ai
Against Explainable Artificial Intelligence In Law: Why Justifiable Ai Matters. A Credit Scoring Example
- Source
- arXiv — Computers and Society
- Published
- Last verified
- 10 Aug 2026
- Confidence
- High
- Evidence
- Original document retained
- Reading time
- 1 min
- Country
- International
- Relevant to
- Technology & Data, Research & Evidence, Risk & Compliance
Executive summary
What happened, and why should leadership care?
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 this matters
Why is this strategically important?
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
What should be noted from the evidence?
- 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.
Evidence and confidence
How far can this assessment be trusted?
High confidence. Named institution, original document retained and analysis corroborated.
Analysis is prepared editorially by Aziz Shuaib Ausi. The original publication remains the authoritative record, and executive judgement remains entirely human.
Source
Where does this originate?
Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.
Read the original publication