the rise of “big ever, discourse on the topic is largely specula- data.” Emblematic of the expansion of sur- tive, focusing on the possibilities, good and veillance (Lyon 2003; Marx 2016; Rule 2007) bad, of new forms of data-based surveillance. is the rapid growth of the criminal justice The technological capacities for surveillance system since 1972 (Carson 2015; Garland far outpace empirical research on the new 2001; Wakefield and Uggen 2010; Western data landscape. Consequently, we actually 2006). At the same time, facilitated by the know very little about how big data is used in mass digitization of information, there has been a rise in the computational analysis of massive and diverse datasets, known as “big aThe University of Texas at Austin data.” Big data analytics have been taken up in a wide range of fields, including finance, Corresponding Author: Sarah Brayne, Assistant Professor, Department of health, social science, sports, marketing, Sociology, The University of Texas at Austin, 305 security, and criminal justice. The use of big E. 23rd St., A1700, Austin, TX 78712-1086 data in police surveillance activities is the Email: [email protected] 978 American Sociological Review 82(5) surveillance activities and to what conse- into relational systems and include data origi- quence. nally collected in other, non-criminal-justice This article provides a case study of a large institutions. These shifts made possible by big urban police department—the Los Angeles data have implications for inequality, law, and Police Department (LAPD)—to investigate organizational practice in a range of institu- the relationship between big data analytics and tional domains. surveillance. In particular, it asks whether and how the adoption of big data analytics trans- forms police surveillance practices. Moreover, THE INTENSIFicatiON OF it investigates implications of new surveillance SURVEILLANCE practices not only for policing, but also for law, Surveillance is ubiquitous in modern societies social inequality, and research on big data sur- (Giddens 1990; Lyon 1994, 2003, 2006, 2015; veillance in other institutions. On the one hand, Marx 1974, 2002, 2016; Rule 1974). The big data analytics may be a rationalizing force, practice of surveillance involves the collec- with potential to reduce bias, increase effi- tion, recording, and classification of informa- ciency, and improve prediction accuracy. On tion about people, processes, and institutions the other hand, use of predictive analytics has (Foucault 1977; Haggerty and Ericson 2000; the potential to technologically reify bias and Lyon 2003; Marx 2016). A wide range of deepen existing patterns of inequality. scholars highlight the growing pervasiveness To shed light on the social dimensions of of surveillance, referring to the emergence of surveillance in the age of big data, I draw on “mass surveillance” (Rule 1974) and “surveil- original interview and observational data col- lance societies” (Lyon 1994). Although it has lected during fieldwork over the course of two received more attention in recent decades, and a half years with the LAPD. As an agency surveillance as a practice is not new. Processes at the forefront of data analytics, the Depart- of surveillance can be traced back to at least ment serves as a strategic site for understand- the sixteenth century, during the appearance of ing the interplay between technology, law, and the nation-state (Marx 2016), through the social relations. I provide one of the first on- transatlantic slave trade (Browne 2015), the-ground accounts of how the growing suite bureaucratization, rationalization, and modern of big data systems and predictive analytics management in the nineteenth and twentieth are used for surveillance within an organiza- centuries (Braverman 1974; Rule 1974; Weber tion, unpacking how in some cases, the adop- 1978), and as an axiomatic accompaniment to tion of big data analytics is associated with risk management practices in the twentieth mere amplifications in prior practices, but that century (Ericson and Haggerty 1997). The in others, it is associated with fundamental attacks on September 11th, 2001, further transformations in surveillance activities. I stimulated and legitimated the expansion of argue there are five key ways in which the surveillance. Widely viewed as a case of infor- adoption of big data analytics is associated mation sharing failure in the intelligence com- with shifts in practice to varying degrees: (1) munity, 9/11 encouraged an alignment of discretionary assessments of risk are supple- actors with vested interests in enhancing sur- mented and quantified using risk scores; (2) veillance operations (Ball and Webster 2007; data are increasingly used for predictive, Lyon 2015), spurred an infusion of tax dollars rather than reactive or explanatory, purposes; for development of new surveillance sensors (3) the proliferation of automated alerts makes and data mining programs to produce strategic it possible to systematically surveil an unprec- intelligence (Gandy 2002), and accelerated edentedly large number of people; (4) datasets the convergence of previously separate sur- now include information on individuals who veillance systems into a “surveillant assem- have not had any direct police contact; and (5) blage” (Deleuze and Guattari 1987; Haggerty previously separate data systems are merged and Ericson 2000). Brayne 979 Surveillance scholars have documented a remote, low visibility or invisible, involun- quantitative increase in surveillance, arguing tary, automated, preemptive, and embedded that surveillance is one of the major institu- into routine activity (Marx 2002, 2016). In tional dimensions of modern societies (Ball fact, new forms of systematic surveillance and Webster 2007; Giddens 1990; Lyon 1994, have become quotidian to the point that it is 2003; Marx 1988, 2016). Although surveil- now an unavoidable feature of everyday life lance is growing in all areas of society, its (Ball and Webster 2007; Lyon 2003; Marx penetration is unevenly distributed (Fiske 2016). Consider the extent to which surveil- 1998). Some individuals, groups, areas, and lance is a requisite of participating in today’s institutions are surveilled more than others, world (Ball and Webster 2007): to use a bank, and different populations are surveilled for send an e-mail, obtain medical care, make a different purposes (Lyon 2003). On the one phone call, travel on a highway, or conduct an hand, there is a deepening of surveillance of Internet search, individuals leave digital “at-risk” groups, such as parolees and indi- traces that are recorded and saved (see also viduals on public assistance (Gilliom 2001; Foucault 1977; Haggerty and Ericson 2000; Gustafson 2011; Soss, Fording, and Schram Lyon 2003). 2011), who can increasingly be tracked across Technologically mediated surveillance has institutional boundaries. On the other hand, become routine organizational practice in a emerging “dragnet” surveillance practices— wide range of public and private domains. It meaning those that collect data on everyone, is a tool of general governance (Lyon 2003), rather than merely individuals under suspi- a basic prerogative of individuals in all sorts cion—result in increased monitoring of of private and public institutions, and a tool to groups “previously exempt from routine sur- accomplish “goals that meet particular inter- veillance” (Haggerty and Ericson 2000:606; ests” (Ericson and Haggerty 2006:22). Sur- see also Angwin 2014; Lyon 2015). Surveil- veillance scholars have accordingly extended