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.

翻译：可解释人工智能旨在揭示黑盒模型的决策过程。传统的基于显著性方法虽能突出显示重要数据片段，但往往缺乏语义理解。近期成果如概念激活向量（CAVs）和概念瓶颈模型（CBMs）提供了基于概念的解释方式，但需依赖人类定义的概念。然而，人工标注概念成本高昂。本文提出概念瓶颈替代模型（SurroCBM），该新框架旨在通过自动发现的概念解释黑盒模型。SurroCBM能识别不同黑盒模型之间的共享概念与独特概念，并采用可解释替代模型进行事后解释。我们提出一种使用自生成数据的有效训练策略，以持续提升解释质量。通过大量实验，我们证明了SurroCBM在概念发现与解释方面的有效性，凸显其在推动可解释人工智能领域发展的潜力。