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.

This paper introduces ALEXChat, a hybrid generative-symbolic chatbot designed to enhance the accessibility and explainability of legal expert systems. It integrates symbolic reasoning with large language models (LLMs) to bridge the gap between complex legal reasoning and user comprehension. ALEXChat combines the extraction of legally relevant facts from scenarios, rule-based prolog reasoning and an argumentation framework for conformity assessment, with the linguistic power of LLMs for accessible user fruition.

Causation plays a central role in the attribution of responsibility, especially in the legal domain, where complex causal scenarios frequently arise. Traditionally, legal reasoners have relied on the idea that a cause must be a necessary condition of its effect, which falls short in scenarios involving overdetermination, preemption, or omission, thereby failing to adequately identify causes-in-fact. In this paper, we present a novel analysis of selected legal cases, each exemplifying common causal dilemmas discussed in causal literature. We employ three different notions of cause in our analysis: abductive explanation (AXp), the NESS test (Necessary Element of a Sufficient Set) and actual cause. We express the three notions and some of their variants in a modal language for causal reasoning that we interpret on a rule-based semantics. We provide a model checking algorithm for our modal language relying on a reduction into TQBF as well as an implementation of the legal cases in our causal model checker to automatically verify “what is the cause of what” and what types of causes apply in each legal case. Our interdisciplinary approach highlights the usefulness of logic-based methods for legal analysis, offering a fully transparent model-checking toolbox that could potentially support legal reasoners in disentangling complex factual scenarios.

We propose to model cause-in-fact in legal cases through fresh argumentation-theoretic notions of explanation and support, meant to capture the set of arguments that contribute to making a conclusion justified. This novel argumentation-based approach to causality in law goes beyond the traditional idea of a cause as a necessary antecedent condition (the conditio-sine-qua-non idea), to handle concurrent causal processes leading to overdetermination and preemption. It also provides sound analyses of cases involving omission and ennoblement. Finally, by relying on defeasible argumentation it can capture causal inferences based on defeasible generalisations, which are very often used in judicial reasoning. Through the analysis of causal puzzles in legal cases, we illustrate the framework’s effectiveness in handling complex causal reasoning, and demonstrate its potential to support legal reasoners with structured and intuitive analysis.

Autonomous vehicles (AVs) must abide by the Highway Code. An AV agent would make plans which incorporate legal reasoning. Such plans must account for contingencies wherein the agent chooses between following a current plan that would lead to a violation of the law or providing an alternative plan which would not lead to a violation - a diversion. The paper utilises Defeasible Deontic Logic representing norms and encoded in Answer Set Programming (ASP) which is integrated with planning in ASP. The paper introduces diversions to address contingencies ith respect to legal reasoning.

This paper implements Large Language Models (LLMs) to support the development of expert systems in the legal domain. Our goal is to tackle one of the most critical issues related to the creation and management of rule-based systems, being the knowledge representation bottleneck. To do so, we employ GPT-4o in combination with an existing expert system developed using the Prolog language, presenting a case study based on multiple tasks. The first task deals with the formalization of legal articles in Prolog given a stable knowledge base and factual structure, including the revision of existing facts. The second task deals with the implementation of case law for updating of the expert system. To do so, it identifies the influence of case law on the application of existing norms, creates new rules and implements them in the system. This paper contributes to the field of law and Artificial Intelligence (AI) by investigating the relationship between LLMs and legal expert systems, and exploring its usefulness for knowledge engineers, as well as contributing to the research of hybrid architectures combining generative and symbolic AI.

Causality is vital for establishing legal liability, but traditional analyses often fail to address complex scenarios and conflate causation with legal responsibility. This paper presents a novel approach to cause-in-fact based on argumentation theory which also assesses whether a cause-in-fact lacks legal relevance.

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.

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.