发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可解释人工智能（XAI）中用户难以选择合适技术的问题，研究提出多智能体大语言模型（LLM）编排工具，将用户查询转化为对应XAI解释技术，以解决选择问题。
AI 中文摘要
可解释人工智能（XAI）研究已产生大量解释技术，但用户研究反复表明，现有解释在实践中并不有效。我们认为，由于传统XAI的孤立性质，用户难以选择合适的XAI技术。从哲学视角审视XAI，我们对所谓的选择问题进行了形式化：即XAI界面系统性地未能弥合用户的自然语言不确定性与解决该不确定性的解释技术之间的差距。遵循逻辑的前提-结论格式，我们表明传统界面要求用户将自身不确定性转化为技术选择，这是一个难以满足的前提条件。我们还提出了一种结构性解决方案：一种多智能体大语言模型（LLM）编排工具，可将用户查询转化为合适的XAI解释技术。我们提供了该结构性方案的一个实例，以说明其如何解决选择问题。
英文摘要
Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not effective in practice. We argue that, given the siloed nature of conventional XAI, users are struggling to select the appropriate XAI technique. Viewing XAI through a philosophical lens, we offer a formalization of what we call the selection problem: the systematic failure of XAI interfaces to bridge the gap between a user's natural-language uncertainty and the explanation technique that resolves it. Following a logical premise-conclusion format, we show that conventional interfaces require users to translate their uncertainty into a technique selection, a challenging prerequisite to meet. We also propose a structural solution: a multi-agent LLM orchestration tool that translates the user's query to the proper XAI explanation technique. We provide an example of how this structural solution could be instantiated to address the selection problem.
CommentsAccepted to the Workshop on Explainable Artificial Intelligence at the International Joint Conference on Artificial Intelligence 2026 (XAI@IJCAI26)
