Summary of Bridging Fairness Gaps: a (conditional) Distance Covariance Perspective in Fairness Learning, by Ruifan Huang et al.
Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective in Fairness Learning
by Ruifan Huang, Haixia Liu
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 approach addresses fairness concerns in machine learning by leveraging conditional distance covariance or distance covariance statistics to assess the independence between predictions and sensitive attributes. A penalty term is incorporated into the machine learning process to enhance fairness, while a matrix form of empirical (conditional) distance covariance enables parallel calculations for improved computational efficiency. Theoretical guarantees are established through convergence proofs between empirical and population (conditional) distance covariance. Experimental results on various real-world datasets demonstrate the effectiveness of this method in bridging fairness gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re trying to make machine learning more fair by using special math tools. We want to make sure that the predictions aren’t based on things we shouldn’t be considering, like a person’s race or gender. To do this, we use something called “distance covariance” which helps us see if there’s any connection between what our model is predicting and these sensitive attributes. We also made some math changes so it can run faster on computers. Our tests showed that this way of doing things actually makes machine learning more fair. |
Keywords
» Artificial intelligence » Machine learning
