Research map / Learning theory and algorithms / Fairness
On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| impose exact equal-opportunity without differential privacy | Predictors on certain probabilistic data sources · Certain probabilistic data sources | accuracy relative to the best constant predictor | Hurt |
| allow probabilistic labels and use the Bayes classifier | Bayes classifier · Probabilistic data sources constructed in Proposition 7 | opportunity difference | Mixed |
| allow probabilistic labels and use the soft target predictor Q | Soft target predictor · Probabilistic data sources constructed in Proposition 7 | opportunity difference | Hurt |
| require positive probability of Q>1/2 and Q<1/2 in each protected group | Predictors on probabilistic data sources · Sources satisfying Theorem 9 | non-trivial accuracy and equal-opportunity compatibility | Helped |
Closest papers by meaning
- Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing (ICML 2020 · similarity 0.89)
- Achievable Fairness on Your Data With Utility Guarantees (NeurIPS 2024 · similarity 0.86)
- How Far Can Fairness Constraints Help Recover From Biased Data? (ICML 2024 · similarity 0.85)
- Learning with Statistical Equality Constraints (NeurIPS 2025 · similarity 0.85)
- Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective (ICML 2025 · similarity 0.85)
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.84)
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