Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Despite the proliferation of XAI techniques, explanations rarely influence real-world workflows. This position paper argues for shifting focus to foundational challenges such as unclear problem formulations, underspecified evaluation objectives, and lack of pipelines for explanation-driven feedback. Based on an analysis of recent papers and a survey of practitioners, the authors propose a checklist to move XAI toward a more human-centered, action-oriented paradigm.
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[Submitted on 18 Jun 2026]
Title:Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
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Abstract: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.
Comments: Accepted to ICML 2026 Position Paper Track
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.14123 [cs.LG]
(or arXiv:2607.14123v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.14123
arXiv-issued DOI via DataCite
Journal reference: Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026
Submission history
From: Suraj Srinivas [view email] [v1] Thu, 18 Jun 2026 21:46:20 UTC (146 KB)
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