Machine Learning Risk Assessments In Criminal Justice Settings

This book puts in one place and in accessible form Richard Berk's most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than "predictive policing" for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.

This book puts in one place and in accessible form Richard Berk’s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than “predictive policing” for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.

Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of “future dangerousness" to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "risk assessments" of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies.

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.

Machine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship. As machine learning and AI approaches become increasingly pervasive, it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response, this book seeks to stimulate this discussion. The opening part is framed through a historical lens, with the first chapter dedicated to the origins of the relationship between AI and research on crime, refuting the "novelty narrative" that often surrounds this debate. The second presents a compact overview of the history of AI, further providing a nontechnical primer on machine learning. The following chapter reviews some of the most important trends in computational criminology and quantitatively characterizing publication patterns at the intersection of AI and criminology, through a network science approach. This book also looks to the future, proposing two goals and four pathways to increase the positive societal impact of algorithmic systems in research on crime. The sixth chapter provides a survey of the methods emerging from the integration of machine learning and causal inference, showcasing their promise for answering a range of critical questions. With its transdisciplinary approach, Machine Learning for Criminology and Crime Research is important reading for scholars and students in criminology, criminal justice, sociology, and economics, as well as AI, data sciences and statistics, and computer science.

This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."

Punishment policies and practices in the United States today are unprincipled, chaotic, and much too often unjust. The financial costs are enormous. The moral cost is greater: countless individual injustices, mass incarceration, the world's highest imprisonment rate, extreme disparities, especially affecting members of racial and ethnic minority groups, high rates of wrongful conviction, assembly line case processing, and a general absence of respectful consideration of offenders' interests, circumstances, and needs. In Doing Justice, Preventing Crime, Michael Tonry lays normative and empirical foundations for building new, more just, and more effective systems of sentencing and punishment in the twenty-first century. The overriding goals are to treat people convicted of crimes justly, fairly, and even-handedly; to take sympathetic account of the circumstances of peoples' lives; and to punish no one more severely than he or she deserves. Drawing on philosophy and punishment theory, this book explains the structural changes needed to uphold the rule of law and its requirement that the human dignity of every person be respected. In clear and engaging prose, Michael Tonry surveys what is known about the deterrent, incapacitative, and rehabilitative effects of punishment, and explains what needs to be done to move from an ignoble present to a better future.

This book examines the nature, prevention, and treatment of violence within families. It reviews the definition of contemporary families, emphasizing various structures, including nuclear families, reconstituted families, gay and lesbian families, and recent immigrant families. In addition, the volume describes the nature of and risk factors for family violence from the perspectives of both victims (e.g., infants, children, seniors) and perpetrators (e.g., adolescent family members, women). It identifies the implications and explores strategies for prevention, treatment, and services. In addition, the volume directly addresses practice and evidence-based interventions for individual perpetrators, family interventions, interventions for victims and systemwide interventions (e.g., those involving the courts, police, and national policy). Chapters review the best available quality evidence from randomized controlled trials, systematic reviews, meta-analyses, research syntheses, and evidence-based recommendations from expert panels and government agencies. Case studies illustrate the application of evidence-based practice to violence within the family to demonstrate the effectiveness of the intervention. Topics featured in this book include: Definition and conceptualization of family. Definition and measurement of as well as risk factors for family violence. Family violence in various traditional and nontraditional families. Prevention strategies as well as Individual and family treatments for perpetrators and victims of family violence. Social policy and legal interventions for family violence. Violence in Families is a must-have resource for researchers, professors, and graduate students as well as clinicians, therapists, and other professionals in developmental psychology, family studies, forensic psychology, criminology/criminal justice, public health, psychotherapy/counseling, psychiatry, social work, educational policy and politics, health psychology, nursing, and behavioral therapy/rehabilitation.