This article describes an ontological model of norms. The basic assumption is that a substantial part of a legal system is grounded on the concept of agency. Since a legal system aims at regulating a society, then its goal can be achieved only by affecting the behaviour of the members of the society. We assume that a society is made up of agents (which can be individuals, institutions, software programs, etc.), that agents have beliefs, goals and preferences, and that they (...) commit to intentions in order to choose a line of behaviour. The role of norms, within a legal system, is to specify how and when the chosen behaviour agrees with the basic principles of the legal system. In this article, we show how a model based on plans can be the basis for the ontological representation of norms, which are expressed as constraints on the possible plans an agent may choose to guide its behaviour. Moreover, the paper describes how the proposed model can be linked to the upper level of a philosophically well-founded ontology (DOLCE); in this way, the model is set in a wider perspective, which opens the way to further developments. (shrink)

In this paper, we describe the use of legal ontologies as a basis to improve IT support for professional judges. As opposed to most legal ontologies designed so far, which are mostly based on dogmatic and normative knowledge, we emphasize the importance of professional knowledge and experience as an important pillar for constructing the ontology. We describe an intelligent FAQ system for junior judges that intensively use the ontology.

Intellectual property law tends to be viewed as the only (or most significant) mechanism for achieving policy goals relating to innovation assets. Yet more creative and effective solutions are often available. When analysed from a transdisciplinary perspective, relying on the cooperative efforts of researchers from fields other than law, innovation governance is characterized not simply as the product of legal rules, but as a function of the interaction of legal rules, practices and institutions. When policy-makers seek to identify conditions under (...) which the creation, use and exchange of innovation assets flourishes, care should be taken to focus on this combination of factors. This article describes the development of an ontology—a computerized method of representing knowledge as concepts and relations between concepts—to convey such understanding. Policy makers (and researchers) are provided with an organized, accessible representation of innovation governance that enriches their understanding and improves their decision-making. (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)

In this introduction I give an overview of Carole Hafner’s work and discuss the papers in this volume. The final section offers some more personal reminiscences of Carole and her contribution to the AI and Law community, from myself and other colleagues.

Carole Hafner’s scholarship on the conceptual organization of case law knowledge bases was an original approach to distilling a library’s worth of cases into a manageable subset that any given legal researcher could review. Her approach applied concept indexation and concept search based on an annotation model of three interacting components combined with a system of expert legal reasoning to aid in the retrieval of pertinent case law. Despite the clear value this tripartite approach would afford to researchers in search (...) of cases with similar fact patterns and desired outcomes, this approach has not been applied consistently in the intervening years since its introduction. Specifically, the conceptual representation of domain concepts and the case frames were not pursued by researchers, and they were not applied by the legal case indexing services that came to dominate the electronic case law market. Advances since Hafner’s original scholarship in the form of digitized case law and related materials; computer science analytical protocols; and more advanced forms of artificial intelligence approaches present the question of whether Hafner’s COC model could move from the hypothetical to the real. (shrink)

I shall compare two views of legal concepts: as nodes in inferential nets and as categories in an ontology (a conceptual architecture). Firstly, I shall introduce the inferential approach, consider its implications, and distinguish the mere possession of an inferentially defined concept from the belief in the concept’s applicability, which also involves the acceptance of the concept’s constitutive inferences. For making this distinction, the inferential and eliminative analysis of legal concepts proposed by Alf Ross will be connected to the views (...) on theoretical concepts in science advanced by Frank Ramsey and Rudolf Carnap. Consequently, the mere comprehension of a legal concept will be distinguished from the application of the concept to a particular legal system, since application presupposes a doctrinal commitment, namely, the belief that the inferences constituting the concept hold in that system. Then, I shall consider how concepts can be characterised by defining the corresponding terms and placing them within an ontology. Finally, I shall argue that there is a tension between the inferential and the ontological approach, but that both need to be taken into account, to capture the meaning and the cognitive function of legal concepts. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.

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 paper discusses several topics that relate more naturally to groups of papers than a single paper published in the journal: ontologies, reasoning about evidence, the various contributions of Douglas Walton, and the practical application of the techniques of AI and Law.

This paper describes the Eunomos software, an advanced legal document and knowledge management system, based on legislative XML and ontologies. We describe the challenges of legal research in an increasingly complex, multi-level and multi-lingual world and how the Eunomos software helps users cut through the information overload to get the legal information they need in an organized and structured way and keep track of the state of the relevant law on any given topic. Using NLP tools to semi-automate the lower-skill (...) tasks makes this ambitious project a realistic commercial prospect as it helps keep costs down while at the same time allowing greater coverage. We describe the core system from workflow and technical perspectives, and discuss applications of the system for various user groups. (shrink)