Challenges in Evaluating Explanation Methods for Static and Evolving Data

Abstract:This paper addresses the limitations of Explainable Artificial Intelligence (XAI) with respect to insufficient evaluation. They are illustrated through the DetoxAI image recognition system for bias detection and concept unlearning. Then, an example of a human-grounded evaluation of methods for explaining image classification is presented. The paper further explores methods for adapting explanations to evolving data streams with concept drift. Experiences with adapting counterfactuals for this problem are discussed. Finally it is related to the challenges of tracking the co-evolution of data, models, and explanations.\footnote{This paper has been accepted for a publication in this http URL (ed) Explainable AI in Space. Proceedings of EASi 2026 Workshop at IJCAI-ECAI 2026 Bremen, Springer CCIS vol 3107 (2016).}

Artificial Intelligence (cs.AI)

arXiv:2608.06351 [cs.AI]

(or arXiv:2608.06351v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.06351 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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