发现正式解释器的漏洞，验证了其实际实现中的错误解释问题。
Uncovering Bugs in Formal Explainers: A Case Study with PyXAI
- 提出新方法验证形式化解释器的正确性
- 发现PyXAI在多数数据集上给出错误解释
- 适合关注可解释人工智能可靠性的人士
形式化可解释人工智能（XAI）相比其他非形式化方法具有独特的理论保障。然而，关于形式化解释器实际实现的验证研究仍十分有限。本文提出一种新型验证方法，并对公开的正式解释器PyXAI进行了评估。实验表明，PyXAI在多数数据集上生成了不正确的解释，证实了所提验证方法的重要性，凸显了形式化解释器实现中潜在缺陷的严重性。
原文摘要 · Abstract (English)
Formal explainable artificial intelligence (XAI) offers unique theoretical guarantees of rigor when compared to other non-formal methods of explainability. However, little attention has been given to the validation of practical implementations of formal explainers. This paper develops a novel methodology for validating formal explainers and reports on the assessment of the publicly available formal explainer PyXAI. The paper documents the existence of incorrect explanations computed by PyXAI on most of the datasets analyzed in the experiments, thereby confirming the importance of the proposed novel methodology for the validation of formal explainers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。