Summary of Making Bias Amplification in Balanced Datasets Directional and Interpretable, by Bhanu Tokas et al.
Making Bias Amplification in Balanced Datasets Directional and Interpretable
by Bhanu Tokas, Rahul Nair, Hannah Kerner
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed paper introduces a new metric called Directional Predictability Amplification (DPA) to measure the amplification of biases in machine learning models trained on biased datasets. This issue, known as bias amplification, is exacerbated when the dataset is balanced with the task being learned. Existing metrics, such as co-occurrence-based and predictability-based methods like leakage amplification, have limitations in measuring directional biases. The authors propose DPA to address these limitations by providing an interpretable and less sensitive metric that can measure bias amplification even for balanced datasets. The paper presents experiments on tabular and image datasets demonstrating the effectiveness of DPA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to measure how machine learning models learn biases from the data they’re trained on. Right now, many datasets used in machine learning are biased, which means that models can end up being unfair or prejudiced. The authors propose a new metric called Directional Predictability Amplification (DPA) to help identify when these biases are getting worse. They also compare their new method to existing ones and show that it’s more effective. |
Keywords
» Artificial intelligence » Machine learning
