Table Of ContentPredictive Policing and Artificial
Intelligence
This edited text draws together the insights of numerous worldwide eminent
academics to evaluate the condition of predictive policing and artificial intelli-
gence (AI) as interlocked policy areas. Predictive and AI technologies are growing
in prominence and at an unprecedented rate. Powerful digital crime mapping
tools are being used to identify crime hotspots in real-t ime, as pattern-m atching
and search algorithms are sorting through huge police databases populated by
growing volumes of data in an effort to identify people liable to experience
(or commit) crime, places likely to host it, and variables associated with its
solvability. Facial and vehicle recognition cameras are locating criminals as they
move, while police services develop strategies informed by machine learning
and other kinds of predictive analytics. Many of these innovations are features
of modern policing in the UK, the US and Australia, among other jurisdictions.
AI promises to reduce unnecessary labour, speed up various forms of police
work, encourage police forces to more efficiently apportion their resources, and
enable police officers to prevent crime and protect people from a variety of future
harms. However, the promises of predictive and AI technologies and innovations
do not always match reality. They often have significant weaknesses, come at a con-
siderable cost and require challenging trade-o ffs to be made. Focusing on the UK,
the US and Australia, this book explores themes of choice architecture, decision-
making, human rights, accountability and the rule of law, as well as future uses of
AI and predictive technologies in various policing contexts. The text contributes
to ongoing debates on the benefits and biases of predictive algorithms, big data
sets, machine learning systems, and broader policing strategies and challenges.
Written in a clear and direct style, this book will appeal to students and
scholars of policing, criminology, crime science, sociology, computer science,
cognitive psychology and all those interested in the emergence of AI as a fea-
ture of contemporary policing.
John L.M. McDaniel teaches and researches within the University of
Wolverhampton Department of Social Science, Inclusion and Public Protection
and is an active member of the University’s Law Research Centre. He focuses
on issues of police accountability, corruption, human rights, and international
cooperation and security.
Ken G. Pease is a Professor in Policing at the University of Derby. He has
written numerous books on policing, psychology and crime science.
Routledge Frontiers of Criminal Justice
Violence Against Children in the Criminal Justice System
Global Perspectives on Prevention
Edited by Wendy O’Brien Cédric Foussard
The Development of Transnational Policing
Past, Present and Future
Edited by John L.M. McDaniel, Karlie E. Stonard and David J. Cox
‘Crossover’ Children in the Youth Justice and Child Protection
Systems
Susan Baidawi and Rosemary Sheehan
Critical Reflections on Evidence- Based Policing
Edited by Nigel Fielding, Karen Bullock and Simon Holdaway
Policing and Mental Health
Theory, Policy and Practice
Edited by John L.M. McDaniel, Kate Moss and Ken G. Pease
The Making of a Police Officer
Comparative Perspectives on Police Education and Recruitment
Edited by Edited by Tore Bjørgo and Marie- Louise Damen
Police Integrity in South Africa
Sanja Kutnjak Ivkovich, Adri Sauerman, Andrew Faull, Michael E. Meyer and
Gareth Newham
Emotional Labour in Criminal Justice and Criminology
Edited by Jake Phillips, Chalen Westaby, Andrew Fowler and Jaime Waters
Predictive Policing and Artificial Intelligence
Edited by John L.M. McDaniel and Ken G. Pease
For more information about this series, please visit: www.routledge.com/
Routledge- Frontiers- of- Criminal- Justice/ book- series/ RFCJ
Predictive Policing and
Artificial Intelligence
Edited by John L.M. McDaniel and
Ken G. Pease
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 selection and editorial matter, John L.M. McDaniel and Ken G. Pease;
individual chapters, the contributors
The right of John L.M. McDaniel and Ken G. Pease to be identifi ed as the authors of the
editorial material, and of the authors for their individual chapters, has been asserted in
accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced or utilised
in any form or by any electronic, mechanical, or other means, now known or
hereafter invented, including photocopying and recording, or in any information
storage or retrieval system, without permission in writing from the publishers.
Trademark notice : Product or corporate names may be trademarks or registered trademarks,
and are used only for identifi cation 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 has been requested for this book
ISBN: 9780367210984 (hbk)
ISBN: 9780429265365 (ebk)
Typeset in Bembo
by Newgen Publishing UK
For Ponniah and Balasoundari Jeyendran
Contents
List of illustrations ix
Foreword by James Sheptycki x
List of contributors xiv
Introduction 1
JOHN L.M. MCDANIEL AND KEN G. PEASE
PART I
Bias and big data 39
1 The future of AI in policing: exploring the sociotechnical
imaginaries 41
JANET CHAN
2 Predictive policing through risk assessment 58
MELISSA HAMILTON
3 Policing, AI and choice architecture 79
JOHN L.M. MCDANIEL AND KEN G. PEASE
4 What big data in health care can teach us about predictive
policing 111
I. GLENN COHEN AND HARRY GRAVER
5 Artificial intelligence and online extremism: challenges and
opportunities 132
MIRIAM FERNANDEZ AND HARITH ALANI
6 Predictive policing and criminal law 163
MANUEL A. UTSET
viii Contents
PART II
Police accountability and human rights 183
7 Accountability and indeterminacy in predictive policing 185
AARON SHAPIRO
8 Machine learning predictive algorithms and the policing
of future crimes: governance and oversight 214
ALEXANDER BABUTA AND MARION OSWALD
9 ‘Algorithmic impropriety’ in UK policing contexts: a
developing narrative? 237
JAMIE GRACE
10 Big data policing: governing the machines? 254
MICHAEL ROWE AND RICK MUIR
11 Decision- making: using technology to enhance learning in
police officers 269
PAMELA RICHARDS, DEBBIE ROBERTS AND MARK BRITTON
Conclusion 289
JOHN L.M. MCDANIEL AND KEN G. PEASE
Index 298
Illustrations
Figures
1.1 Components of AI, reproduced with permission from ACOLA
(2019) 43
1.2 The prediction- led policing business process (from Perry et al.,
2013) 49
5.1 Overview of AI approaches to counter online radicalisation 133
5.2 Main challenges of the development of AI applications to
counter radicalisation 144
5.3 Mechanisms used for data collection, verification and publication 145
5.4 Problems with existing datasets to study radicalisation 147
Tables
5.1 Approaches that focus on the analysis of online radicalisation 135
5.2 Approaches that focus on the detection of online radicalisation 139
5.3 Approaches that focus on the prediction of online radicalisation 143
