Summary of Diverse Concept Proposals For Concept Bottleneck Models, by Katrina Brown et al.
Diverse Concept Proposals for Concept Bottleneck Models
by Katrina Brown, Marton Havasi, Finale Doshi-Velez
First submitted to arxiv on: 24 Dec 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 This paper proposes an approach to building interpretable predictive models, specifically concept bottleneck models, which are crucial in domains where trust is paramount, such as healthcare. The authors’ goal is to overcome the challenge of identifying relevant concepts from data that align with expert intuition, thereby ensuring interpretability. Their method identifies multiple alternative explanations, allowing human experts to choose the one that best fits their expectations. To demonstrate its effectiveness, the paper showcases its ability to discover all possible concept representations on a synthetic dataset and identify 4 out of 5 predefined concepts without supervision on EHR data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us build better models for healthcare by making them more understandable. It’s like giving doctors and researchers a tool to figure out why their AI-powered predictions are correct or not. Right now, these predictive models can be tricky to understand, so we need ways to make them clearer. This approach does just that by showing different explanations for how the data works. It even works well on real-world health records! The goal is to have experts choose which explanation makes the most sense. |
