Predictive 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 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.

Chapter “Predictive Policing in 2025: A Scenario” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Dive into the revolutionary intersection of law enforcement and cutting-edge technology with "AI in Policing: The Emergence of Predictive Analytics." This illuminating book explores the transformative role of Artificial Intelligence (AI) in shaping the landscape of modern policing, specifically through the lens of predictive analytics. From crime prevention to proactive intervention, AI has swiftly become an invaluable tool for law enforcement agencies. This book navigates the ethical considerations, challenges, and unprecedented opportunities brought forth by the fusion of AI and policing. Unravel the complex web of algorithms, machine learning, and data analysis that empowers law enforcement to forecast potential criminal activity, enhancing strategic decision-making and resource allocation. Inside "AI in Policing," readers will discover: The Evolution of Predictive Analytics: Trace the evolution of predictive analytics and its integration into the fabric of modern policing, from its nascent stages to its current advanced applications. Ethical and Legal Implications: Delve into the ethical considerations and legal ramifications of implementing AI-driven predictive technologies in law enforcement, exploring the delicate balance between security and individual rights. Success Stories and Challenges: Learn from real-world case studies showcasing the successes and challenges encountered by law enforcement agencies leveraging predictive analytics, providing insights into its efficacy and limitations. This book is a comprehensive guide, designed not only for law enforcement professionals, policymakers, and technologists but also for anyone intrigued by the dynamic fusion of AI and public safety. It offers a balanced perspective on the potential, limitations, and ethical boundaries of AI applications in the realm of policing. "AI in Policing: The Emergence of Predictive Analytics" is an essential resource for understanding the profound impact of AI on the future of law enforcement, offering a thought-provoking exploration of the evolving landscape at the convergence of technology and public safety. Prepare to embark on a thought-provoking journey that unveils the possibilities and challenges of AI-driven predictive analytics in policing. Whether you're a scholar, a professional in the field, or an individual seeking insight into the technological advancements shaping our society, this book will illuminate the path forward in law enforcement's evolving landscape. Get ready to immerse yourself in the profound transformation of law enforcement through the pages of "AI in Policing: The Emergence of Predictive Analytics." Explore the future of policing and the powerful integration of AI in reshaping security strategies and crime prevention.

Winner, 2018 Law & Legal Studies PROSE Award The consequences of big data and algorithm-driven policing and its impact on law enforcement In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence. This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool. Andrew Guthrie Ferguson reveals how these new technologies —viewed as race-neutral and objective—have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias. But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections. In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police. These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime. The Rise of Big Data Policing is a must read for anyone concerned with how technology will revolutionize law enforcement and its potential threat to the security, privacy, and constitutional rights of citizens. Read an excerpt and interview with Andrew Guthrie Ferguson in The Economist.

Predictive policing is the use of analytical techniques to identify targets for police intervention with the goal of preventing crime, solving past crimes, or identifying potential offenders and victims. These tools are not a substitute for integrated approaches to policing, nor are they a crystal ball. This guide assesses some of the most promising technical tools and tactical approaches for acting on predictions in an effective way.

This interdisciplinary volume critically explores how the ever-increasing use of automated systems is changing policing, criminal justice systems, and military operations at the national and international level. The book examines the ways in which automated systems are beneficial to society, while addressing the risks they represent for human rights. This book starts with a historical overview of how different types of knowledge have transformed crime control and the security domain, comparing those epistemological shifts with the current shift caused by knowledge produced with high-tech information technology tools such as big data analytics, machine learning, and artificial intelligence. The first part explores the use of automated systems, such as predictive policing and platform policing, in law enforcement. The second part analyzes the use of automated systems, such as algorithms used in sentencing and parole decisions, in courts of law. The third part examines the use and misuse of automated systems for surveillance and social control. The fourth part discusses the use of lethal (semi)autonomous weapons systems in armed conflicts. An essential read for researchers, politicians, and advocates interested in the use and potential misuse of automated systems in crime control, this diverse volume draws expertise from such fields as criminology, law, sociology, philosophy, and anthropology.

Master's Thesis from the year 2019 in the subject Computer Science - Theory, grade: A*, Management Center Innsbruck, language: English, abstract: This Master Thesis introduces theoretical fundamentals of Predictive Policing tools used in German police institutions such as Hot-Spot techniques, Near-Repeat approaches, Risk-terrain Analysis and Concentric-Zone Model. In times of Big Data, police work has also changed and the usage of forecasting technologies in order to prevent crime does not only vary state-wide in definitions but also in its application. Therefore, objectives and appliances are described in general. Additionally, a chronological transformation is established in order to compare lineages in Germany with those in the USA. Since Predictive Policing polarises, the research question deals with potential opportunities and challenges police institutions and the society have to deal with, when it comes to leveraging data-analytical forecasting technologies in order to prevent crime. The motivation for writing the Master Thesis about the present topic stems from the fact that it is highly current and has not yet been thoroughly studied. Preventing crime and thus ensuring a safe environment is an important field of research in our society and should be guaranteed with problem-oriented policing. Since there are varying considerations and application measures of PP according to different country side frameworks, the Thesis provides an overview about technical functioning and practical appliance within Germany. Therefore, content provides on the one hand added value for lecturers and students in the field of Public Security Management and related studies or police officers in the upper grade of the civil service. On the other hand, it serves to educate citizens about how far the technologies have progressed in this area and to what extent this will influence the lives of citizens in the future. Many police departments worldwide test software-based forecasting technologies according to their relevance in practice. Forecasting systems work with data sets about already registered crime activities. Those datasets are then complemented with socio-spatial, calendar and meteorological data. Since the amount of collected and analyzed data increases day by day, the question arises as to what extent Machine Learning and Artificial Intelligence will influence the human advice origin to predict and prevent crime.

How can the law address the constitutional challenges of the algorithmic society? This volume provides possible solutions.