Summary of Minimax Optimal Fair Classification with Bounded Demographic Disparity, by Xianli Zeng et al.
Minimax Optimal Fair Classification with Bounded Demographic Disparity
by Xianli Zeng, Guang Cheng, Edgar Dobriban
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 paper investigates the impact of using finite datasets on statistical machine learning methods aimed at reducing demographic disparity in binary classification problems. The authors explore the statistical foundations of fair classification with two protected groups, focusing on controlling acceptance rates between the groups while minimizing accuracy loss. They introduce a novel measure called fairness-aware excess risk to quantify the impact of fairness constraints and derive a minimax lower bound on this measure that all classifiers must satisfy. To achieve this, they propose FairBayes-DDP+, a group-wise thresholding method with an offset that attains the minimax lower bound. The authors demonstrate that their approach effectively controls disparity at user-specified levels while offering a favorable fairness-accuracy tradeoff compared to several baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into how using only some data affects the effort to make machine learning fairer for everyone. It’s like trying to make sure people are treated equally, even if we don’t have all the information. The authors figure out ways to control how different groups do on a test or quiz, making sure they’re not too far apart. They come up with new ideas and formulas to help make this process fairer and better. Their approach shows that it’s possible to balance fairness and accuracy when working with limited data. |
Keywords
* Artificial intelligence * Classification * Machine learning
