Digital Transformation in Policing: The Promise, Perils and Solutions

<div> <p>Part I. Transformation in Policing: Technological Perspective</p>
<p>Introduction to Digital Policing</p>
<p>This chapter will provide an overview of Digital Policing with a main focus on the US and the UK police forces. As part of this overview, the chapter will explore how pervasive the use of technology in policing is and how day to day operations of police officers have changed compared to the past. As part of this study, the chapter aims to establish how often police follow patrols based on computer-forecast crime hot-spots, and what algorithmically based methods the police use when ranking people who are at risk of becoming victims or perpetrators of crime[1]^{, [2]}.</p> <p>An Overview of AI, Big Data and Predictive Analytics in Digital Policing</p>
<p>This chapter will provide the readers with the necessary background knowledge in relation to what Artificial Intelligence, Big Data and Predictive Analytics are and how they are used within different sectors. The chapter will then proceed to discuss how these technologies are transforming traditional policing into modern, efficient policing.</p> <p>A Study of Correlation between Big Data Policing and Reduction in Crime</p>
<p>There are currently few studies that have investigated the impact of Big Data Policing on the reductions in crime. Considering that these studies tend to be inconclusive¹ (Crime rates often relate to a number of factors such as economic and environmental forces that render it challenging to establish any underlying connection with a specific technology.), this chapter will aim to synthesise this research to determine to what extent BDP has facilitated reductions in crime.</p> <p>Part II. The Promises</p>
<p>The Use of Big Data and Predictive Analytics in Modern Policing</p>
<p>This chapter will explore how Big Data and Predictive Analytics are transforming policing around the world. In particular, the chapter will focus on how the US’ and the UK’s police forces use these technologies to predict and identify individuals who might become perpetrators or victims of crime.</p> <p>Application of Artificial Intelligence in Digital Policing</p>
This chapter will explore how Big Data and Predictive Analytics are transforming policing around the world. In particular, the chapter will focus on how the US’ and the UK’s police forces use these technologies to predict and identify individuals who might become perpetrators or victims of crime.<p>Enterprise Geographic Information System and Digital Policing</p>
<p>This chapter will examine the ways in which Enterprise GIS supports policing operations. To this end, the chapter will explore how the implementation of an Enterprise GIS platform across a police services could facilitate data-driven decision making to address important questions such as where and when communities are most at risk, and how the police mitigate these risks[3].</p> <p>Criminal Intelligence, Crime Pattern Analysis and Operational Policing</p>
<p>This chapter will investigate how intelligence analysis is used as a tool to understand crime and disorder and offer insight, clarity and context to decision makers. As part of this examination, a number of analytical techniques will be discussed including: regression and data mining techniques, GIS data mining, neural network, and support vector machine, etc. The chapter will discuss major theories of criminal behaviour such as&nbsp; routine activity theory, rational choice theory, and crime pattern theory.</p> <p>Cell Site Analysis and ANPR Mapping</p>
This chapter will discuss how law enforcement agencies deploy Cell Cire Analysis to determine the likely locations of a mobile phone using the detailed examination of historic call data records together with radio frequency network surveys. This discussion will also briefly draw upon the use Cell Site Analysis evidence by the criminal defence team as maps and schedules[4].<p>Part III. The Perils</p>
<p>The Dark Web and Digital Policing</p>
<p>This chapter will explore some of the most difficult challenges that the dark web and its marketplaces present to law enforcement agencies. Examples of these challenges will include those concerning digital forensic investigations of crimes, encryption and anonymity, etc.</p> <p>Implications of Algorithm-Driven and Person-Based Predictive Policing</p>
<p>Person-based predictive policing involves the adoption of data to detect and investigate potential suspects or victims. This chapter will explore algorithm-driven policing and its associated threats to the security, privacy, and constitutional rights of citizens. Particularly, the chapter will focus on examining person-based policing systems and the targeting of individuals. The chapter will analyse elements such as racial bias, facial recognition, a lack of transparency, data error and the distortions of constitutional protections, all of which pose significant challenges to the design and implementation of workable person-based predictive strategies^{1, 2}.</p> <p>Algorithmic Bias, Transparency and Data Error in Person-Based Predictive Policing</p>
