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Probabilistic Concept Bottleneck Models
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Probabilistic Concept Bottleneck Models
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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.
Forward citations
Cited by 9 Pith papers
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When Interpretability Becomes a Liability: Adversarial Attacks on CBM Concept Layers
Concept-level adversarial attacks exploit CBM interpretability on the CUB dataset, but SPECTRA raises required perturbation norm from 0.46 to over 4200 while keeping accuracy loss under 2.2%.
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Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
Introduces synthetic benchmarks for concept bottleneck models that control data modality, concept choice, annotation quality, and completeness to evaluate performance in decision support and automation.
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Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model
A new semi-supervised hypergraph Concept Bottleneck Model framework improves label efficiency and interpretability for medical image diagnosis on PAS ultrasound, breast ultrasound, and SkinCon datasets.
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SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches
SynCB adds a dynamic routing module and joint training to a hybrid concept-plus-neural architecture, reporting up to 3.9 pp higher accuracy than a full neural baseline and up to 6.43 pp better intervention responsiven...
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CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models
CLIF applies influence functions to pinpoint influential samples and concepts in CBMs on CEBaB and Yelp datasets, enabling performance restoration via adjustments without retraining.
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A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding
A 'tool bottleneck' framework—VLM tool selection plus learned spatial fusion—matches or beats black-box classifiers, especially on scarce data.
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CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models
CLIF applies influence functions to pinpoint influential training samples and key concepts in Concept Bottleneck Models, enabling data debugging and behavioral insights on CEBaB and Yelp datasets.
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ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
A perturbation-and-surrogate audit shows MedSAM and VLM retinal concept explanations have pathway- and concept-specific reliability, not automatic trustworthiness.
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Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Formal concept lattices guide staged, hierarchical concept learning in deep networks to produce more interpretable and semantically structured representations.
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