Beyond the Black Box: Architecting Explainable AI for the Structured Logic of Law

Recent research highlights a fundamental challenge in applying standard explainable AI (XAI) techniques to legal reasoning, emphasizing the epistemic gap between AI explanations and legal justification processes. While AI models often utilize attention maps and counterfactuals to elucidate decision-making, these methods primarily reveal superficial correlations, such as which text segments influenced a model's output, without capturing the hierarchical and precedent-driven structure intrinsic to legal reasoning. This discrepancy undermines the ability of current XAI approaches to provide legally meaningful explanations, as they fail to account for the layered authority of statutes, precedents, and principles that underpin legal