SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc
  Explanation
Bo Pan Zhenke Liu Yifei Zhang Liang Zhao
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.