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This research bridges Consistency-Based Diagnosis and actual causality frameworks to advance Explainable AI (XAI), offering a theoretical foundation for more robust diagnostic reasoning in machine learning systems. By linking these distinct areas, the work aims to improve both XAI methodologies and explainable data management practices.

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

arXivAugust 25, 20261 min read

arXiv:2605.08688v2 Announce Type: replace Abstract: 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.

Abstract: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.

Several typos fixed, and remarks added. To appear in Proc. of The 17th International Conference on Scalable Uncertainty Management (SUM'26), Springer LNCS, 2026

Artificial Intelligence (cs.AI); Databases (cs.DB); Logic in Computer Science (cs.LO)

arXiv:2605.08688 [cs.AI]

(or arXiv:2605.08688v2 [cs.AI] for this version)

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