The judiciary is in the early stages of a transformation in which AI (Artificial Intelligence) technology will help to make the judicial process faster, cheaper, and more predictable without compromising the integrity of judges' discretionary reasoning. Judicial decision-making is an area of daunting complexity, where highly sophisticated legal expertise merges with cognitive and emotional competence. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has had two more practical goals: producing tools to support judicial activities, including programs for intelligent document assembly, case retrieval, and support for discretionary decision-making; and developing new analytical tools for understanding and modeling the judicial process, such as case-based reasoning and formal models of dialectics, argumentation, and negotiation. Judges, squeezed between tightening budgets and increasing demands for justice, are desperately trying to maintain the quality of their decision-making process while coping with time and resource limitations. Flexible AI tools for decision support may promote uniformity and efficiency in judicial practice, while supporting rational judicial discretion. Similarly, AI may promote flexibility, efficiency and accuracy in other judicial tasks, such as drafting various judicial documents. The contributions in this volume exemplify some of the directions that the AI transformation of the judiciary will take.

The availability of very large data sets and the increase in computing power to process them has led to a renewed intensity in corporate and governmental use of Artificial Intelligence (AI) technologies. This groundbreaking book, the first devoted entirely to the growing presence of AI in the legal profession, responds to the necessity of building up a discipline that due to its novelty requires the pooling of knowledge and experiences of well-respected experts in the AI field, taking into account the impact of AI on the law and legal practice. Essays by internationally known expert authors introduce the essentials of AI in a straightforward and intelligible style, offering jurists as many practical examples and business cases as possible so that they are able to understand the real application of this technology and its impact on their jobs and lives. Elements of the analysis include the following: crucial terms: natural language processing, machine learning and deep learning; regulations in force in major jurisdictions; ethical and social issues; labour and employment issues, including the impact that robots have on employment; prediction of outcome in the legal field (judicial proceedings, patent granting, etc.); massive analysis of documents and identification of patterns from which to derive conclusions; AI and taxation; issues of competition and intellectual property; liability and responsibility of intelligent systems; AI and cybersecurity; AI and data protection; impact on state tax revenues; use of autonomous killer robots in the military; challenges related to privacy; the need to embrace transparency and sustainability; pressure brought by clients on prices; minority languages and AI; danger that the existing gap between large and small businesses will further increase; how to avoid algorithmic biases when AI decides; AI application to due diligence; AI and non-disclosure agreements; and the role of chatbots. Interviews with pioneers in the field are included, so readers get insights into the issues that people are dealing with in day-to-day actualities. Whether conceiving AI as a transformative technology of the labour market and training or an economic and business sector in need of legal advice, this introduction to AI will help practitioners in tax law, labour law, competition law and intellectual property law understand what AI is, what it serves, what is the state of the art and the potential of this technology, how they can benefit from its advantages and what are the risks it presents. As the global economy continues to suffer the repercussions of a framework that was previously fundamentally self-regulatory, policymakers will recognize the urgent need to formulate rules to properly manage the future of AI.

The judiciary is in the early stages of a transformation in which AI (Artificial Intelligence) technology will help to make the judicial process faster, cheaper, and more predictable without compromising the integrity of judges' discretionary reasoning. Judicial decision-making is an area of daunting complexity, where highly sophisticated legal expertise merges with cognitive and emotional competence. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has had two more practical goals: producing tools to support judicial activities, including programs for intelligent document assembly, case retrieval, and support for discretionary decision-making; and developing new analytical tools for understanding and modeling the judicial process, such as case-based reasoning and formal models of dialectics, argumentation, and negotiation. Judges, squeezed between tightening budgets and increasing demands for justice, are desperately trying to maintain the quality of their decision-making process while coping with time and resource limitations. Flexible AI tools for decision support may promote uniformity and efficiency in judicial practice, while supporting rational judicial discretion. Similarly, AI may promote flexibility, efficiency and accuracy in other judicial tasks, such as drafting various judicial documents. The contributions in this volume exemplify some of the directions that the AI transformation of the judiciary will take.

This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.

'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.

This book discusses issues relating to the application of AI and computational modelling in criminal proceedings from a European perspective. Part one provides a definition of the topics. Rather than focusing on policing or prevention of crime – largely tackled by recent literature – it explores ways in which AI can affect the investigation and adjudication of crime. There are two main areas of application: the first is evidence gathering, which is addressed in Part two. This section examines how traditional evidentiary law is affected by both new ways of investigation – based on automated processes (often using machine learning) – and new kinds of evidence, automatically generated by AI instruments. Drawing on the comprehensive case law of the European Court of Human Rights, it also presents reflections on the reliability and, ultimately, the admissibility of such evidence. Part three investigates the second application area: judicial decision-making, providing an unbiased review of the meaning, benefits, and possible long-term effects of ‘predictive justice’ in the criminal field. It highlights the prediction of both violent behaviour, or recidivism, and future court decisions, based on precedents. Touching on the foundations of common law and civil law traditions, the book offers insights into the usefulness of ‘prediction’ in criminal proceedings.

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."