Adaptive Test-Time Intervention for Concept Bottleneck Models

• URL: http://arxiv.org/abs/2503.06730v2
• Date: Mon, 14 Apr 2025 16:39:33 GMT
• Title: Adaptive Test-Time Intervention for Concept Bottleneck Models
• Authors: Matthew Shen, Aliyah Hsu, Abhineet Agarwal, Bin Yu,
• Abstract summary: Concept bottleneck models (CBM) aim to improve model interpretability by predicting human level "concepts"<n>We propose to use Fast Interpretable Greedy Sum-Trees (FIGS) to obtain Binary Distillation (BD)<n>FIGS-BD distills a binary-augmented concept-to-target portion of the CBM into an interpretable tree-based model.
• Score: 6.31833744906105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept bottleneck models (CBM) aim to improve model interpretability by predicting human level "concepts" in a bottleneck within a deep learning model architecture. However, how the predicted concepts are used in predicting the target still either remains black-box or is simplified to maintain interpretability at the cost of prediction performance. We propose to use Fast Interpretable Greedy Sum-Trees (FIGS) to obtain Binary Distillation (BD). This new method, called FIGS-BD, distills a binary-augmented concept-to-target portion of the CBM into an interpretable tree-based model, while maintaining the competitive prediction performance of the CBM teacher. FIGS-BD can be used in downstream tasks to explain and decompose CBM predictions into interpretable binary-concept-interaction attributions and guide adaptive test-time intervention. Across 4 datasets, we demonstrate that our adaptive test-time intervention identifies key concepts that significantly improve performance for realistic human-in-the-loop settings that only allow for limited concept interventions.

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