AI systems can perpetuate and amplify existing biases and discrimination, prompting academic efforts to develop mitigation techniques. Despite progress, real-world deployments often expose limitations in current methods and tools--- overlooking preprocessing, adopting poor evaluation protocols, and failing to integrate domain knowledge. These gaps hinder the effectiveness and reproducibility of fairness solutions. AutoML has emerged as a promising approach to optimize AI pipelines and provide an evaluation framework. However, challenges persist, especially around: intersectionality support, explainability, and stakeholder engagement, which are crucial for fairness and human-centric AI development. We introduce HAMLET4Fairness, integrating AutoML with human-centered approaches grounded in logic and argumentation. This enhances interactivity and transparency in AI pipeline optimization while supporting intersectional fairness. HAMLET4Fairness leverages multi-objective optimization and bounds the search space by user-defined constraints, adapting the CRISP-DM methodology for co-design and collaborative problem solving. We validate HAMLET4Fairness through the well-known case studies in the literature and provide insights into how preprocessing choices affect fairness.

In recent years, addressing bias, discrimination, and fairness in AI has garnered significant attention. However, integrating these discussions with established legal frameworks, particularly within European Union legislation, remains a critical and underexplored area. This article addresses this gap by proposing legally valid fairness assessment metrics that capture the socio-legal context specific to each case. Our approach seeks to integrate legal, social, and technical perspectives in evaluating AI fairness. While many AI fairness toolkits provide statistical measures of fairness, they often fall short of aligning with the context-sensitive discrimination metrics and evidential requirements outlined by the European Court of Justice. To bridge this gap, we leverage the concept of contextual equality. The concept of contextual equality must be reified within the technology through two main steps: (1) providing a formal definition of contextual equality for AI decision-making problems, whether classification or regression, and (2) operationalizing this definition within state-of-the-art metrics for ‘measuring’ fairness compliance. The paper provides these two critical contributions. An experimental evaluation is carried out on two benchmark datasets in the field of AI and education, validating the approach with AI stakeholders involved in the field and in the decision-making process. This also serves to highlight the negative impacts that an assessment not legally sound could have.

Symbolic knowledge-extraction (SKE) techniques are becoming of key importance for AI applications since they enable the explanation of opaque black-box predictors, enhancing trust and transparency. Among all the available SKE techniques, the best option for the case at hand should be selected. However, an automatic comparison between different options can be performed only if an adequate metric – such as a scoring function resuming all the interesting features of the extractors – is provided. Regrettably, the literature currently lacks definitions of effective evaluation metrics for symbolic knowledge extractors. This paper proposes the novel FiRe score metric, which comprehensively assesses the quality of an SKE procedure by considering both its predictive performance and the readability of the extracted knowledge. FiRe is compared to another existing scoring metric and a rigorous mathematical formulation is provided along with several practical examples to highlight its effectiveness to the end of being exploited inside automatic hyper-parameter tuning procedures.

A significant challenge in detecting and mitigating bias is creating a mindset amongst AI developers to address unfairness. The current literature on fairness is broad, and the learning curve to distinguish where to use existing metrics and techniques for bias detection or mitigation is difficult. This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. Gaps and challenges identified during the development of this work are also discussed.

This paper proposes CREAM, a new explainable clustering technique based on decision tree induction, providing human-interpretable clusters by performing hypercubic approximations of the input feature space. CREAM may also be applied to data sets describing classification and regression tasks, given that the algorithm discriminates amongst input and output features. We also present OrCHiD, an automated tuning procedure to select the optimum CREAM parameter. Experiments demonstrating the effectiveness of CREAM in clustering, classification, and regression tasks are reported here, in comparison with other state-of-the-art techniques used as benchmarks.

Abstract Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the legal domain (LegalTech). The authors of this paper deem as crucial the research and implementation of compliance checkers that can directly process data in RDF format, as nowadays more and more (big) data in this format are becoming available worldwide, across a multitude of different domains. Among the automated technologies that have been used in recent literature, to the best of our knowledge, only two of them have been evaluated with input states of affairs encoded in RDF format. This paper formalizes a selected use case in these two technologies and compares the implementations, also in terms of simulations with respect to shared synthetic datasets.

In this paper we formalise a meta-argumentation framework as an ASPIC+ extension which enables reasoning about conflicts between formulae of the argumentation language. The result is a standard abstract argumentation framework that can be evaluated via grounded semantics.

