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Probabilistic Concept Bottleneck Models
Eunji Kim · Dahuin Jung · Sangha Park · Siwon Kim · Sungroh Yoon

Tue Jul 25 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #637

Interpretable models are designed to make decisions in a human-interpretable manner. Representatively, Concept Bottleneck Models (CBM) follow a two-step process of concept prediction and class prediction based on the predicted concepts. CBM provides explanations with high-level concepts derived from concept predictions; thus, reliable concept predictions are important for trustworthiness. In this study, we address the ambiguity issue that can harm reliability. While the existence of a concept can often be ambiguous in the data, CBM predicts concepts deterministically without considering this ambiguity. To provide a reliable interpretation against this ambiguity, we propose Probabilistic Concept Bottleneck Models (ProbCBM). By leveraging probabilistic concept embeddings, ProbCBM models uncertainty in concept prediction and provides explanations based on the concept and its corresponding uncertainty. This uncertainty enhances the reliability of the explanations. Furthermore, as class uncertainty is derived from concept uncertainty in ProbCBM, we can explain class uncertainty by means of concept uncertainty. Code is publicly available at https://github.com/ejkim47/prob-cbm.

Author Information

Eunji Kim (Seoul National University)
Dahuin Jung (Seoul National University)
Sangha Park (Seoul National University)
Siwon Kim (Seoul National University)
Sungroh Yoon (Seoul National University)

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