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

Argumentation-based explanations are uniquely suited for legal AI because they can capture the defeasible and contestable nature of legal reasoning—meaning they can represent how legal arguments can be challenged, overruled, or depend on competing values, rather than presenting decisions as fixed logical outputs.

Current "black box" AI systems undermine legal legitimacy by denying affected individuals meaningful explanations for automated decisions, which creates accountability gaps that argumentation-based approaches can help address by making the reasoning process transparent and contestable.

Emerging regulations like the EU's GDPR and AI Act require explainability, and the paper argues that computational argumentation frameworks are better positioned than simpler explanation methods (like example-based or rule-based techniques alone) to meet both the technical transparency requirements and the normative legal standards these regulations demand.

Significant research gaps remain in deploying explainable legal AI, particularly around empirical validation of these systems in actual judicial settings, bias mitigation strategies, and ensuring compliance with evolving ethical standards—suggesting that argumentation-based approaches are promising but not yet fully tested in practice.

Different explanation strategies have distinct strengths and limitations for legal contexts, and the paper advocates for argumentation frameworks specifically because they can represent the value-sensitive dimensions of law that simpler technical explanations often miss.