从可解释性方法到可解释模型

中文摘要 AI 辅助

可解释人工智能（XAI）在计算机视觉领域已发展十余年，形成了成熟的工具集：归因法、特征可视化法、基于概念的方法和基于回路的方法。然而该领域几乎所有努力都集中在构建和比较这些方法上，却很少关注其旨在回答的核心问题——我们的模型可解释性如何？随着模型的演进是否取得了进展？本文主张将该领域的重点从方法转向模型，沿两条互补路线推进：一条已触手可及，现有工具可用于表征和比较不同模型的表征与计算内容；另一条更困难且被忽视，即模型是否能被依赖它的人类真正理解——这些人类是信任和认证所依赖的独立评估者，而非确认已有预期的专家。这只能被测量，不能被推断。本文综述了工具集为何足够成熟以支持这两条路线，梳理了为数不多的模型比较研究，将其与系统神经科学进行类比，并以一个以模型为中心的XAI议程作结。

英文摘要

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, not the experts confirming what they already expect. It can only be measured, not inferred. We review why the toolbox is mature enough to support both, survey the thin body of work comparing models, draw a parallel to systems neuroscience, and close with a model-centric XAI agenda.

发表机构

• ELLIS Alicante（ELLIS 阿利坎特）
• Carney Institute for Brain Science, Brown University（布朗大学卡尼脑科学研究所）
• Department of Cognitive and Psychological Sciences, Brown University（布朗大学认知与心理科学系）

机构由 AI 辅助整理，请以论文原文为准。