Summary of Tree-based Leakage Inspection and Control in Concept Bottleneck Models, by Angelos Ragkousis et al.
Tree-Based Leakage Inspection and Control in Concept Bottleneck Models
by Angelos Ragkousis, Sonali Parbhoo
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach for training Concept Bottleneck Models (CBMs) to enhance interpretability while preventing information leakage. CBMs map inputs to intermediate concepts before making predictions, but often suffer from leakage when additional input data is used. The authors propose joint and sequential CBMs that can identify and control leakage using decision trees. They quantify leakage by comparing the decision paths of hard CBMs with their soft, leaky counterparts. The method shows that soft leaky CBMs extend decision paths in cases where concept information is incomplete. By controlling leakage, the technique improves task accuracy and yields more informative explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how AI models make decisions. It’s like having a special tool to look inside the model’s brain. Right now, these models can be tricky to figure out because they use extra information that isn’t really part of their “brain”. The authors created a new way to train these models so we can better understand what’s going on inside. They tested it and showed that it makes predictions more accurate and helps us understand why the model made certain decisions. |
