Research map / Learning theory and algorithms / Fairness
Paradoxes in Fair Machine Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add resource monotonicity on top of equalized odds | optimal equalized-odds allocation · FICO credit scores | efficiency | No clear effect |
| require population monotonicity in addition to equalized odds | equalized-odds allocation · FICO credit scores | efficiency fraction of unfair optimum | Hurt |
| require consistency in addition to equalized odds | equalized-odds allocation · FICO credit scores | efficiency | Hurt |
Closest papers by meaning
- Recycling Privileged Learning and Distribution Matching for Fairness (NeurIPS 2017 · similarity 0.89)
- Demystifying Algorithmic Fairness in an Uncertain World (AAAI 2024 · similarity 0.89)
- Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (ICML 2018 · similarity 0.89)
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.88)
- The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning (NeurIPS 2024 · similarity 0.88)
- Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning (NeurIPS 2025 · similarity 0.87)
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