Criminal Justice Forecasts of Risk


Book Description

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.




Machine Learning Risk Assessments in Criminal Justice Settings


Book Description

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.




The Encyclopedia of Research Methods in Criminology and Criminal Justice, 2 Volume Set


Book Description

The Encyclopedia of RESEARCH METHODS IN CRIMINOLOGY & CRIMINAL JUSTICE The most comprehensive reference work on research designs and methods in criminology and criminal justice This Encyclopedia of Research Methods in Criminology and Criminal Justice offers a comprehensive survey of research methodologies and statistical techniques that are popular in criminology and criminal justice systems across the globe. With contributions from leading scholars and practitioners in the field, it offers a clear insight into the techniques that are currently in use to answer the pressing questions in criminology and criminal justice. The Encyclopedia contains essential information from a diverse pool of authors about research designs grounded in both qualitative and quantitative approaches. It includes information on popular datasets and leading resources of government statistics. In addition, the contributors cover a wide range of topics such as: the most current research on the link between guns and crime, rational choice theory, and the use of technology like geospatial mapping as a crime reduction tool. This invaluable reference work: Offers a comprehensive survey of international research designs, methods, and statistical techniques Includes contributions from leading figures in the field Contains data on criminology and criminal justice from Cambridge to Chicago Presents information on capital punishment, domestic violence, crime science, and much more Helps us to better understand, explain, and prevent crime Written for undergraduate students, graduate students, and researchers, The Encyclopedia of Research Methods in Criminology and Criminal Justice is the first reference work of its kind to offer a comprehensive review of this important topic.




Risk Terrain Modeling


Book Description

Imagine using an evidence-based risk management model that enables researchers and practitioners alike to analyze the spatial dynamics of crime, allocate resources, and implement custom crime and risk reduction strategies that are transparent, measurable, and effective. Risk Terrain Modeling (RTM) diagnoses the spatial attractors of criminal behavior and makes accurate forecasts of where crime will occur at the microlevel. RTM informs decisions about how the combined factors that contribute to criminal behavior can be targeted, connections to crime can be monitored, spatial vulnerabilities can be assessed, and actions can be taken to reduce worst effects. As a diagnostic method, RTM offers a statistically valid way to identify vulnerable places. To learn more, visit http://www.riskterrainmodeling.com and begin using RTM with the many free tutorials and resources.




Predictive Policing


Book Description

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.




Predictive Policing and Artificial Intelligence


Book Description

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.




Juvenile Crime, Juvenile Justice


Book Description

Even though youth crime rates have fallen since the mid-1990s, public fear and political rhetoric over the issue have heightened. The Columbine shootings and other sensational incidents add to the furor. Often overlooked are the underlying problems of child poverty, social disadvantage, and the pitfalls inherent to adolescent decisionmaking that contribute to youth crime. From a policy standpoint, adolescent offenders are caught in the crossfire between nurturance of youth and punishment of criminals, between rehabilitation and "get tough" pronouncements. In the midst of this emotional debate, the National Research Council's Panel on Juvenile Crime steps forward with an authoritative review of the best available data and analysis. Juvenile Crime, Juvenile Justice presents recommendations for addressing the many aspects of America's youth crime problem. This timely release discusses patterns and trends in crimes by children and adolescentsâ€"trends revealed by arrest data, victim reports, and other sources; youth crime within general crime; and race and sex disparities. The book explores desistanceâ€"the probability that delinquency or criminal activities decrease with ageâ€"and evaluates different approaches to predicting future crime rates. Why do young people turn to delinquency? Juvenile Crime, Juvenile Justice presents what we know and what we urgently need to find out about contributing factors, ranging from prenatal care, differences in temperament, and family influences to the role of peer relationships, the impact of the school policies toward delinquency, and the broader influences of the neighborhood and community. Equally important, this book examines a range of solutions: Prevention and intervention efforts directed to individuals, peer groups, and families, as well as day care-, school- and community-based initiatives. Intervention within the juvenile justice system. Role of the police. Processing and detention of youth offenders. Transferring youths to the adult judicial system. Residential placement of juveniles. The book includes background on the American juvenile court system, useful comparisons with the juvenile justice systems of other nations, and other important information for assessing this problem.




Prisoner Reentry in the Era of Mass Incarceration


Book Description

Understanding and Improving Prisoner Reentry Outcomes Prisoner Reentry is an engaging and comprehensive examination of prisoner reentry and how to improve public safety, well-being, and justice in the “era of mass incarceration.” Renowned authors Daniel P. Mears and Joshua C. Cochran investigate historical trends in incarceration and punishment policy, the salience of in-prison and post-prison contexts and experiences for reentry, and the importance of understanding group differences in offending, punishment, and social context. Using extensive reliance on both theory and empirical research, the authors identify how reentry reflects criminal justice policy in America and, at the same time, has profound implications for crime prevention and justice. Readers will develop a diverse foundation for current policies, identify the implications of reentry for families, community, and society at large, and gain a conceptual and empirical toolkit for analyzing and improving the lives of those released from prison.




ECML PKDD 2020 Workshops


Book Description

This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.




Targeting Domestic Abuse with Police Data


Book Description

This book explores the potential of domestic abuse data to assess the level of harm caused to victims and the amount of resources required to respond to it. Policing domestic abuse has become a major activity for the police service in England and Wales. Part of the police strategy is to gather hundreds of thousands of detailed records about victims and suspects – the single largest set of domestic abuse records available, but one that to date has largely unexplored by researchers. In this volume, Matthew Bland and Barak Ariel analyse three substantial datasets taken from police forces across the country and ask: · Can police data be used to derive meaningful insight? · How should we use these data to measure harm? · Just how much domestic abuse involves a repeat victim? · Does abuse get more serious over time? · Can serious domestic abuse be predicted before it occurs? This volume illustrates the scale of the challenge the police and other agencies face with reducing domestic abuse. A small proportion of individuals generate a majority of harm; this book argues that police records offer opportunities to identify these individuals before the harm occurs. Demonstrating that statistical techniques can be used to profile domestic abuse to target harm reduction strategies more precisely and even identify a sizable proportion of serious cases before they occur, this volume will be of interest to law enforcement officials, policing researchers, and policy makers interested in reducing the phenomenon of domestic abuse.