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Understanding Inter-Concept Relationships in Concept-Based Models

Authors :
Raman, Naveen
Zarlenga, Mateo Espinosa
Jamnik, Mateja
Publication Year :
2024

Abstract

Concept-based explainability methods provide insight into deep learning systems by constructing explanations using human-understandable concepts. While the literature on human reasoning demonstrates that we exploit relationships between concepts when solving tasks, it is unclear whether concept-based methods incorporate the rich structure of inter-concept relationships. We analyse the concept representations learnt by concept-based models to understand whether these models correctly capture inter-concept relationships. First, we empirically demonstrate that state-of-the-art concept-based models produce representations that lack stability and robustness, and such methods fail to capture inter-concept relationships. Then, we develop a novel algorithm which leverages inter-concept relationships to improve concept intervention accuracy, demonstrating how correctly capturing inter-concept relationships can improve downstream tasks.<br />Comment: Accepted at ICML 2024

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2405.18217
Document Type :
Working Paper