Research map / Learning theory and algorithms / Fairness
Unlocking Fairness: a Trade-off Revisited
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| enforce fairness (parity) during training, evaluated on unbiased labels | fair classifiers · simulated COMPAS, beta=0.25 (assumption holds) | F1 vs unbiased labels | Helped |
| enforce fairness (parity) during training, evaluated on biased labels | fair classifiers · simulated COMPAS, beta=0.25 | F1 vs biased labels | Hurt |
| add semi-supervised fairness loss on unlabeled data | supervised logistic regression · selection bias experiment, label bias 0.2 | fairness and F1 | Helped |
| enforce fairness when COMPAS violates the equal-groups assumption, low label bias | fair classifiers · COMPAS (assumption violated) | F1 vs unbiased labels | Mixed |
Closest papers by meaning
- The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning (NeurIPS 2024 · similarity 0.90)
- Achievable Fairness on Your Data With Utility Guarantees (NeurIPS 2024 · similarity 0.89)
- Mitigating Label Bias in Machine Learning: Fairness through Confident Learning (AAAI 2024 · similarity 0.89)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.89)
- Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework (ICLR 2024 · similarity 0.89)
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.89)
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