Summary of Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning, by Ao Sun et al.
Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
by Ao Sun, Yuanyuan Yuan, Pingchuan Ma, Shuai Wang
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores a new approach to generating interpretable and interventionable predictions using Concept Bottleneck Models (CBMs). By bridging features and labels with human-understandable concepts, CBMs have shown promising results. However, recent advancements in this area have been hindered by information leakage, where unintended data is leaked from the concept representation to the subsequent label prediction. This issue can lead to false classifications between classes that are indistinguishable based on their concepts, undermining the interpretability and intervention capabilities of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CBMs aim to make predictions easier to understand and change by linking features and labels with simple ideas we can grasp. While this approach has shown promise, it has a problem called information leakage. This means that extra details are being included in the prediction that aren’t part of the main idea. As a result, similar groups might be mistakenly classified because they have similar underlying concepts. |
