L. Karl Branting – författare

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.

Few areas of human expertise are so well understood that they can be completely reduced to general principles. Similarly, there are few domains in which experience is so extensive that every new problem precisely matches a previous problem whose solution is known. When neither rules nor examples are individually sufficient, problem-solving expertise depends on integrating both. This book presents a computational framework for the integration of rules and cases for analytic tasks typified by legal analysis. The book uses the framework for integrating cases and rules as a basis for a new model of legal precedents. This model explains how the theory under which a case is decided controls the case's precedential effect. The framework for integrating rules and cases is implemented in GREBE, a system for legal analysis. The book presents techniques for representing, indexing, and comparing complex cases and for converting justification structures based on rules and case into natural-language text. This book will interest researchers in artificial intelligence, particularly those involved in case-based reasoning, artificial intelligence and law, and formal models of argumentation, and to scholars in legal philosophy, jurisprudence, and analogical reasoning.

Millions of individuals worldwide struggle to understand and assert their legal rights without legal representation. Equalizing Justice examines how AI and other technologies can address this access to justice crisis by providing unrepresented litigants with knowledge and skills traditionally available only through lawyers. This volume takes a needs-first approach, identifying tasks that unrepresented litigants must complete and mapping specific technologies to each task, such as generative AI, computational logic, and document automation. The book highlights real-world applications, demonstrating proven impact, and presents case studies and interviews to explore both the potential positive outcomes and potential challenges of AI for access to justice. Equalizing Justice proves that AI technologies offer unprecedented opportunities to create equitable justice systems serving everyone, not just those who can afford representation, and that legal AI assistants should be treated as a public good accessible to all. In honor of Karl Branting, 100% of the royalties from this book will be donated to a nonprofit organization that uses artificial intelligence to expand access to justice.

Millions of individuals worldwide struggle to understand and assert their legal rights without legal representation. Equalizing Justice examines how AI and other technologies can address this access to justice crisis by providing unrepresented litigants with knowledge and skills traditionally available only through lawyers. This volume takes a needs-first approach, identifying tasks that unrepresented litigants must complete and mapping specific technologies to each task, such as generative AI, computational logic, and document automation. The book highlights real-world applications, demonstrating proven impact, and presents case studies and interviews to explore both the potential positive outcomes and potential challenges of AI for access to justice. Equalizing Justice proves that AI technologies offer unprecedented opportunities to create equitable justice systems serving everyone, not just those who can afford representation, and that legal AI assistants should be treated as a public good accessible to all. In honor of Karl Branting, 100% of the royalties from this book will be donated to a nonprofit organization that uses artificial intelligence to expand access to justice.

The biennial International Conference on Case-Based Reasoning (ICCBR) - ries, which began in Sesimbra, Portugal, in 1995, was intended to provide an international forum for the best fundamental and applied research in case-based reasoning (CBR). It was hoped that such a forum would encourage the g- wth and rigor of the eld and overcome the previous tendency toward isolated national CBR communities. The foresight of the original ICCBR organizers has been rewarded by the growth of a vigorous and cosmopolitan CBR community. CBR is now widely recognized as a powerful and important computational technique for a wide range of practical applications. By promoting an exchange of ideas among CBR researchers from across the globe, the ICCBR series has facilitated the broader acceptance and use of CBR. ICCBR-99 has continued this tradition by attracting high-quality research and applications papers from around the world. Researchers from 21 countries submitted 80 papers to ICCBR-99. From these submissions, 17 papers were selected for long oral presentation, 7 were accepted for short oral presentation, and 19 papers were accepted as posters. This volume sets forth these 43 papers, which contain both mature work and innovative new ideas.

The biennial International Conference on Case-Based Reasoning (ICCBR) - ries, which began in Sesimbra, Portugal, in 1995, was intended to provide an international forum for the best fundamental and applied research in case-based reasoning (CBR). It was hoped that such a forum would encourage the g- wth and rigor of the eld and overcome the previous tendency toward isolated national CBR communities. The foresight of the original ICCBR organizers has been rewarded by the growth of a vigorous and cosmopolitan CBR community. CBR is now widely recognized as a powerful and important computational technique for a wide range of practical applications. By promoting an exchange of ideas among CBR researchers from across the globe, the ICCBR series has facilitated the broader acceptance and use of CBR. ICCBR-99 has continued this tradition by attracting high-quality research and applications papers from around the world. Researchers from 21 countries submitted 80 papers to ICCBR-99. From these submissions, 17 papers were selected for long oral presentation, 7 were accepted for short oral presentation, and 19 papers were accepted as posters. This volume sets forth these 43 papers, which contain both mature work and innovative new ideas.

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.

Few areas of human expertise are so well understood that they can be completely reduced to general principles. Similarly, there are few domains in which experience is so extensive that every new problem precisely matches a previous problem whose solution is known. When neither rules nor examples are individually sufficient, problem-solving expertise depends on integrating both. This book presents a computational framework for the integration of rules and cases for analytic tasks typified by legal analysis. The book uses the framework for integrating cases and rules as a basis for a new model of legal precedents. This model explains how the theory under which a case is decided controls the case's precedential effect. The framework for integrating rules and cases is implemented in GREBE, a system for legal analysis. The book presents techniques for representing, indexing, and comparing complex cases and for converting justification structures based on rules and case into natural-language text. This book will interest researchers in artificial intelligence, particularly those involved in case-based reasoning, artificial intelligence and law, and formal models of argumentation, and to scholars in legal philosophy, jurisprudence, and analogical reasoning.

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.

Few areas of human expertise are so well understood that they can be completely reduced to general principles. Similarly, there are few domains in which experience is so extensive that every new problem precisely matches a previous problem whose solution is known. When neither rules nor examples are individually sufficient, problem-solving expertise depends on integrating both. This book presents a computational framework for the integration of rules and cases for analytic tasks typified by legal analysis. The book uses the framework for integrating cases and rules as a basis for a new model of legal precedents. This model explains how the theory under which a case is decided controls the case''s precedential effect. The framework for integrating rules and cases is implemented in GREBE, a system for legal analysis. The book presents techniques for representing, indexing, and comparing complex cases and for converting justification structures based on rules and case into natural-language text. This book will interest researchers in artificial intelligence, particularly those involved in case-based reasoning, artificial intelligence and law, and formal models of argumentation, and to scholars in legal philosophy, jurisprudence, and analogical reasoning.