The Rise Of Big Data Policing

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.

Predict and Surveil offers an unprecedented, inside look at how police use big data and new surveillance technologies. Sarah Brayne conducted years of fieldwork with the LAPD--one of the largest and most technically advanced law enforcement agencies in the world-to reveal the unmet promises and very real perils of police use of data--driven surveillance and analytics.

A comprehensive collection on police and policing, written by experts in political theory, sociology, criminology, economics, law, public health, and critical theory.

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.

Offers one hundred rules that every first year law student should live by “Dear Law Student: Here’s the truth. You belong here.” Law professor Andrew Ferguson and former student Jonathan Yusef Newton open with this statement of reassurance in The Law of Law School. As all former law students and current lawyers can attest, law school is disorienting, overwhelming, and difficult. Unlike other educational institutions, law school is not set up simply to teach a subject. Instead, the first year of law school is set up to teach a skill set and way of thinking, which you then apply to do the work of lawyering. What most first-year students don’t realize is that law school has a code, an unwritten rulebook of decisions and traditions that must be understood in order to succeed. The Law of Law School endeavors to distill this common wisdom into one hundred easily digestible rules. From self-care tips such as “Remove the Drama,” to studying tricks like “Prepare for Class like an Appellate Argument,” topics on exams, classroom expectations, outlining, case briefing, professors, and mental health are all broken down into the rules that form the hidden law of law school. If you don’t have a network of lawyers in your family and are unsure of what to expect, Ferguson and Newton offer a forthright guide to navigating the expectations, challenges, and secrets to first-year success. Jonathan Newton was himself such a non-traditional student and now shares his story as a pathway to a meaningful and positive law school experience. This book is perfect for the soon-to-be law school student or the current 1L and speaks to the growing number of first-generation law students in America.

What's wrong with targeted advertising in political campaigns? Should we be worried about echo chambers? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges. It brings together original research on the philosophy of big data and democracy from leading international authors, with recent examples - including the 2016 Brexit Referendum, the Leveson Inquiry and the Edward Snowden leaks. And it asks whether an ethical compass is available or even feasible in an ever more digitised and monitored world.

In the investigation Exploring the Boundaries of Big Data The Netherlands Scientific Council for Government Policy (WRR) offers building blocks for developing a regulatory approach to Big Data.

This book puts forward a comprehensive criminology of disaster by drawing - and building - upon existing theories which attempt to explain disaster crime. Although antisocial behaviour in disasters has long been viewed as a rarity, the authors present ample evidence that a variety of crime occurs in the wake of disaster. Frailing and Harper's explorations of property crime, interpersonal violence and fraud during disaster reveal the importance of methodological approaches to understanding these phenomena. They highlight the need for the application of social disorganization, routine activity and general strain theories of crime in the development of disaster crime prevention strategies. An accessible and detailed study, this book will have particular appeal for both students and scholars of criminology, sociology, disaster studies and emergency management.