剖析静态与动态数据下解释方法的评估难题，提出改进思路。
Challenges in Evaluating Explanation Methods for Static and Evolving Data
- 用图像识别系统展示现有解释方法的评估缺陷
- 提出基于人类反馈的图像分类解释评估范式
- 探讨概念漂移下解释自适应与三者共演化挑战
本文针对可解释人工智能（XAI）评估不足的问题展开研究，以DetoxAI图像识别系统在偏见检测与概念遗忘中的应用为例进行说明。通过构建基于人类反馈的图像分类解释评估案例，揭示当前方法的局限性。进一步探讨了在数据流概念漂移背景下，如何使解释方法实现自适应调整，特别是对反事实解释的适应经验进行了分析。最后，论文讨论了数据、模型与解释三者共同演进过程中的跟踪难题，强调了长期评估框架的重要性。
原文摘要 · Abstract (English)
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 J.Nalepa (ed) Explainable AI in Space. Proceedings of EASi 2026 Workshop at IJCAI-ECAI 2026 Bremen, Springer CCIS vol 3107 (2016).}
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