Understanding Inter-Concept Relationships in Concept-Based Models
<|reference_start|>Understanding Inter-Concept Relationships in Concept-Based Models: 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.<|reference_end|>
@article{raman2024understanding,
    title={Understanding Inter-Concept Relationships in Concept-Based Models},
    author={Naveen Raman, Mateo Espinosa Zarlenga, and Mateja Jamnik},
    journal={arXiv preprint arXiv:2405.18217},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18217},
    primaryClass={cs.LG}
}