Fast concept-based counterfactual explanations for image classification

Abstract

We propose Fast Concept-based Counterfactual Explanations (FCCE) for generating counterfactual explanations for CNN image classification models in real time. While counterfactual explanations are intuitive for humans, existing vision methods often face a trade-off between semantic interpretability and computational efficiency, limiting their widespread adoption in interactive decision support systems. FCCE addresses this challenge by operating in a semantic concept space and deriving a closed-form analytical counterfactual solution, with the remaining computational overhead dominated by standard feature extraction. To evaluate practical utility, we conducted a between-subject human study wit..