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

2025-10-13

AI Summary

The “black-box” nature of legal AI severely undermines fairness, accountability, and legitimacy in judicial decision-making—particularly due to the absence of meaningful, party-oriented explanations. To address this, we propose a legally compliant computational argumentation model that innovatively integrates defeasibility, contestability, and value-sensitivity into an explainable AI framework, explicitly aligned with regulatory requirements including the GDPR and the EU AI Act. Methodologically, we unify rule-based, example-based, and hybrid explanation techniques to construct an argumentation system faithful to the structure of legal reasoning. Empirical evaluation demonstrates that the model satisfies both technical traceability and normative auditability—the dual transparency requirements for legal AI—while significantly improving explanation quality, enabling bias detection, and supporting judicial verification. Our work thus provides both theoretical foundations and practical pathways toward lawful, trustworthy legal AI systems.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Addressing legal AI opacity challenges fairness and accountability
• Providing legally relevant explanations through argumentation-based frameworks
• Ensuring compliance with GDPR and AI Act regulations

Innovation

• Argumentation frameworks for legal AI explainability
• Computational models align with GDPR and AIA regulations
• Argumentation captures law's defeasible and value-sensitive nature