XAI评估怎么做才靠谱？这篇论文用DetoxAI和概念漂移案例讲清了不足和应对思路，适合搞模型解释的人读。
该论文指出可解释人工智能（XAI）在评估不足方面的局限性，并以DetoxAI图像识别系统为例说明偏见检测与概念遗忘中的问题。论文展示了一种人工地面评估图像分类解释方法的具体案例，并探讨了在概念漂移下适应解释的方法。作者还分享了为概念漂移问题调整反事实解释的经验，并讨论了数据、模型与解释共同演化带来的追踪挑战。该文已收录于IJCAI-ECAI 2026的EASi研讨会论文集，由Springer CCIS第3107卷出版。
Challenges in Evaluating Explanation Methods for Static and Evolving Data
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).}