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)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely (...) argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments. (shrink)

In this paper, we present an approach to commonsense causal explanation of stories that can be used for automatically determining the liable party in legal case descriptions. The approach is based on, a core ontology for law that takes a commonsense perspective. Aside from our thesis that in the legal domain many terms still have a strong commonsense flavour, the descriptions of events in legal cases, as e.g. presented at judicial trials, are cast in commonsense terms as well. We present (...) design principles for representing commonsense causation, and describe a process-based approach to automatic identification of causal relations in stories, which are described in terms of the core ontology. The resulting causal explanation forms a necessary condition for determining the liability and responsibility of agents that play a role in the case. We describe the basic architecture and working of, the demonstrator we are constructing to test the validity of our process oriented view on commonsense causation. This view holds that causal relations are in fact abstractions constructed on the basis of our commonsense understanding of physical and mental processes. (shrink)

This article focuses on the problems of application of artificial intelligence to represent legal knowledge. The volume of legal knowledge used in practice is unusually large, and therefore the ontological knowledge representation is proposed to be used for semantic analysis, presentation and use of common vocabulary, and knowledge integration of problem domain. At the same time some features of legal knowledge representation in Ukraine have been taken into account. The software package has been developed to work with the ontology. The (...) main features of the program complex, which has a Web-based interface and supports multi-user filling of the knowledge base, have been described. The crowdsourcing method is due to be used for filling the knowledge base of legal information. The success of this method is explained by the self-organization principle of information. However, as a result of such collective work a number of errors are identified, which are distributed throughout the structure of the ontology. The results of application of this program complex are discussed in the end of the article and the ways of improvement of the considered technique are planned. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This special issue marks the 30th anniversary of the journal by reviewing the progress of the field through thirty commentaries on landmark papers and groups of papers from that journal.

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify (...) two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology. (shrink)