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 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 Routledge Handbook of Homicide Studies comprehensively examines the topic of homicide from a diverse collection of perspectives and backgrounds. It brings together original contributions on homicide, with a focus on the broad range of impacts of homicide from a multitude of disciplines that evaluate and examine homicide in actual practice and theory. The editors have assembled a comprehensive collection highlighting the multifaceted causes and ramifications of homicide both across the United States and globally, with chapters exploring the current state of homicide, typologies of homicides offenders, causes and correlates of homicide, homicides and the criminal justice system, and a professional observations chapters authored by some of the leading practicing professionals in the world, many of whom have made pivotal contributions to the evaluation and investigation of homicide offenders and cases. Providing state-of-the-art scholarship on homicide in modern society, this handbook is a key collection and an invaluable resource for students, researchers, and practitioners engaged in the study of homicide across a diverse range of disciplines, including criminal justice and criminology, psychology, sociology, forensics, interdisciplinary departments, and sociolegal studies.

This book advances a theoretically informed realist criminology of computer crime. Looking beyond current strategies of online crime control, this book argues for a new sort of policy that addresses the root causes of computer crime and criminality, reduces the harms experienced by the victims of such crimes, and does not unduly contribute to state and corporate power and surveillance. Drawing both on the proponents of realist criminology and on those who have leveled critiques of the approach, Steinmetz illustrates the contours of a realist criminology of computer crime by considering definitions of harm with online crime, the idiosyncrasies of online locality and community, the social relations of computer crime, the tension between piecemeal reform and structural changes, and other matters. Furthermore, Steinmetz surveys the methodological dimensions of computer crime research, offers a critique of positivist “computational criminology,” and posits an agenda for computer crime policy. Against Cybercrime is an essential reading for all those engaged with cybercrime, realist criminology, criminological theory, and social harm online.

As scholarly work on crime, deviance, criminal justice, and social control advances and sophisticated methods of investigation develop, chapter authors demonstrate the methodological maturity and diversity of current empirical research in criminology and criminal justice.

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints

Discussing social media-related scholarship found in criminology, legal studies, policing, courts, corrections, victimization, and crime prevention, this book presents the current state of our knowledge on the impact of social media and the major sociological frameworks employed to study the U.S. justice system. Building a theoretical framework for the study of social media and criminal justice in each chapter, the chapters provide a systematic reflection of extant research on social media in cybercrime, operations of courts, administration of institutional and community corrections, law enforcement, and crime prevention. The book fills the gap between the contemporary state of knowledge regarding social media and criminal justice with respect to both empirical evidence and types of sociological frameworks being employed to explore and identify the societal costs and benefits of our growing dependence upon social media. In addition to providing an up-to-date overview of our current state of knowledge, this book highlights important areas of future research, wherein the benefits of social media can be expanded and the negative aspects of its broadening use can be minimized. Social Media and Criminal Justice will be of interest to students, scholars and practitioners in the areas of judicial administration, corrections management, law enforcement, and criminal justice-engaged community-based nonprofit organizations involved in court-referred treatment and/or active collaboration with local law enforcement agencies.

The Handbook on Risk and Need Assessment: Theory and Practice covers risk assessments for individuals being considered for parole or probation. Evidence-based approaches to such decisions help take the emotion and politics out of community corrections. As the United States begins to back away from ineffective, expensive policies of mass incarceration, this handbook will provide the resources needed to help ensure both public safety and the effective rehabilitation of offenders. The ASC Division on Corrections & Sentencing Handbook Series will publish volumes on topics ranging from violence risk assessment to specialty courts for drug users, veterans, or the mentally ill. Each thematic volume focuses on a single topical issue that intersects with corrections and sentencing research.

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 book presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.