Abstract In this paper we present the computational model of Arg2P, a logic-based argumentation framework for defeasible reasoning and agent conversation particularly suitable for explaining agent intelligent behaviours. The model is reified as the Arg2P technology, which is presented and discussed both from an architectural and a technological perspective so as to point out its potential in the engineering of intelligent systems. Finally, an illustrative application scenario is discussed in the domain of computable law for autonomous vehicles.

In this paper we provide an account of the burden of persuasion in the context of structured argumentation. A formal model for the burden of persuasion is defined, discussed, and used to capture the role of the burden of persuasion in adjudicating conflicts between conflicting arguments and in determining the dialectical status of arguments. We consider how our model can also capture adversarial burdens of proof, namely, those cases in which failure to establish an argument for a proposition burdened with persuasion entails establishing the complementary proposition.

Abstract Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI”—in particular, logic-based ones—will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies , which characterised their early days, might open new ways to engineer explainable intelligent systems . This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones.

Private international law (PIL) addresses overlaps and conflicts between legal systems by distributing cases between the authorities of such systems (jurisdiction) and establishing what rules these authorities have to apply to each case(choice of law). A modular argumentation tool, Arg-tuProlog, is here presented that enables reasoning with rules and interpretations of multiple legal systems.

This work provides a formal model for the burden of persuasion in legal proceedings. The model shows how the allocation of the burden of persuasion may induce a satisfactory outcome in contexts in which the assessment of conflicting arguments would, without such an allocation, remain undecided. The proposed model is based on an argumentation setting in which arguments may be accepted or rejected according to whether the burden of persuasion falls on the conclusion of such arguments or on its complements. Our model merges two ideas that have emerged in the debate on the burden of persuasion: the idea that allocation of the burden of persuasion makes it possible to resolve conflicts between arguments, and the idea that its satisfaction depends on the dialectical statuses of the arguments involved. Our model also addresses cases in which the burden of persuasion is inverted, and cases in which burdens of persuasion are inferred through arguments.

Different formalisms for defeasible reasoning have been used to represent legal knowledge and to reason with it. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: Defeasible Logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches from three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software). On this basis, we identify and apply criteria for assessing their suitability for legal applications. We discuss the different approaches through a legal running example.

Distributing situated intelligence in Cyber-Physical Systems (CPS) to realise the vision of Internet of Intelligent Things (IoIT) raises issues of efficiency and scalability-in particular when dealing with huge numbers of physical objects. Such issues do not just regard the application or service logic and runtime, but also impact on the software development process. Moving from the notion of Logic Programming as a Service (LPaaS) - a re-interpretation of distributed logic programming tailored to the IoT era - in this paper we describe how its architecture and development process deals with the aforementioned issues from a software engineering standpoint, by discussing the design, development practices, and delivery means of the LPaaS technology.

In order to enable logic programming to deal with the diversity of pervasive systems, where many heterogeneous, domain-specific computational models could benefit from the power of symbolic computation, we explore the expressive power of labelled systems. To this end, we define a new notion of trut h for logic programs extended with labelled variables interpreted in non-Herbrand domains—where, however, terms maintain their usual Herbrand interpretations. First, a model for labelled variables in logic programming is defined. Then, the fixpoint and the operational semantics are presented and their equivalence is formally proved. A meta-interpreter implementing the operational semantics is also introduced, followed by some case studies aimed at showing the effectiveness of our approach in selected scenarios.

Abstract New generations of distributed systems are opening novel perspectives for logic programming (LP): On the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper, we introduce the notion ofLogic Programming as a Service(LPaaS) as a means to address the needs of pervasive intelligent systems through logic engines exploited as a distributed service. First, we define the abstract architectural model by re-interpreting classical LP notions in the new context; then we elaborate on the nature of LP interpreted as a service by describing the basic LPaaS interface. Finally, we show how LPaaS works in practice by discussing its implementation in terms of distributed tuProlog engines, accounting for basic issues such as interoperability and configurability.

The testing of the performance of opportunistic communication protocols and applications is usually done through simulation as i) deployments are expensive and should be left to the final stage of the development process, and ii) the number of varying parameters in thesesystems is so high that it would be very hard to conduct thorough testing of all the functionality within a single deployment. Therefore, protocols and applications are often plugged into mobility simulators to test their performance; however, until recently, most of the testing has been conducted with random mobility models which do not mirror reality. Furthermore, despite disconnections playing a veryprominent role in the performance of any opportunistic mobile system, most models do not really account for it. A different approach to testing is the use of real traces of movement collected in specific domains as test cases. These cases, however, do not allow for flexible performance testing, as they are specific for a given scenario withfixed connectivity properties.