Argumentation-Based Explainability for Legal AI: Comparative and Regulatory Perspectives

TL;DR

This work tackles the opacity of AI in legal decision-making by advocating computational argumentation as a robust, normative explainability framework aligned with EU regulations. It provides a structured comparison of explanation strategies—example-based, rule-based, hybrid, and various argumentation formalisms (AAF, BAF, VAF, ADF)—and demonstrates how argumentation yields contestable, legally coherent explanations. The paper argues that argumentative approaches best capture the defeasible, value-laden nature of law and align with GDPR and the AI Act, while acknowledging challenges such as bias, empirical validation, and regulatory rollout. Practically, it guides the design of legally compliant, transparent AI systems and highlights regulatory pathways toward adoption in judicial and administrative contexts.

Abstract

Artificial Intelligence (AI) systems are increasingly deployed in legal contexts, where their opacity raises significant challenges for fairness, accountability, and trust. The so-called ``black box problem'' undermines the legitimacy of automated decision-making, as affected individuals often lack access to meaningful explanations. In response, the field of Explainable AI (XAI) has proposed a variety of methods to enhance transparency, ranging from example-based and rule-based techniques to hybrid and argumentation-based approaches. This paper promotes computational models of arguments and their role in providing legally relevant explanations, with particular attention to their alignment with emerging regulatory frameworks such as the EU General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AIA). We analyze the strengths and limitations of different explanation strategies, evaluate their applicability to legal reasoning, and highlight how argumentation frameworks -- by capturing the defeasible, contestable, and value-sensitive nature of law -- offer a particularly robust foundation for explainable legal AI. Finally, we identify open challenges and research directions, including bias mitigation, empirical validation in judicial settings, and compliance with evolving ethical and legal standards, arguing that computational argumentation is best positioned to meet both technical and normative requirements of transparency in the law domain.

Figures (9)

• Figure 1: Example of counterfactual, semi-factual and factual explanations.
• Figure 2: Example of normative and comparative explanations.
• Figure 3: Example of the main methods of rule-based explanations.
• Figure 4: Example of the structure of an argument according to Toulmin’s model toulmin1958argumenttoulmin2003argument.
• Figure 5: Example of an argumentation graph.