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A concept-based classifier can explain the decision process of a deeplearning model by human-understandable concepts in image classificationproblems. However, sometimes concept-based explanations may cause falsepositives, which misregards unrelated concepts as important for the predictiontask. Our goal is to find the statistically significant concept forclassification to prevent misinterpretation. In this study, we propose a methodusing a deep learning model to learn the image concept and then using theKnockoff samples to select the important concepts for prediction by controllingthe False Discovery Rate (FDR) under a certain value. We evaluate the proposedmethod in our synthetic and real data experiments. Also, it shows that ourmethod can control the FDR properly while selecting highly interpretableconcepts to improve the trustworthiness of the model.

Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs