The paper focuses on a specific type of referring legal provisions, in which the referring phrase contains a component that indicates the position of a certain fragment of the same text of a normative act by determining the position of that fragment in relation to the fragment in which the given referring phrase is located. Despite the fact that these referrals, called deictic, may be perceived as uncomplicated in structure and as functioning correctly in legal texts, many theoretical as well (...) as practical problems arise during their drafting and legal interpretation. The article attempts to reveal, organize and name these problems basing on the conceptual grid formulated by Studnicki, Łachwa, Fall and Stabrawa as a universal, although not widely known outside Polish jurisprudence, tool for analysing and solving referring provisions as elements of various legal systems, especially continental ones. Texts of selected Polish Codes have been chosen as an illustrative material of deictic referrals which have been incorrectly formulated as leaving space for interpretative doubts in terms of place of indication and place of reference, which also causes a potential problem of the deferred ostension phenomenon noticed by Quine. The author argues that proper drafting of referring legal provisions with deictic indication requires that the legislator should have extensive pragmalinguistic knowledge and is an essential part of law-making oriented towards achieving comprehensibility of texts of normative acts, especially as important as Codes. (shrink)

This paper reflects my address as IAAIL president at ICAIL 2021. It is aimed to give my vision of the status of the AI and Law discipline, and possible future perspectives. In this respect, I go through different seasons of AI research : from the Winter of AI, namely a period of mistrust in AI, to the Summer of AI, namely the current period of great interest in the discipline with lots of expectations. One of the results of the first (...) decades of AI research is that “intelligence requires knowledge”. Since its inception the Web proved to be an extraordinary vehicle for knowledge creation and sharing, therefore it’s not a surprise if the evolution of AI has followed the evolution of the Web. I argue that a bottom-up approach, in terms of machine/deep learning and NLP to extract knowledge from raw data, combined with a top-down approach, in terms of legal knowledge representation and models for legal reasoning and argumentation, may represent a promotion for the development of the Semantic Web, as well as of AI systems. Finally, I provide my insight in the potential of AI development, which takes into account technological opportunities and theoretical limits. (shrink)

Access to legal information and, in particular, to legal literature is examined for the creation of a search and retrieval system for Italian legal literature. The design and implementation of services such as integrated access to a wide range of resources are described, with a particular focus on the importance of exploiting metadata assigned to disparate legal material. The integration of structured repositories and Web documents is the main purpose of the system: it is constructed on the basis of a (...) federation system with service provider functions, aiming at creating a centralized index of legal resources. The index is based on a uniform metadata view created for structured data by means of the OAI approach and for Web documents by a machine learning approach, which, in this paper, has been assessed as regards document classification. Semantic searching is a major requirement for legal literature users and a solution based on the exploitation of Dublin Core metadata, as well as the use of legal ontologies and related terms prepared for accessing indexed articles have been implemented. (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)

This paper describes the automated classification of legal norms in German statutes with regard to their semantic type. We propose a semantic type taxonomy for norms in the German civil law domain consisting of nine different types focusing on functional aspects, such as Duties, Prohibitions, Permissions, etc. We performed four iterations in classifying legal norms with a rule-based approach using a manually labeled dataset, i.e., tenancy law, of the German Civil Code ). During this experiment the \ score continuously improved (...) from 0.52 to 0.78. In contrast, a machine learning based approach for the classification was implemented. A performance of \ was reached. Traditionally, machine learning classifiers lack of transparency with regard to their decisions. We extended our approach using so-called local linear approximations, which is a novel technique to analyze and inspect a trained classifier’s behavior. We can show that there are significant similarities of manually crafted knowledge, i.e., rules and pattern definitions, and the trained decision structures of machine learning approaches. (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)