In this work, we provide a broad overview of the distinct stages of E-Discovery. We portray them as an interconnected, often complex workflow process, while relating them to the general Electronic Discovery Reference Model (EDRM). We start with the definition of E-Discovery. We then describe the very positive role that NIST’s Text REtrieval Conference (TREC) has added to the science of E-Discovery, in terms of the tasks involved and the evaluation of the legal discovery work performed. Given the critical nature (...) that data analysis plays at various stages of the process, we present a pyramid model, which complements the EDRM model: for gathering and hosting; indexing; searching and navigating; and finally consolidating and summarizing E-Discovery findings. Next we discuss where the current areas of need and areas of growth appear to be, using one of the field’s most authoritative surveys of providers and consumers of E-Discovery products and services. We subsequently address some areas of Artificial Intelligence, both Information Retrieval-related and not, which promise to make future contributions to the E-Discovery discipline. Some of these areas include data mining applied to e-mail and social networks, classification and machine learning, and the technologies that will enable next generation E-Discovery. The lesson we convey is that the more IR researchers and others understand the broader context of E-Discovery, including the stages that occur before and after primary search, the greater will be the prospects for broader solutions, creative optimizations and synergies yet to be tapped. (shrink)

Law and legal reasoning are a natural target for artificial intelligence systems. Like medical diagnosis and other tasks for expert systems, legal analysis is a matter of interpreting data in terms of higher-level concepts. But in law the data are more like those for a system aimed at understanding natural language: they tell a story about human events that may lead to a lawsuit. Statements of the law, too, are written in natural language and legal arguments are often arguments about (...) what that language means or ought to mean.This study is one of the few research efforts in this fertile area. It is unique in developing a computational model for analyzing legal problems in a way that brings these strands of AI research together and makes sense from a jurisprudential perspective as well.Gardner first analyzes several positions in Anglo-American jurisprudence and their relevance for work in artificial intelligence. She identifies aspects of legal reasoning that any truly expert system in law must make a place for and suggests a way of decomposing the process of legal analysis that takes these aspects into account. She compares the resulting framework with those used by other legal analysis programs. A solid exposition of current AI techniques follows in chapters covering the author's system (written in Maclisp) for offer and acceptance problems, taken from law examinations, involved in contract law.Anne von der Lieth Gardner has a law degree and a Ph.D. in computer science, both from Stanford University. An Artificial Intelligence Approach to Legal Reasoning inaugurates the series Artificial Intelligence and the Law: Processes and Models of Legal Reasoning, edited by L. Thorne McCarty and Edwina L. Rissland. A Bradford Book. (shrink)

In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating roller-coaster ride through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine.

. This paper offers a survey of the current state of Artificial Intelligence and Law, and makes recommendations for future research. Two main areas of investigation are discussed: the practical work on intelligent legal information systems, and the theoretical work on computational models of legal reasoning. In both areas, the knowledge representation problem is identified as the most important issue facing this field.

What does it mean to assert or deny the existence of a legal system? How can one determine whether a given law belongs to a certain legal system? What kind of structure do these systems have, that is--what necessary relations obtain between their laws? The examination of these problems in this volume leads to a new approach to traditional jurisprudential question, though the conclusions are based on a critical appraisal, particularly those of Bentham, Austin, Kelsen, and Hart.

We shall introduce a set of fundamental legal concepts, providing a definition of each of them. This set will include, besides the usual deontic modalities (obligation, prohibition and permission), the following notions: obligative rights (rights related to other’s obligations), permissive rights, erga-omnes rights, normative conditionals, liability rights, different kinds of legal powers, potestative rights (rights to produce legal results), result-declarations (acts intended to produce legal determinations), and sources of the law.

Legislation usually lacks a systematic organization which makes the management and the access to norms a hard problem to face. A more analytic semantic unit of reference (provision) for legislative texts was identified. A model of provisions (provisions types and their arguments) allows to describe the semantics of rules in legislative texts. It can be used to develop advanced semantic-based applications and services on legislation. In this paper an automatic bottom-up strategy to qualify existing legislative texts in terms of provision (...) types is described. (shrink)

If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the.

Fundamentals of Critical Argumentation presents the basic tools for the identification, analysis, and evaluation of common arguments for beginners. The book teaches by using examples of arguments in dialogues, both in the text itself and in the exercises. Examples of controversial legal, political, and ethical arguments are analyzed. Illustrating the most common kinds of arguments, the book also explains how to evaluate each kind by critical questioning. Douglas Walton shows how arguments can be reasonable under the right dialogue conditions by (...) using critical questions to evaluate them. The book teaches by example, both in the text itself and in exercises, but it is based on methods that have been developed through the author's thirty years of research in argumentation studies. (shrink)

The book builds on recent work in pragmatics and speech-act theory to explain how, and to what extent, legal content is determined by linguistic considerations. At the same time, the analysis shows that some of the unique features of communication in the legal domain - in particular, its strategic nature - can be employed to put pressure on certain assumptions in philosophy of language. This enables a more nuanced picture of how semantic and pragmatic determinants of communication work in complex (...) and large-scale systems such as law. (shrink)

In the course of legal reasoning—whether for purposes of deciding an issue, justifying a decision, predicting how an issue will be decided, or arguing for how it should be decided—one often is required to reach conclusions based on a balance of reasons that is not straightforwardly reducible to the application of rules. Recent AI and Law work has modeled reason-balancing, both within and across cases, with set-theoretic and rule- or value-ordering approaches. This article explores a way to model balancing in (...) quantitative terms that may yield new questions, insights, and tools. (shrink)

Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be (...) good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)

A model of normative provisions and related axioms represented by using RDF/owl are presented as a contribution to implement the semantic web in the legal domain. In particular, a pattern able to implement the Hohfeldian legal fundamental relations between provisions using OWL-DL expressivity is proposed. Moreover, a query-based approach able to deal with relations between provision instances is described. An example of advanced access and reasoning over provisions using the proposed approach, as well as a prototype architecture of a provision (...) retrieval system, are shown. The main benefit is represented by the ability of the approach to keep the complexity of the problem within a description logic computational tractability. (shrink)