Prototype-Grounded Concept Models for Verifiable Concept Alignment

• URL: http://arxiv.org/abs/2604.16076v1
• Date: Fri, 17 Apr 2026 14:04:14 GMT
• Title: Prototype-Grounded Concept Models for Verifiable Concept Alignment
• Abstract summary: Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts.<n>We introduce Prototype-Grounded Concept Models (PGCMs), which ground concepts in learned visual prototypes.<n>PGCMs match the predictive performance of state-of-the-art CBMs while substantially improving transparency, interpretability, and intervenability.
• Score: 21.877856489908797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, but they provide no way to verify whether learned concepts align with the human's intended meaning, hurting interpretability. We introduce Prototype-Grounded Concept Models (PGCMs), which ground concepts in learned visual prototypes: image parts that serve as explicit evidence for the concepts. This grounding enables direct inspection of concept semantics and supports targeted human intervention at the prototype level to correct misalignments. Empirically, PGCMs match the predictive performance of state-of-the-art CBMs while substantially improving transparency, interpretability, and intervenability.

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