Research map / Learning theory and algorithms / Fairness
Popularizing Fairness: Group Fairness and Individual Welfare
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Postprocess a fair classifier with DOS or k-QLS to target popularity while retaining fairness | Fair classifiers · Four datasets | balanced accuracy and ROC-AUC | No clear effect |
| Use existing group-fair learning methods instead of the conventional classifier, without popularity postprocessing | Reductions and other group-fair methods · Several standard datasets | popularity | Hurt |
| Increase the target popularity γ | k-QLS randomized classifiers · Four datasets | Expected FPR | Hurt |
| Use deterministic rather than randomized fair classifiers | Fair classifiers · Four datasets | popularity | Helped |
Closest papers by meaning
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.88)
- FRAPPÉ: A Group Fairness Framework for Post-Processing Everything (ICML 2024 · similarity 0.88)
- Metric-Agnostic Continual Learning for Sustainable Group Fairness (AAAI 2025 · similarity 0.87)
- FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods (ICLR 2024 · similarity 0.87)
- Enhancing Group Fairness in Online Settings Using Oblique Decision Forests (ICLR 2024 · similarity 0.87)
- Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness (NeurIPS 2025 · similarity 0.86)
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