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Hierarchical Concept-based Interpretable Models

arXiv:2602.23947v1 Announce Type: new Abstract: Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, and debiasing. Concept Embedding Models (CEMs) address this by mapping inputs to human-interpretable concept representations from which tasks can be predicted. Yet, CEMs fail to represent inter-concept relationships and require concept annotations at different granularities during training, limiting their applicability. In this paper, we introduce Hierarchical Concept Embedding Models (HiCEMs), a new family of CEMs that explicitly model concept relationships through hierarchical structures. To enable HiCEMs in real-world settings, we propose Concept Splitting, a method for automatically discovering finer-grained sub-concepts from a pretrained CEM's embedding space without requiring additional annotations. This allows HiCEMs to generate fine-grained explanations from limited concept label

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Oscar Hill, Mateo Espinosa Zarlenga, Mateja Jamnik · · 1 min read · 13 views
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arXiv:2602.23947v1 Announce Type: new Abstract: Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, and debiasing. Concept Embedding Models (CEMs) address this by mapping inputs to human-interpretable concept representations from which tasks can be predicted. Yet, CEMs fail to represent inter-concept relationships and require concept annotations at different granularities during training, limiting their applicability. In this paper, we introduce Hierarchical Concept Embedding Models (HiCEMs), a new family of CEMs that explicitly model concept relationships through hierarchical structures. To enable HiCEMs in real-world settings, we propose Concept Splitting, a method for automatically discovering finer-grained sub-concepts from a pretrained CEM's embedding space without requiring additional annotations. This allows HiCEMs to generate fine-grained explanations from limited concept labels, reducing annotation burdens. Our evaluation across multiple datasets, including a user study and experiments on PseudoKitchens, a newly proposed concept-based dataset of 3D kitchen renders, demonstrates that (1) Concept Splitting discovers human-interpretable sub-concepts absent during training that can be used to train highly accurate HiCEMs, and (2) HiCEMs enable powerful test-time concept interventions at different granularities, leading to improved task accuracy.

Executive Summary

Hierarchical Concept Embedding Models (HiCEMs) address the opacity of deep neural networks by introducing a hierarchical concept-based structure, enabling the automatic discovery of finer-grained sub-concepts through Concept Splitting. This innovation allows for human-interpretable explanations and powerful test-time concept interventions, leading to improved task accuracy. The proposed method reduces annotation burdens and expands the applicability of Concept Embedding Models (CEMs). The evaluation across multiple datasets demonstrates the effectiveness of HiCEMs in real-world settings, with promising results on a user study and the newly proposed PseudoKitchens dataset.

Key Points

  • ▸ HiCEMs introduce hierarchical concept-based structures to explicitly model concept relationships
  • ▸ Concept Splitting automatically discovers finer-grained sub-concepts from pre-trained CEMs' embedding space
  • ▸ HiCEMs enable powerful test-time concept interventions at different granularities, leading to improved task accuracy

Merits

Strength in Addressing Concept Opacity

HiCEMs address the opacity of deep neural networks by introducing a hierarchical concept-based structure, making it easier to understand and interpret model behavior.

Improved Task Accuracy

HiCEMs enable powerful test-time concept interventions, leading to improved task accuracy and reducing the need for extensive annotations.

Increased Applicability

The proposed method reduces annotation burdens and expands the applicability of CEMs, making them more suitable for real-world applications.

Demerits

Dependence on Pre-trained CEMs

HiCEMs rely on pre-trained CEMs, which may limit their generalizability and require significant computational resources for training.

Potential Overfitting

The hierarchical structure of HiCEMs may lead to overfitting, particularly when dealing with concept annotations at different granularities.

Expert Commentary

The introduction of HiCEMs marks a significant advancement in addressing the opacity of deep neural networks. By incorporating hierarchical concept-based structures, HiCEMs effectively enable the automatic discovery of finer-grained sub-concepts, reducing annotation burdens and expanding the applicability of CEMs. The proposed method has promising implications for various domains, including XAI, and policy-making. However, it is essential to address the potential limitations, such as dependence on pre-trained CEMs and overfitting. Future research should focus on exploring the generalizability of HiCEMs and developing strategies to mitigate these limitations.

Recommendations

  • ✓ Future research should investigate the application of HiCEMs in diverse domains, including healthcare, finance, and education.
  • ✓ Developing strategies to mitigate the limitations of HiCEMs, such as dependence on pre-trained CEMs and overfitting, is crucial for widespread adoption.

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