Understanding Inter-Concept Relationships in Concept-Based Models

• URL: http://arxiv.org/abs/2405.18217v1
• Date: Tue, 28 May 2024 14:20:49 GMT
• Title: Understanding Inter-Concept Relationships in Concept-Based Models
• Authors: Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik,
• Abstract summary: We analyse 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.
• Score: 12.229150338065828
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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