Explainability research should prioritize foundations over ad-hoc methods; position paper argues for integrating explanations into end-to-end, human-in-the-loop systems.
Read the original at arxiv.org→arXiv:2607.14123v1 Announce Type: new Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows....
Original headline: "Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods"
Coverage timeline
- Jul 17, 04:00 UTC arXiv cs.LG lead source Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
- Jul 17, 04:00 UTC arXiv cs.LG Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models