Explainable AI seeks to bring light to the decision-making processes of black-box models. Traditional saliency-based methods, while highlighting influential data segments, often lack semantic understanding. Recent advancements, such as Concept Activation Vectors (CAVs) and Concept Bottleneck Models (CBMs), offer concept-based explanations but necessitate human-defined concepts. However, human-annotated concepts are expensive to attain. This paper introduces the Concept Bottleneck Surrogate Models (SurroCBM), a novel framework that aims to explain the black-box models with automatically discovered concepts. SurroCBM identifies shared and unique concepts across various black-box models and employs an explainable surrogate model for post-hoc explanations. An effective training strategy using self-generated data is proposed to enhance explanation quality continuously. Through extensive experiments, we demonstrate the efficacy of SurroCBM in concept discovery and explanation, underscoring its potential in advancing the field of explainable AI.

本文介绍了Concept Bottleneck Surrogate Models (SurroCBM)的新颖框架，旨在通过自动发现的概念来解释黑盒模型。通过自动生成的数据提供了有效的训练策略，不断提高解释质量，并通过广泛实验证明了SurroCBM在概念发现和解释方面的有效性，突显了其在可解释AI领域的潜力。

SurroCBM: 生成后处理解释的概念瓶颈替代模型