Addressing the Selection Problem in Explainable AI

2026-08-23

AI Summary

本文针对可解释AI中的选择问题，提出了一种多代理LLM协调工具，将用户的自然语言查询转化为合适的解释技术，以解决用户难以选择合适XAI技术的问题。

Abstract

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.

Problem

• Explainable AI
• Selection Problem
• User Uncertainty

Innovation

• multi-agent LLM orchestration
• natural-language uncertainty
• XAI technique selection