用户选不对解释方法？这篇论文提出用大模型自动匹配问题与解释技术。
Addressing the Selection Problem in Explainable AI
- 用多智能体大模型将用户自然语言问题转化为对应解释方法
- 指出传统XAI界面让用户自行翻译疑问到技术，导致效果差
- 为需要解释的非专业人士提供更友好的工具设计思路
可解释人工智能（XAI）研究产生了大量解释技术，但用户研究反复表明这些解释在实践中并不有效。我们认为，由于传统XAI的孤岛化特性，用户难以选择合适的解释方法。从哲学视角出发，我们形式化提出了所谓的‘选择问题’：即XAI界面系统性地无法弥合用户自然语言表达的不确定性与能解决该问题的解释技术之间的鸿沟。遵循逻辑前提-结论结构，我们指出传统界面要求用户将自身不确定性转化为技术选择，这一挑战性前置条件难以满足。我们还提出一种结构性解决方案：一个基于多智能体大语言模型的编排工具，可将用户查询转化为恰当的XAI解释技术。我们展示了该方案如何具体实现以解决选择问题。
原文摘要 · Abstract (English)
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.
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