This timely book presents rare ethnographic data within an outstanding analysis of current debates on predictive policing. Conceptualising predictive policing as a sociotechnical system, the book describes various translation processes that lay bare the political, cultural and organizational forces at work. This welcome book sets the standards for future research on data-driven policing. —Janet Chan, Professor, UNSW Law Wary of simplistic dystopia/utopia dichotomies, Criminal Futures offers a theoretically sophisticated and empirically rich account of predictive policing as a sociotechnical process. This is a landmark study, providing frameworks and analytical tools for understanding – and responding to – the rapid datafication of security that is unfolding. —Dean Wilson, Professor of Criminology, University of Sussex Criminal Futures 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. Simon Egbert is a postdoc researcher at the Department of Sociology, Technische Universität Berlin. Trained in sociology and criminology, his research interests include science and technology studies, security studies, sociology of prediction, time studies, discourse theory, visual knowledge studies, and sociology of testing. He has published papers on predictive policing, drug testing, lie detection, and ignition interlock devices. Matthias Leese is Senior Researcher for governance and technology at the Center for Security Studies, ETH Zurich. His research is primarily interested in the social effects produced at the intersections of security and technology. It pays specific attention to the normative repercussions of new security technologies across society, in both intended and unintended forms. His work covers various application contexts of security technologies, including airports, borders, policing, and R&D activities. Routledge Studies in Policing and Society Series Editors Jenny Fleming, University of Southampton, UK Jennifer Wood, Temple University, USA Routledge Studies in Policing and Society aims to establish an inter- disciplinary, international intellectual space of original contributions to either classic or emerging debates about the nature and effects of policing in society. The works in this series will advance our theoretical, methodological and/or empirical knowledge of policing in various societies across the world. It is the hope of the series editors that the works in this series will help fill gaps in our global understanding of policing and society. Criminal Futures Predictive Policing and Everyday Police Work Simon Egbert and Matthias Leese Criminal Futures Predictive Policing and Everyday Police Work Simon Egbert and Matthias Leese First published 2021 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 52 Vanderbilt Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2021 Simon Egbert and Matthias Leese The right of Simon Egbert and Matthias Leese to be identified as authors of this work has been asserted by them in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. The Open Access version of this book, available at www.taylorfrancis. com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. We acknowledge support by the Open Access Publication Fund of Technische Universität Berlin, as well as by the Center for Security Studies, ETH Zurich. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing- in- Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging- in- Publication Data A catalog record for this book has been requested ISBN: 978- 0- 367- 34926- 4 (hbk) ISBN: 978- 0- 429- 32873- 2 (ebk) Typeset in Bembo by Apex CoVantage, LLC Contents Preface viii List of figures x 1 Criminal futures 1 2 Predictive policing and its origins 19 3 The police and technology 44 4 Data and the need for speed 69 5 Humans and machines 94 6 Putting risk on the map 116 7 Patrolling risk 145 8 Does it work, though? 164 9 “Bad” predictions 186 10 The future of (predictive) policing 206 Index 226 Preface This book is the product of a windfall encounter. Each of us individually would have probably written a quite different book – or, even more likely, no book at all. We were lucky enough that our paths crossed at a workshop in Freiburg, Germany, in March of 2017. At the time, both of us had, independent of each other, only recently started to engage with predictive policing. The research outlines that both of us presented at that workshop were remarkably similar, and as it turned out during a couple of longer follow-up conversations, we were in fact interested in almost identical questions surrounding the use of algorithmic crime analysis software. There was a large overlap in the theoretical and conceptual literature that we had been reading. And we had at that point even started to interview some of the same police representatives and software developers. The decision to join forces and proceed with our research together there- fore only felt natural. Admittedly, conducting a multiyear qualitative research and writing project across a physical distance came with a set of challenges. In- person meetings were few and far between, so not only did the weekly coordination of activities have to be done via video-conferencing but also the coding and analysis of our empirical material, the interpretations of interview segments and ethnographic observations, debates about arguments, and so on. Matters were further complicated by the usual struggles of academic precarity: funding ran out, applications were written, and jobs and cities were changed. The book manuscript was finished from the confines of our homes, as the COVID-19 pandemic forced universities to shut down. Three years after we first met at the said workshop, we are, however, more than happy with how things turned out. Almost needless to say, our profes- sional relationship has turned into a friendship. And this book has offered us the opportunity to work through our empirical material at adequate length and in adequate depth. It will, so at least our modest hope, contribute some nuance to current debates about algorithms, data, and the prediction and prevention of crime. Obviously, we could not have realized this book without the support of a number of people and institutions. First and foremost, although for reasons of Preface ix anonymization we cannot do so by name, we must thank our research partici- pants for sharing their thoughts, concerns, experiences, and daily work prac- tices with us. We are aware that access to security agencies and their lifeworlds is not always easy. All the more do we appreciate the willingness of police departments and other predictive policing actors to support our research. Over the years, our work has benefited from countless conversations with colleagues, whose generous engagement with our thoughts has continuously pushed the boundaries of our project. We would like to particularly high- light the support and feedback we received from John Austin, Myriam Dunn Cavelty, Dominik Gerstner, Jens Hälterlein, Lucas Introna, Mareile Kaufmann, Anna Leander, Thomas Linder, Monique Mann, Lars Ostermeier, Bettina Paul, Nikolaus Pöchhacker, Tobias Singelnstein, Dean Wilson, Aleš Završnik, and Nils Zurawski, as well as series editors Jenny Fleming and Jennifer Wood. Last but not least, our work was to a large extent facilitated by the institu- tional support from our employers. The Universität Hamburg, the