The eXplainable Artificial Intelligence Paradox in Law: Technological Limits and Legal Transparency

Abstract

The integration of Artificial Intelligence (AI) into legal systems offers transformative potential, promising enhanced efficiency and predictive accuracy. However, this progress also brings to the spotlight the explainability paradox: the unavoidable trade-off between the accuracy of complex Machine Learning (ML) and Deep Learning (DL) models and their lack of transparency. This paradox challenges foundational legal principles such as fairness, due process, and the right to explanation. While eXplainable AI (XAI) techniques have emerged to address this issue, their post-hoc nature, limited fidelity, and inaccessibility to non-expert stakeholders impede their practical utility in legal contexts. This paper critically reflects on the explainability paradox and its implications for AI-assisted legal decision-making, proposing a balanced framework to reconcile accuracy with transparency. By examining the limitations of current XAI methods and exploring the potential of inherently interpretable models, it highlights pathways to align AI systems with the procedural and ethical standards of the legal domain. These reflections not only address a gap in existing research but also challenge conventional reliance on opaque models, advocating for AI systems that prioritize trust, accountability, and legitimacy. This reflection invites interdisciplinary dialogue and encourages the development of AI tools that integrate technical performance with ethical and societal needs, ensuring the responsible adoption of AI in law.