Summary of Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort, by Jeeyung Kim et al.
Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort
by Jeeyung Kim, Ze Wang, Qiang Qiu
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 In this paper, researchers propose a novel framework for enhancing model interpretability by leveraging foundation models to construct Concept Bottleneck Models (CBMs) with minimal human effort. The approach aims to address spurious correlations by revealing how models draw their predictions through human-understandable concepts. By utilizing multiple pre-trained models and adopting a pipeline that assesses potential biases, annotates concepts for images, and refines annotations for improved robustness, the method demonstrates its effectiveness in reducing model reliance on spurious correlations while preserving interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how AI models make predictions by showing us what they’re learning from data. The researchers created a new way to build these “Concept Bottleneck Models” that doesn’t require a lot of human work. They found that some of these models have biases and can get stuck on bad patterns in the data, which makes their predictions less trustworthy. To fix this problem, they developed a system that uses pre-trained AI models to identify potential issues, labels what’s important in an image, and then refines those labels to make them more reliable. This new approach shows promise for helping us understand how AI models work and making sure they’re not relying on fake connections. |
