评估无模型依赖的可解释AI方法是否符合欧盟人工智能法案要求
Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
- 将专家对XAI特性的定性评价转化为合规评分
- 发现现有方法在满足法案解释性要求上存在明显差距
- 为欧盟市场从业者提供合规指引，明确技术短板
可解释人工智能（XAI）的发展需回应公众期待与法规要求，如欧盟人工智能法案提出的针对人工智能系统的要求。然而，现有XAI方法与社会法律要求之间仍存在持续差距，导致从业者在进入欧盟市场时缺乏明确的合规指导。为此，本文研究了无模型依赖的XAI方法，并将其可解释性特征与人工智能法案的要求相对应。我们提出一种从定性到定量的评分框架：将专家对XAI属性的定性评估聚合为特定于法规的合规分数。该框架帮助从业者识别哪些XAI解决方案可能支持法律规定的解释要求，同时揭示需要进一步研究和技术澄清的问题。
原文摘要 · Abstract (English)
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.
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