Coding Inequality

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.

Directed Surveillance

This chapter focuses on directed surveillance, or the surveillance of people and places deemed suspicious. Big data is associated with a shift from reactive to predictive policing. Predictive policing refers to analytic techniques used by law enforcement to forecast potential criminal activity. It involves using data to determine current crime patterns and direct patrol resources, such as where officers should go and who they should stop. In general terms, the stages of predictive policing are collection, analysis, intervention, and response. The chapter analyzes how the Los Angeles Police Department (LAPD) conducts person- and place-based predictive policing; why it adopted different forms of predictive policing; how it uses algorithms to quantify criminal risk; how the police do—and do not—incorporate insights from the algorithms into their work in the field; and how predictive policing serves as a foundation for ongoing intelligence gathering.

Algorithmic Suspicion and Big Data Searches

This chapter assesses how existing legal frameworks are anachronistic and inadequate for governing police work in the age of big data. There is now a burgeoning body of legal scholarship analyzing the legal implications of big data policing, yet it is largely theoretical. By grounding legal debates about police use of data in empirical detail, the chapter makes the case that basic legal principles are inadequate not simply because they are anachronistic, but also because the legal debates are too narrow. There are a number of ways legal frameworks are overlooking the social side of big data. First, the way the conceptual categories that underpin legal doctrine—like individualized suspicion—are deployed and organized to make normative assessments do not reflect how decision-making plays out on the ground. Second, police are not simply scaling up data collection in the digital age; rather, different kinds of data are being produced. Despite the fact that there is a difference in kind—rather than just degree—old legal doctrine is still being laid on top of these data. Third, relying on extant legal mechanisms like the exclusionary rule involves using what is meant to be a check on state power at one point in time and space, whereas data is fundamentally social and, as such, has a life course. Fourth, unfettered big data policing creates new opportunities for information asymmetries and can threaten due process through a practice called “parallel construction.”

Policing by Numbers

This chapter traces the history of quantification in policing, from pin maps to predictive algorithms. It examines the surveillance landscape, starting with the “scientific turn” in policing in the early 20th century, then moving to the rise of evidence-based policing, the Information Sharing Environment (ISE) that emerged after the terrorist attacks of 9/11, and then predictive algorithms and big data analytics put to work in modern policing. Historically, the police collected most of the information they use in the course of their daily operations themselves. However, the chapter highlights the growing role of the private sector—for data collection and the provision of analytic platforms—in policing. Both the past and present of policing are highly racialized, so it also describes how data is positioned as an antidote to racism and bias in policing. The chapter concludes with an overview of data use and technologies at work in the Los Angeles Police Department (LAPD).

Conclusion

This concluding chapter reflects on the major lessons learned from this book. The Los Angeles Police Department’s continued use of big data policing underscores the primary argument running through this book—that big data is fundamentally social. At every phase—from big data’s adoption to collection and analysis, institutional intervention, and reception—there is a social patterning to how data is used. There is a need to curb institutions’ craving for data, because that hunger too often outpaces people’s understanding of the intended and unintended consequences of a data binge. The chapter then offers guidelines for academics, lawmakers, and community groups regarding how data can be leveraged to promote efficiency, fairness, and accountability in criminal justice reform. It also discusses the future of law enforcement; identifies policy and research priorities; and argues that the implications of big data surveillance extend beyond policing into other social sectors.

Police Pushback

This chapter highlights how the police resist and contest big data analytics. Their resistance stems in large part from the proliferation of high-frequency observations and data collection sensors resulting in the police themselves coming under increased surveillance. New developments in surveillance technologies are frequently viewed with suspicion, with officers believing technology is a means of deskilling, entrenching managerial control, devaluing experiential knowledge, and threatening their professional autonomy. Novel tech ultimately serves to reinforce old divisions—such as those between managers and patrol officers—even as it creates new distinctions within the Los Angeles Police Department (LAPD). Understanding these patterns of contestation underscores how big data is ultimately social. It also allows one to consider the extent to which data-based surveillance is—and is not—associated with deeper organizational change.

Introduction

This introductory chapter provides a definition of big data as well as an overview of the use of big data in policing. Big data is a data environment made possible by the mass digitization of information and is associated with the use of advanced analytics, including network analysis and machine learning algorithms. Law enforcement’s adoption of big data is part of a broader shift toward the use of big data and machine-learned decisions throughout the criminal justice system. From surveillance to pretrial determinations and sentencing, big data saturates American criminal justice. It is also the subject of contentious debate in policy, media, legal, regulatory, advocacy, and academic circles. Focusing on the Los Angeles Police Department’s use of big data and associated surveillance technologies, this book studies how big data is actually used by police in practice—and to what consequence.

Dragnet Surveillance

This chapter discusses dragnet surveillance, which is the collection and analysis of information on everyone, rather than merely those under suspicion. Dragnet surveillance—and the data it produces—can be useful for law enforcement to solve crimes. Dragnet surveillance widens and deepens social oversight: it includes a broader swath of people and can follow any single individual across a greater range of institutional settings. It is associated with three key transformations in the practice of policing: the shift from query-based to alert-based systems makes it possible to systematically surveil an unprecedentedly large number of people; individuals with no direct police contact are now included in law enforcement systems, lowering the threshold for inclusion in police databases; and institutional data systems are integrated, with police now collecting and using information gleaned from institutions not typically associated with crime control. However, dragnet surveillance is not an inevitable result of mass digitization. Rather, it is the result of choices that reflect the social and political positions of the subjects and the subject matter under surveillance.