Predictive Policing and Artificial Intelligence

✍ Scribed by John L.M. McDaniel, Ken G. Pease

• Publisher: Routledge
• Year: 2021
• Tongue: Russian
• Leaves: 331
• Series: Routledge Frontiers of Criminal Justice
• Category: Library

✦ Synopsis

This edited text draws together the insights of numerous worldwide eminent academics to evaluate the condition of predictive policing and artificial intelligence (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-time, as pattern-matching and search algorithms are sorting through huge police databases populated by growing volumes of data in an eff ort 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 considerable cost and require challenging trade- off s 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 feature of contemporary policing.

✦ Table of Contents

Cover
Half Title
Series Information
Title Page
Copyright Page
Dedication
Table of contents
Illustrations
Foreword
Contributors
Introduction
Two extremes
A challenging environment for police forces
What is predictive policing?
The ‘predictive’ part
Environmental criminology and crime science
Artificial intelligence
The ‘policing’ part
AI in policing
Structure of book
Part I: Bias and big data
Part II: Police accountability and human rights
References
Part I Bias and Big Data
Chapter 1 The future of AI in policing: Exploring the sociotechnical imaginaries
Introduction
1. Sociotechnical imaginaries
2. The benefits and risks of AI for society
Definition of AI
The benefits of AI
The risks of AI
Technical limitations
Data-driven biases
Trust
3. Using AI in policing
A utopian view
A social science view
Assumptions
Evaluation
Accountability
A data science view
A civil rights community view
Conclusion
Notes
References
Chapter 2 Predictive policing through risk assessment
Introduction
Projected benefits of predictive policing with individual risk
Examples of predictive policing tools with individual risk
Contentious issues with individual risk prediction
Entry points for biases in predictive policing algorithms
Label bias
Feature selection
Sample bias
Feedback loop
Future prospects for predictive policing
Conclusions
References
Chapter 3 Policing, AI and choice architecture
Introduction
The ubiquity of choice architecture
Policing and choice architecture
AI and choice architecture
AI as a product of choice architects
AI technologies as choice architects
Choice architects within police organisations
Conclusion
References
Chapter 4 What big data in health care can teach us about predictive policing
Introduction
Part I
Predictive analytics in policing and health care
Predictive policing
Health care
Part II
The professions in dialogue
Practitioners
Role disruption
Automation bias and discretion
Policymakers
The duty of explanation
Transparency and trade secrets
Scarcity and the inevitability of distributional choices
The polity
Bias and equality
Privacy
Conclusion
Acknowledgement
Notes
References
Chapter 5 Artificial intelligence and online extremism: Challenges and opportunities
Introduction
An overview of existing approaches
Analysis
Detection
Prediction
Challenges
Defining radicalisation
Data collection, verification and publication
Noisy data (false positives)
Biases
Incompleteness
Heterogeneity (variety of content)
Irreproducibility
Research methodologies
Lack of comparison against a control group
Lack of comparison across approaches
Lack of cooperation across research fields
Adaptation of extremist groups
Ethics and conflicts in legislation
Opportunities
Collaboration across research disciplines and organisations
Creation of reliable datasets to study radicalisation
Comparative studies
Contextual adaptation of technological solutions
Better integration of humans and technology
Ethical vigilance
Conclusions
References
Chapter 6 Predictive policing and criminal law
Introduction
Part I: Crime prevention and law enforcement
A. Rational offenders and the expected benefits and costs of crime
B. Punishment-focused deterrence
C. Police-focused deterrence
D. Long-term and short-term deterrence
E. Real-time policing and enforcing the criminal law
Part II: Machine predictions and policing
A. Machine learning: a brief overview
B. Place-based predictions
C. Person-based predictive policing
D. Real-time situational awareness technologies
Part III: Real-time policing and crime prevention
A. Inchoate and corollary crimes
B. Precommitment and credible law enforcement policies
C. Real-time policing, salient signals and deterrence
Myopic offenders
Self-control problems and time-inconsistent misconduct
Perceptual deterrence and ‘erroneous crimes’
Learning from crime and serial offenders
D. Real-time intervention
The projection bias and hot-state crimes
Risky crimes
Part IV: The social costs of relying on machine predictions in policing
A. Fairness and accuracy
B. Machine predictions and indirect, non-transparent deterrence
C. Switching to more serious crimes under a proactive predictive policing regime
D. Machine predictions and police judgements
E. The costs of proactive deterrence policies
Conclusion
References
Part II Police accountability and human rights
Chapter 7 Accountability and indeterminacy in predictive policing
Introduction
Police, accountability, transparency and reform
Three accountabilities
Algorithmic indeterminacy
Towards a police accountability frame for the age of AI
References
Chapter 8 Machine learning predictive algorithms and the policing of future crimes: Governance and oversight
Introduction
Functions of the police in England and Wales under the common law
‘Austerity AI’ and the problem of prioritisation
‘In accordance with law’
Discretion in police decision-making
Impact on rights
Safeguards, governance and oversight
Conclusion
Notes
References
Chapter 9 ‘Algorithmic impropriety’ in UK policing contexts: A developing narrative?
Introduction
Algorithms in the UK public sector
The Gangs Matrix case study
The West Midlands case study
Legal points on algorithmic or predictive policing tools
Data scope issues
Process issues
Issues of human rights impacts
Conclusions
References
Chapter 10 Big data policing: Governing the machines?
Introduction
The problem of governance
The problem of privacy
The problem of bias
Conclusion
References
Chapter 11 Decision-making: Using technology to enhance learning in police officers
Introduction
Artificial intelligence
The demands of modern-day policing
Training and upskilling the next generation of officers
Contextualising learning
Developing a personalised reflective learning environment for policing using technology
Policing exemplar #1
Created immersive learning environments
Policing exemplar #2
Responsive immersive learning environment: application of the decision-making framework
Policing exemplar #3
Summary and future directions
References
Conclusion
References
Index