Research map / Learning theory and algorithms / Fairness
Testing Group Fairness via Optimal Transport Projections
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using the optimal-transport fairness test · Arrhythmia, equal opportunity | rejection percentage: 68.4% -> 11.6% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using the optimal-transport fairness test · COMPAS, equal opportunity | rejection percentage: 100% -> 16.6% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using the optimal-transport fairness test · Drug, equal opportunity | rejection percentage: 30.1% -> 21.6% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using the optimal-transport fairness test · Arrhythmia, equalized odds | rejection percentage: 75.1% -> 13.7% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using the optimal-transport fairness test · COMPAS, equalized odds | rejection percentage: 100% -> 21.7% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using the optimal-transport fairness test · Drug, equalized odds | rejection percentage: 30.5% -> 17.2% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using Welch's test · Arrhythmia, equal opportunity | rejection percentage: 76.1% -> 14.0% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using Welch's test · COMPAS, equal opportunity | rejection percentage: 100% -> 16.1% | Helped |
| Use Donini et al. (2018) instead of Naive SVM | Classifier compared using Welch's test · Drug, equal opportunity | rejection percentage: 35.5% -> 23.0% | Helped |
Closest papers by meaning
- Optimal Transport of Classifiers to Fairness (NeurIPS 2022 · similarity 0.90)
- Individual Fairness In Strategic Classification (NeurIPS 2025 · similarity 0.87)
- Post-hoc bias scoring is optimal for fair classification (ICLR 2024 · similarity 0.87)
- FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods (ICLR 2024 · similarity 0.86)
- Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness (NeurIPS 2025 · similarity 0.86)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.86)
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