Use of argumentation methods applied to legal reasoning is a relatively new field of study. The book provides a survey of the leading problems, and outlines how future research using argumentation-based methods show great promise of leading to useful solutions. The problems studied include not only these of argument evaluation and argument invention, but also analysis of specific kinds of evidence commonly used in law, like witness testimony, circumstantial evidence, forensic evidence and character evidence. New tools for analyzing these kinds of evidence are introduced.

Recent work in artificial intelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificial intelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject to disastrous failures. At the same time such testimony can provide evidence that is not only necessary but inherently reasonable for logically guiding legal experts to accept or reject a claim. Walton shows how to overcome the traditional disdain for witness testimony as a type of evidence shown by logical positivists, and the views of trial sceptics who doubt that trial rules deal with witness testimony in a way that yields a rational decision-making process.

In the study of forms of legal reasoning, logic and argumentation theory long followed separate tracks. `Legal logicians' tended to focus on a deductive reconstruction of justifying a decision, disregarding the dialectical process leading to the chosen justification. Others instead emphasized the adversarial and discretionary nature of legal reasoning, involving reasonable evaluation of alternative choices, and the use of analogical reasoning. Recently, however, developments in Artificial Intelligence and Law have paved the way for overcoming this separation. Logic has widened its scope to defensible argumentation, and informal accounts of analogy and dialectics have inspired the construction of computer programs. Thus the prospect is emerging of an integrated logical and dialectical account of legal argument, adding to the understanding of legal reasoning, and providing a formal basis for computer tools that assist and mediate legal debates while leaving room for human initiative. This book presents contributions to this development. From a logical point of view it covers topics such as evaluating conflicting arguments, weighing reasons, modelling legal disputes as a dialogue game, the role of the burden of proof, the relation between principles, rules, reasons and facts, and the relation between deductive and nondeductive arguments. Written by leading scholars in the field and building on recent developments in logic and Artificial Intelligence, the chapters provide a state-of-the-art account of research on the logical aspects of legal argument.

A leading expert in informal logic, Douglas Walton turns his attention in this new book to how reasoning operates in trials and other legal contexts, with special emphasis on the law of evidence. The new model he develops, drawing on methods of argumentation theory that are gaining wide acceptance in computing fields like artificial intelligence, can be used to identify, analyze, and evaluate specific types of legal argument. In contrast with approaches that rely on deductive and inductive logic and rule out many common types of argument as fallacious, Walton&’s aim is to provide a more expansive view of what can be considered &"reasonable&" in legal argument when it is construed as a dynamic, rule-governed, and goal-directed conversation. This dialogical model gives new meaning to the key notions of relevance and probative weight, with the latter analyzed in terms of pragmatic criteria for what constitutes plausible evidence rather than truth.

This book introduces methods to analyze legal documents such as negotiation records and legal precedents, using computational argumentation theory. First, a method to automatically evaluate argumentation skills from the records of argumentation exercises is proposed. In law school, argumentation exercises are often conducted and many records of them are produced. From each utterance in the record, a pattern of “speech act +factor” is extracted, and argumentation skills are evaluated from the sequences of the patterns, using a scoring prediction model constructed by multiple regression analyses between the appearance pattern and the scoring results. The usefulness of this method is shown by applying it to the example case “the garbage house problem”. Second, a method of extracting factors (elements that characterize precedents and cases) and legal topoi from individual precedents and using them as the expression of precedents to analyze how the pattern of factors and legal topoi appearing in a group of precedents affects the judgment (plaintiff wins/defendant wins) is proposed. This method has been applied to a group of tax cases. Third, the logical structure of 70 labor cases is described in detail by using factors and a bipolar argumentation framework (BAF) and an (extended argumentation framework (EAF) together. BAF describes the logical structure between plaintiff and defendant, and EAF describes the decision of the judge. Incorporating the legal topoi into the EAF of computational argumentation theory, the strength of the analysis of precedents by combined use of factored BAF and EAF, not only which argument the judge adopted could be specified. It was also possible to determine what kind of value judgment was made and to verify the logic. The analysis methods in this book demonstrate the application of logic-based AI methods to the legal domain, and they contribute to the education and training of law school students in logical ways of argumentation.

Legal theory, political sciences, sociology, philosophy, logic, artificial intelligence: there are many approaches to legal argumentation. Each of them provides specific insights into highly complex phenomena. Different disciplines, but also different traditions in disciplines (e.g. analytical and continental traditions in philosophy) find here a rare occasion to meet. The present book contains contributions, both historical and thematic, from leading researchers in several of the most important approaches to legal rationality. One of the main issues is the relation between logic and law: the way logic is actually used in law, but also the way logic can make law explicit. An outstanding group of philosophers, logicians and jurists try to meet this issue. The book is more than a collection of papers. However different their respective conceptual tools may be, the authors share a common conception: legal argumentation is a specific argumentation context.

​This monograph poses a series of key problems of evidential reasoning and argumentation. It then offers solutions achieved by applying recently developed computational models of argumentation made available in artificial intelligence. Each problem is posed in such a way that the solution is easily understood. The book progresses from confronting these problems and offering solutions to them, building a useful general method for evaluating arguments along the way. It provides a hands-on survey explaining to the reader how to use current argumentation methods and concepts that are increasingly being implemented in more precise ways for the application of software tools in computational argumentation systems. It shows how the use of these tools and methods requires a new approach to the concepts of knowledge and explanation suitable for diverse settings, such as issues of public safety and health, debate, legal argumentation, forensic evidence, science education, and the use of expert opinion evidence in personal and public deliberations.

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

This book provides an overview of computer techniques and tools — especially from artificial intelligence (AI) — for handling legal evidence, police intelligence, crime analysis or detection, and forensic testing, with a sustained discussion of methods for the modelling of reasoning and forming an opinion about the evidence, methods for the modelling of argumentation, and computational approaches to dealing with legal, or any, narratives. By the 2000s, the modelling of reasoning on legal evidence has emerged as a significant area within the well-established field of AI & Law. An overview such as this one has never been attempted before. It offers a panoramic view of topics, techniques and tools. It is more than a survey, as topic after topic, the reader can get a closer view of approaches and techniques. One aim is to introduce practitioners of AI to the modelling legal evidence. Another aim is to introduce legal professionals, as well as the more technically oriented among law enforcement professionals, or researchers in police science, to information technology resources from which their own respective field stands to benefit. Computer scientists must not blunder into design choices resulting in tools objectionable for legal professionals, so it is important to be aware of ongoing controversies. A survey is provided of argumentation tools or methods for reasoning about the evidence. Another class of tools considered here is intended to assist in organisational aspects of managing of the evidence. Moreover, tools appropriate for crime detection, intelligence, and investigation include tools based on link analysis and data mining. Concepts and techniques are introduced, along with case studies. So are areas in the forensic sciences. Special chapters are devoted to VIRTOPSY (a procedure for legal medicine) and FLINTS (a tool for the police). This is both an introductory book (possibly a textbook), and a reference for specialists from various quarters.