立场：可解释性研究必须优先考虑基础而非临时方法
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
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中文总结 AI 辅助
该论文指出可解释人工智能技术虽多但未有效影响实际工作流程，原因是存在基础缺陷。主张机器学习社区从临时方法转向解决基础和结构挑战，通过分析论文和调查从业者揭示问题，最后给出清单推动XAI走向以人为本、行动导向范式。
中文摘要 AI 辅助
尽管可解释人工智能（XAI）技术激增，从特征归因到稀疏自动编码器，但解释很少影响实际工作流程。实际上，它们常常在未引导有意义行动的情况下被生成和丢弃。这种差距反映了基础缺陷：研究尚未建立将解释整合到端到端、人在回路系统中的方法。本文认为机器学习社区必须从临时的XAI方法转向解决基础和结构挑战，包括问题表述不清、评估目标不明确以及缺乏解释驱动反馈的管道。通过对近期ICML、NeurIPS和ICLR论文的分析以及对XAI从业者的调查，揭示了限制累积进展的反复出现的问题。最后概述了一个实用清单，旨在将XAI转向更以人为本、以行动为导向的范式。通过强调基础清晰度而非临时方法的开发，希望为将解释整合到可操作、反馈驱动的人工智能系统提供路线图。
英文摘要
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems. This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress. We conclude by outlining a practical checklist designed to shift XAI toward a more human-centered, action-oriented paradigm. By emphasizing foundational clarity over the development of ad-hoc methods, we hope to provide a roadmap for integrating explanations into actionable, feedback-driven AI systems.
