Research Summary: Against Explainable Artificial Intelligence In Law: Why Justifiable Ai Matters. A Credit Scoring Example

Research from arXiv highlights that the increasing complexity of AI in applications such as credit scoring raises concerns about explainability. The paper reviews EU legal frameworks and technical capabilities to argue that a broad interpretation of the 'right to explanation' is necessary. This interpretation should encompass not only technical explanations but also legal justification to effectively safeguard credit applicants' rights, challenging narrow views on explainable AI in law.

Why it matters

This analysis is strategically important for entities deploying AI systems, particularly in regulated financial sectors like credit scoring, as it underscores the evolving regulatory expectations for AI explainability. Understanding the distinction between technical explanation and legal justification is critical for compliance and maintaining public trust in AI-driven decision-making.

Key insights

• AI solutions, including those in credit scoring, offer significant efficiency gains.
• The growing sophistication of machine learning models in use is a source of concern regarding various aspects, including explainability.
• A review of relevant EU legal backgrounds integrated with technical insights is necessary to interpret legal provisions in light of technological possibilities.
• The concept of 'explainable AI' (XAI) should be interpreted broadly to include both technical explanations and legal justifications.
• A broad interpretation of the right to explanation is essential to operatively safeguard creditors' rights.
• The paper argues against narrow interpretations of the right to explanation in law, advocating for 'justifiable AI'.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.07452