Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements

Researchers have developed a framework to assess how well existing explainable AI (XAI) methods comply with the EU AI Act's transparency requirements. The study bridges the gap between current XAI techniques and regulatory mandates by proposing a scoring system that translates expert qualitative assessments into quantitative compliance metrics, helping practitioners navigate AI regulation in European markets.

The EU AI Act represents a watershed moment for AI governance, establishing mandatory explainability standards for high-risk AI systems. This research addresses a critical implementation challenge: while regulations now demand interpretability, the technical community lacks standardized methods to verify compliance. The study examines model-agnostic XAI approaches—techniques that work across different AI architectures—and maps their capabilities against specific regulatory requirements, recognizing that legal and technical definitions of 'explainability' often diverge.

The framework's significance lies in its practical utility. Rather than declaring XAI methods universally compliant or non-compliant, the researchers propose a nuanced scoring approach that aggregates expert assessments into regulation-specific scores. This acknowledges that compliance exists on a spectrum and varies depending on use case context. For AI developers and companies entering EU markets, this provides clearer direction than abstract regulatory language alone.

The broader implications extend beyond Europe. As other jurisdictions—including the UK, Singapore, and potentially the US—develop AI regulations, similar compliance frameworks will become essential infrastructure. This research highlights persistent technical gaps: current XAI methods may satisfy some regulatory dimensions while failing others, indicating where innovation remains necessary. Regulators also benefit by identifying which requirements demand technological advances versus regulatory clarification.

Looking ahead, the field must move toward standardized compliance assessment tools and potentially certification systems. Organizations should track how this framework evolves and whether regulatory bodies formally adopt it as guidance. The intersection of technical XAI capabilities and legal requirements will increasingly determine which AI vendors can operate in regulated markets.

• →Model-agnostic XAI methods have interpretability features that partially but inconsistently align with EU AI Act explainability requirements.
• →The proposed scoring framework translates qualitative expert assessments of XAI properties into quantitative compliance metrics tailored to specific regulations.
• →Significant gaps remain between current XAI capabilities and legal requirements, indicating where technical innovation and regulatory clarification are needed.
• →Practitioners now have a structured methodology to evaluate whether XAI solutions adequately support legal explanation requirements for EU market entry.
• →This framework provides a template that other jurisdictions developing AI regulations can adapt for their own compliance assessment needs.