Predictive policing: not yet, but soon preemptive?

Egbert, Simon; Krasmann, Susanne

doi:10.1080/10439463.2019.1611821

Cited by 58 publications

(23 citation statements)

References 49 publications

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“…With the advent of widespread security cameras in private as well as public spaces, the developments in face recognition software, computing power, and bandwidth, the police increasingly receive the power to track almost anyone anywhere for (hopefully) legal purposes. With such means, predictive policing goes way beyond crime prevention (which may also be aided by data analyses) and becomes a real possibility as data algorithms identify potential crime hotspots before they even occur (Williams et al 2017a;Egbert and Krasmann 2019). Still, this is nothing compared to the width and depth of data penetration the NSA, the US National Security Agency, routinely undertake in their efforts to prevent terrorist attacks and other security threats (Landau 2013).…”

Section: Value and Riskmentioning

confidence: 99%

Big Data: Some Ethical Concerns for the Social Sciences

Weinhardt

2021

Social Sciences

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While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

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Section: Value and Riskmentioning

confidence: 99%

Big Data: Some Ethical Concerns for the Social Sciences

Weinhardt

2021

Social Sciences

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“…Here, I propose that the concept of security assemblages affords an ecological view of the making of crime forecasts that may overcome the above limitations. 4 For a thorough analysis of the shift from after-the-fact, reactive policing to risk-based, pre-emptive security practices, see Ratcliffe 2016;Ferguson 2017b;Jefferson 2018;Shapiro 2019;Egbert and Krasmann 2019;Brayne 2020. 5 For valuable exceptions, see : Kaufmann 2019;Shapiro 2019;Benbouzid 2019.…”

Section: Security Assemblages: Unraveling Grand Narratives Of Police Technologiesmentioning

confidence: 99%

The The Making of Crime Predictions: Sociotechnical Assemblages and the Controversies of Governing Future Crime

Duarte

2021

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We are witnessing an upsurge in crime forecasting software, which supposedly draws predictive knowledge from data on past crime. Although prevention and anticipation are already embedded in the apparatuses of government, going beyond a mere abstract aspiration, the latest innovations hold out the promise of replacing police officers’ “gut feelings” and discretionary risk assessments with algorithmic-powered, quantified analyses of risk scores. While police departments and private companies praise such innovations for their cost-effective rationale, critics raise concerns regarding their potential for discriminating against poor, black, and migrant communities. In this article, I address such controversies by telling the story of the making of CrimeRadar, an app developed by a Rio de Janeiro-based think tank in partnership with private associates and local police authorities. Drawing mostly on Latour’s contributions to the emerging literature on security assemblages, I argue that we gain explanatory and critical leverage by looking into the mundane practices of making and unmaking sociotechnical arrangements. That is, I address the chain of translations through which crime data are collected, organized, and transformed into risk scores. In every step, new ways of seeing and presenting crime are produced, with a significant impact on how we experience and act upon (in)security.

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“…Against this backdrop, predictive policing must be understood as a symptom of larger trajectories of datafication of police work (i.e., the implementation of algorithmic systems for decision assistance and corresponding practices), just as it must be understood as a catalyst for structural reform (Egbert and Krasmann, 2019). The introduction and rapid diffusion of crime prediction software initially caused a hype in media coverage and public attention, but its long-term implications might be far more relevant for the future of policing.…”

Section: From Experiments To Structural Reformmentioning

confidence: 99%

Criminal Futures

Egbert¹,

Leese²

2020

Self Cite

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This book explores how predictive policing transforms police work. Police departments around the world have started to use data-driven applications to produce crime forecasts and intervene into the future through targeted prevention measures. Based on three years of field research in Germany and Switzerland, this book provides a theoretically sophisticated and empirically detailed account of how the police produce and act upon criminal futures as part of their everyday work practices. The authors argue that predictive policing must not be analyzed as an isolated technological artifact, but as part of a larger sociotechnical system that is embedded in organizational structures and occupational cultures. The book highlights how, for crime prediction software to come to matter and play a role in more efficient and targeted police work, several translation processes are needed to align human and nonhuman actors across different divisions of police work. Police work is a key function for the production and maintenance of public order, but it can also discriminate, exclude, and violate civil liberties and human rights. When criminal futures come into being in the form of algorithmically produced risk estimates, this can have wide-ranging consequences. Building on empirical findings, the book presents a number of practical recommendations for the prudent use of algorithmic analysis tools in police work that will speak to the protection of civil liberties and human rights as much as they will speak to the professional needs of police organizations. An accessible and compelling read, this book will appeal to students and scholars of criminology, sociology, and cultural studies as well as to police practitioners and civil liberties advocates, in addition to all those who are interested in how to implement reasonable forms of data-driven policing.

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Predictive policing: not yet, but soon preemptive?

Cited by 58 publications

References 49 publications

Big Data: Some Ethical Concerns for the Social Sciences

Big Data: Some Ethical Concerns for the Social Sciences

The The Making of Crime Predictions: Sociotechnical Assemblages and the Controversies of Governing Future Crime

Criminal Futures

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