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What’s in the Bottle? a Survey and Roadmap of Concept Bottleneck Models

MCML Authors

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Udo Schlegel

→ Group Thomas Seidl
Database Systems, Data Mining and AI

Abstract

Concept Bottleneck Models (CBMs) are interpretable learning architectures that factor predictions through intermediate, ideally human-understandable concepts, enabling explicit and inspectable reasoning. Although CBM research has gained substantial momentum in recent years, this growth has also revealed numerous open challenges and a fragmented set of methodological choices. In this work, we systematically review the CBM literature, identify previously unidentified core components and challenges, and propose a unified taxonomy. Based on this taxonomy, we provide a detailed categorization of existing works. We hereby discuss current challenges for the CBM paradigm and outline important directions to extend it beyond its current scope. Overall, this survey aims to consolidate the CBM landscape, clarify open issues, and provide guidance for developing future models.

misc KSB+26