发表机构
Poznan University of Technology（波兹南工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对可解释人工智能的评估不足，以DetoxAI系统为例展开研究，探索了演化数据流的解释适配方法，最后关联了数据、模型与解释协同演化的跟踪挑战。
AI 中文摘要
本文针对可解释人工智能（XAI）在评估方面存在的不足展开研究，通过DetoxAI图像识别系统（用于偏差检测与概念遗忘）阐明了这些不足。随后，给出了图像分类解释方法的基于人类认知的评估示例，进一步探索了针对带有概念漂移的演化数据流的解释适配方法，讨论了将反事实方法适配到该问题的相关经验，最后将上述内容与数据、模型及解释协同演化的跟踪挑战关联起来。本文已被收录至IJCAI-ECAI 2026不来梅会议的EASi 2026研讨会论文集《Explainable AI in Space》，该论文集属于Springer CCIS第3107卷（2016年）。
英文摘要
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).}
Comments13 pages, 1 figure = this paper is a preprint of the workshop [Explainable AI in Space] paper for IJCAI ECAI 2026 conference
