Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

Short summary

This position paper argues that the XAI field must shift from developing ad-hoc explanation methods toward solving foundational challenges like problem formulation, evaluation objectives, and feedback pipelines. The authors analyze recent ICML, NeurIPS, and ICLR papers plus a practitioner survey to show recurring gaps that limit cumulative progress. They propose a practical checklist to move XAI toward a human-centered, action-oriented paradigm.

• •XAI explanations rarely influence real-world workflows due to foundational gaps
• •Analysis of top ML conference papers and practitioner survey reveals recurring issues
• •Proposes a checklist to shift XAI toward action-oriented, feedback-driven systems

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