Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
Summary: arXiv:2604.09628v1 Announce Type: cross
Abstract: Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-powered systems. However, a persistent gap remains between existing XAI methods and society’s legal requirements, leaving practitioners without clear guidance on how to approach compliance in the EU market. To bridge this gap, we study model-agnostic XAI methods and relate their interpretability features to the requirements of the AI Act. We then propose a qualitative-to-quantitative scoring framework: qualitative expert assessments of XAI properties are aggregated into a regulation-specific compliance score. This helps practitioners identify when XAI solutions may support legal explanation requirements while highlighting technical issues that require further research and regulatory clarification.
Introduction
The emergence of Explainable AI (XAI) reflects a growing demand for transparency and accountability in artificial intelligence systems. As regulatory frameworks like the EU AI Act come into play, the need for compliance with legal explanation requirements becomes paramount. This article explores the relationship between model-agnostic XAI methods and the stipulations outlined in the EU AI Act.
Understanding the EU AI Act
The EU AI Act is a pioneering regulatory effort aimed at ensuring that AI technologies are safe and respect fundamental rights. Key provisions include:
- Risk-based categorization of AI systems
- Transparency obligations for high-risk AI applications
- Accountability measures for AI deployments
- Requirements for human oversight and interpretability
Model-Agnostic XAI Methods
Model-agnostic XAI methods provide interpretability across various AI models without being tied to specific algorithms. These methods are crucial in fulfilling the explainability requirements set forth by the EU AI Act. Some prominent model-agnostic techniques include:
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Feature Importance Mapping
Bridging the Gap
Despite the potential of model-agnostic XAI methods, there remains a significant gap between their capabilities and the legal expectations outlined in the EU AI Act. This gap can be attributed to several factors:
- Variability in the quality of explanations provided by different XAI methods
- Lack of standardized metrics for assessing compliance with legal requirements
- Insufficient guidance for practitioners on selecting appropriate XAI solutions
A Scoring Framework for Compliance
To address these challenges, we propose a novel scoring framework that transforms qualitative assessments of XAI properties into a quantitative compliance score. This framework involves:
- Conducting expert evaluations of various XAI methods
- Aggregating findings into a compliance score specific to the EU AI Act
- Providing practitioners with insights into the suitability of XAI options for their systems
Conclusion
The intersection of XAI and regulatory compliance presents both challenges and opportunities. By developing a structured framework for assessing model-agnostic XAI methods against the EU AI Act requirements, we can enhance the clarity and applicability of explainable AI in the EU market. This initiative not only aids practitioners in navigating compliance but also highlights areas where further research and regulatory guidance are necessary.