<p>Person-based predictive policing involves the adoption of data to detect and investigate potential suspects or victims. This chapter will investigate person-based policing systems and the targeting of individuals. The chapter will analyse elements such as racial bias, facial recognition, a lack of transparency, data error and the distortions of constitutional protections, all of which pose significant challenges to the design and implementation of workable person-based predictive strategies^{1, 2}.</p> <p>Policing and Probabilistic Predictions</p>
Data can certainly enable police forces to make predictions which will assist them in expending their resources smarter. This chapter will argue that, however, a probabilistic prediction cannot be considered certainty. The chapter will go on to discuss ways through which police could harm innocent individuals when taking actions based on probabilities[5].<p>Surveillance Technologies, Human Rights and the Imbalance of Power</p>
<p>This chapter will argue that there has been an exponential growth in in web of police surveillance, thereby threatening freedoms, political expression, personal privacy and other human rights^{1, 2}.</p> <p>Part IV. The Solutions</p>
<p>Balancing Crime Reduction with Civil Rights</p>
<p>This chapter will argue that although predictive policing could have significant impact on crime reduction, simultaneously, it threatens individual’s civil liberties. The chapter observes that, as a result, care must be taken to ensure that predictive policing is deployed in a fair manner. To this end, the chapter will offer a number of practical recommendations with a view to balancing crime reduction based on predictive policing with civil rights⁵.</p><p>Tentative list of authors who might submit a chapter:</p> <p>Prof. Iain Sutherland</p>
<p>Noroff University, Norway.</p>
E: iain.sutherland@noroff.no<p></p>
<p>W: https://bit.ly/3qnOMH5</p>
<p>Professor. Mark Stamp</p>
<p>San Jose State University, USA. </p>
<p>E: mark.stamp@sjsu.edu</p>
<p>W: https://bit.ly/39BOPsG</p>
<p>Professor. Stuart Macdonald</p>
Swansea University, UK.<p></p>
<p>E: s.macdonald@swansea.ac.uk</p>
<p>W: https://bit.ly/3mOWtUF</p>
Dr. Kim-Kwang Raymond Choo<p></p>
<p>The University of Texas at San Antonio, USA.</p>
<p>E: raymond.Choo@utsa.edu</p>
<p>W: https://bit.ly/39F3qE2</p>
<p>Nick Furneaux </p>
<p>Director of CSITech Company</p>
<p>W: https://bit.ly/2VWm55X</p>
<p>Professor. Maura Conway</p>
Dublin City University, Republic of Ireland.<p></p>
<p>E: maura.conway@dcu.ie</p>
<p>W: https://bit.ly/3qpcoLq</p>
Dr. Phil Legg <p></p>
<p>University of West England, UK.</p>
<p>E: Phil.Legg@uwe.ac.uk</p>
W: https://bit.ly/37GEV6s<p></p>
<p>Dr. Simon Parkinson</p>
<p>Huddersfield University, UK.</p>
<p>E: s.parkinson@hud.ac.uk</p>
<p>W: https://bit.ly/37LYFWH</p>
<p>Professor. Peter Sommer</p>
Birmingham City University, UK.<p></p>
<p>E: peter.sommer@bcu.ac.uk</p>
<p>W: https://bit.ly/3qqCWvB</p>
Andy Cuff<p></p>
<p>Computer Network Defence Ltd, USA and UK.</p>
<p>E: andy.cuff@cndltd.com</p>
<p>W: https://www.cndltd.com/</p>
<p>&nbsp;</p>
<p>Dr. Z. Cliffe Schreuders</p>
<p>Leeds Beckett University, UK.</p>
<p>E: c.schreuders@leedsbeckett.ac.uk</p>
<p>W: https://bit.ly/39CVnHz</p>
<p>&nbsp;</p>
<p>Professor. Hamid Jahankhani </p>
<p>Northumbria University, UK. </p>
<p>E: hamid.jahankhani@northumbria.ac.uk</p>
<p>W: https://bit.ly/3gvCE25</p>
<p>Jens Kirschner</p>
Senior Computer Forensics Specialist at X-Ways Software Technology AG, Germany.<p></p>
<p>E: linkedin@jenskirschner.eu</p>
<p>W: https://bit.ly/33HoXYF</p>
<p>Lance Spitzner</p>
<p>Director, SANS Institute Founder, Honeynet Project, USA.</p>
E: lance@spitzner.net<p></p>
<p>W: https://bit.ly/3onRxWO</p>
<p>Helen Olsen Bedford</p>
<p>Publisher, UKAuthority.com &amp; UKA Live, UK.</p>
<p>E: helen@ukauthority.co.uk</p>
<p>W: https://www.ukauthority.com/</p>
<p>&nbsp;</p> <p>Responding to the Threats of Big Data and Predictive Analytics Policing </p>
<p>This chapter discusses a number of practical ways by which individual’s human rights can be protected against threats of Big Data Policing.</p>
<p>&nbsp;</p> <p>Best Practices and Guidance for Police Use of Facial Recognition </p>
<p>This chapter will focus on ways that could be adopted to address ethical, privacy and legal challenges posed by the police use of overt surveillance camera systems integrating facial recognition technology. To this end, the chapter will recommend best practices and guidance that could be considered to address these challenges.</p>
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