将一致性诊断与实际因果解释关联，提升AI可解释性。

Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations

• 构建一致性诊断与实际因果的理论桥梁
• 揭示两种解释范式间的深层联系
• 适合研究AI可解释性的学者参考

从可解释人工智能（XAI）的角度出发，本文建立了基于一致性的诊断（CBD）与实际因果性及因果责任之间的联系。尽管CBD在XAI领域尚未受到足够关注，但这两个领域的结合可能对可解释人工智能和可解释数据管理产生深远影响。

原文摘要 · Abstract (English)

We establish, from the point of view of Explainable AI (XAI), connections between Consistency-Based Diagnosis (CBD), on one side, and Actual Causality and Causal Responsibility, on the other. CBD has received little attention from the XAI community. Connections between these two areas could have a fruitful impact on XAI and Explainable Data Management.

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