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What eventually determines the semantics of algorithmic decision-making is not the program artefact, nor—if applicable—the data used to create it, but the preparatory (enabling) and consequent (enabled) practices holding in the environment (computational and human) in which such algorithmic procedure is embedded. The notion of responsibility captures a very similar construct: in all human societies actions are evaluated in terms of the consequences they could reasonably cause, and of the reasons that motivate them. But to what extent does this function exist in computational systems? The paper aims to sketch links between several of the approaches and concepts proposed for responsible computing, from AI to networking, identifying gaps and possible directions for operationalization.

The so-called “Right to Be Forgotten or Erasure” (RTBF), article 17 of the proposed General Data Protection Regulation, provides individuals with a means to oppose the often persistent digital memory of the Web. Because digital information technologies affect the accessibility of information over time and time plays a fundamental role in biological forgetting,‘time’ is a factor that should play a pivotal role in the RTBF.This chapter explores the roles that ‘time’ plays and could plain decisions regarding the retention or erasure of data. Two roles are identified: (1) ‘time’ as the marker of a discrete moment where the grounds for retention no longer hold and ‘forgetting’ of the data should follow and (2) ‘time’ as a factor in the balance of interests, as adding or removing weight to the request to ‘forget’ personal information or its opposing interest. The chapter elaborates on these two roles from different perspectives and highlights the importance and underdeveloped understanding of the second role.

We can trace five origins of ontology engineering, and all five still play a major role in ontology engineering. Each of these roots gives a different perspective on content and use of ontologies. Philosophical ontology is concerned with "reality"; Information science with systematic terminology; Artificial Intelligence (AI) with terminological knowledge, Knowledge Engineering with the specification of knowledge bases, and Information Management with semantics. Associated with these roots, the applications differ and range from analytic clarification to automated reasoning. Also mismatches between formalism and aim occur frequently. These mismatches can often be traced to an unclear distinction between knowledge and semantics. We explain this difference in Section 4.3 using a simple cognitive architecture for natural language production. A Cognitive Science perspective is however well suited where top ontologies try to cover the core concepts of common sense, as a wealth of empirical studies have become available on the content of our "knowledge instincts". We present an example on the modeling of spatial concepts and refer to our still ongoing work on a common-sense based core ontology for legal domains: LKIF-Core (Hoekstra et al. 2007; Hoekstra 2009).

This paper describes the experiments carried out in the context of the BEST-project, an interdisciplinary project with researchers from the Law faculty and the AI department of the VU University Amsterdam. The aim of the project is to provide laymen with information about their legal position in a liability case, based on retrieved case law. The process basically comes down to (1) analyzing the input of a layman in terms of a layman ontology, (2) mapping this ontology to a legal ontology, (3) retrieve relevant case law based, and finally (4) present the results in a comprehensible way to the layman. This paper describes the experiments undertaken regarding step 4, and in particular step 3.

Currently almost all legislative bodies throughout Europe use general purpose word-processing software for the drafting of legal documents. These regular word processors do not provide specific support for legislative drafters and parliamentarians to facilitate the legislative process. Furthermore, they do not natively support metadata on regulations. This paper describes how the MetaLex regulation-drafting environment (MetaVex) aims to meet such requirements.

This introduction presents the fifth volume of a series started twelve years ago: the AI Approaches to the Complexity of Legal Systems (AICOL). The introduction revises the recurrently addressed topics of technology, Artificial Intelligence and law and presents new challenges and areas of research, such as the AI ethical and legal turn, hybrid and conflictive intelligences, regulatory compliance and AI explainability. Other domains not yet fully explored include the regulatory models of the Web of Data and the Internet of Things that integrate legal reasoning and legal knowledge modelling.

This paper presents the result of the research project Lexdatafication that aims to model the legal knowledge information of Italy in Akoma Ntoso. The University of Bologna, in cooperation with IPZS, the Official Gazette entity, developed a framework capable to exploit the existing legacy databases of Normattiva in Akoma Ntoso. Additionally, Constitutional Court decisions were converted in Akoma Ntoso using the existing XML and metadata dataset provided by the open data portal. This output was linked to the legislative information. The collection of the documents in AKN constitutes a great annotated corpus in a machine-consumable format capable to produce relevant legal data analytics applications and visualizations to support both practitioners and citizens in legal information retrieval.

This paper argues in favour of the necessity of dynamically restricting and expanding the applicability of norms regulating computer systems like multiagent systems, in situations where the compliance to the norm does not achieve the purpose of the norm. We propose a logical framework which distinguishes between constitutive and regulative norms and captures the norm change power and at the same time the limitations of the judicial system in dynamically revising the set of constitutive rules defining the concepts on which the applicability of norms is based. In particular, the framework is used to reconstruct some interpretive arguments described in legal theory such as those corresponding to the Roman maxims lex minus dixit quam voluit and lex magis dixit quam voluit. The logical framework is based on an extension of defeasible logic.

Different implications of the concept of "rule of reason" inthe Internal Market and in Competition according to the case law of the European Court of Justice

This paper describes the experiments carried out in the context of the BEST-project, an interdisciplinary project with researchers from the Law faculty and the AI department of the VU University Amsterdam. The aim of the project is to provide laymen with information about their legal position in a liability case, based on retrieved case law. The process basically comes down to (1) analyzing the input of a layman in terms of a layman ontology, (2) mapping this ontology to a legal ontology, (3) retrieve relevant case law based, and finally (4) present the results in a comprehensible way to the layman. This paper describes the experiments undertaken regarding step 4, and in particular step 3.