Understanding Inter-Concept Relationships in Concept-Based Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929362354307072 |
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| author | Raman, Naveen Zarlenga, Mateo Espinosa Jamnik, Mateja |
| author_facet | Raman, Naveen Zarlenga, Mateo Espinosa Jamnik, Mateja |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_18217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Inter-Concept Relationships in Concept-Based Models Raman, Naveen Zarlenga, Mateo Espinosa Jamnik, Mateja Machine Learning 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. |
| title | Understanding Inter-Concept Relationships in Concept-Based Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.18217 |