SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation
This addresses the need for semantic, concept-based explanations in explainable AI without costly human-defined concepts, representing a novel method for a known bottleneck.
The paper tackled the problem of explaining black-box AI models by automatically discovering concepts without human annotation, introducing SurroCBM, which identified shared and unique concepts and used a surrogate model for post-hoc explanations, demonstrating efficacy in concept discovery and explanation through extensive experiments.
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.