Predictive Sentencing addresses the role of risk assessment in contemporary sentencing practices. Predictive sentencing has become so deeply ingrained in Western criminal justice decision-making that despite early ethical discussions about selective incapacitation, it currently attracts little critique. Nor has it been subjected to a thorough normative and empirical scrutiny. This is problematic since much current policy and practice concerning risk predictions is inconsistent with mainstream theories of punishment. Moreover, predictive sentencing exacerbates discrimination and disparity in sentencing. Although structured risk assessments may have replaced 'gut feelings', and have now been systematically implemented in Western justice systems, the fundamental issues and questions that surround the use of risk assessment instruments at sentencing remain unresolved. This volume critically evaluates these issues and will be of great interest to scholars of criminal justice and criminology.

From random security checks at airports to the use of risk assessment in sentencing, actuarial methods are being used more than ever to determine whom law enforcement officials target and punish. And with the exception of racial profiling on our highways and streets, most people favor these methods because they believe they’re a more cost-effective way to fight crime. In Against Prediction, Bernard E. Harcourt challenges this growing reliance on actuarial methods. These prediction tools, he demonstrates, may in fact increase the overall amount of crime in society, depending on the relative responsiveness of the profiled populations to heightened security. They may also aggravate the difficulties that minorities already have obtaining work, education, and a better quality of life—thus perpetuating the pattern of criminal behavior. Ultimately, Harcourt shows how the perceived success of actuarial methods has begun to distort our very conception of just punishment and to obscure alternate visions of social order. In place of the actuarial, he proposes instead a turn to randomization in punishment and policing. The presumption, Harcourt concludes, should be against prediction.

'Is it fair for a judge to increase a defendant's prison time on the basis of an algorithmic score that predicts the likelihood that he will commit future crimes? Many states now say yes, even when the algorithms they use for this purpose have a high error rate, a secret design, and a demonstratable racial bias. The former federal judge Katherine Forrest, in her short but incisive When Machines Can Be Judge, Jury, and Executioner, says this is both unfair and irrational ...' See full reviewJed S RakoffUnited States District Judge for the Southern District of New YorkNew York Review of Books This book explores justice in the age of artificial intelligence. It argues that current AI tools used in connection with liberty decisions are based on utilitarian frameworks of justice and inconsistent with individual fairness reflected in the US Constitution and Declaration of Independence. It uses AI risk assessment tools and lethal autonomous weapons as examples of how AI influences liberty decisions. The algorithmic design of AI risk assessment tools can and does embed human biases. Designers and users of these AI tools have allowed some degree of compromise to exist between accuracy and individual fairness.Written by a former federal judge who lectures widely and frequently on AI and the justice system, this book is the first comprehensive presentation of the theoretical framework of AI tools in the criminal justice system and lethal autonomous weapons utilized in decision-making. The book then provides a comprehensive explanation as to why, tracing the evolution of the debate regarding racial and other biases embedded in such tools. No other book delves as comprehensively into the theory and practice of AI risk assessment tools.

The first collective work devoted exclusively to the ethical and penal theoretical considerations of the use of artificial intelligence at sentencing Is it morally acceptable to use artificial intelligence (AI) in the determination of sentences on those who have broken the law? If so, how should such algorithms be used--and what are the consequences? Jesper Ryberg and Julian V. Roberts bring together leading experts to answer these questions. Sentencing and Artificial Intelligence investigates to what extent, and under which conditions, justice and the social good may be promoted by allocating parts of the most important task of the criminal court--that of determining legal punishment--to computerized sentencing algorithms. The introduction of an AI-based sentencing system could save significant resources and increase consistency across jurisdictions. But it could also reproduce historical biases, decrease transparency in decision-making, and undermine trust in the justice system. Dealing with a wide-range of pertinent issues including the transparency of algorithmic-based decision-making, the fairness and morality of algorithmic sentencing decisions, and potential discrimination as a result of these practices, this volume offers avaluable insight on the future of sentencing.