Summary of Bayes-optimal Fair Classification with Linear Disparity Constraints Via Pre-, In-, and Post-processing, by Xianli Zeng et al.
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
by Xianli Zeng, Guang Cheng, Edgar Dobriban
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); 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 Machine learning algorithms may have disparate impacts on protected groups. This paper develops methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints. The authors introduce linear disparity measures, which are linear functions of a probabilistic classifier, and bilinear disparity measures, which are also linear in the group-wise regression functions. Notably, they show that several popular disparity measures – including deviations from demographic parity, equality of opportunity, and predictive equality – are bilinear. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms can have different effects on different groups. To fix this, scientists developed new ways to make classification fairer. They created methods for Bayes-optimal fair classification, which tries to reduce mistakes while following fairness rules. The team introduced two types of measures: linear and bilinear. Linear measures are based on the classifier itself, while bilinear ones look at how groups respond differently. They found that some popular fairness measures are actually both linear and bilinear. |
Keywords
* Artificial intelligence * Classification * Machine learning * Regression
