[论文] From Interpretability Methods to Interpretable Models

论文概要

研究领域: CV 作者: Julien Colin, Nuria Oliver, Thomas Serre 发布时间: 2026-09-04 arXiv: 2609.05399

中文摘要

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原文摘要

More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, feature visualization, concept-based, and circuit-based methods. Yet almost all of the field's effort has gone into building and comparing these methods, and little into the question they were meant to answer---how interpretable are our models, and are we making progress as they evolve? We argue for shifting the field's focus from methods to models, along two complementary lines. One is already within reach: existing tools let us characterize and compare what different models represent and compute. The other is harder, and largely neglected: whether a model can actually be understood by the humans who rely on it---the independent evaluators on whom trust and certification depend, no...

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