Research map / Learning theory and algorithms / Fairness
From Parity to Preference-based Notions of Fairness in Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace parity-based fairness with preferred treatment | Classifier on synthetic data · Synthetic data | accuracy: 0.57 -> 0.87 | Helped |
| Replace parity-based fairness with preferred impact | Classifier on synthetic data · Synthetic data | accuracy: 0.57 -> 0.76 | Helped |
| Replace parity-based fairness with preferred treatment | RBF SVM on non-linearly separable synthetic data · Non-linearly separable synthetic data | accuracy: 0.61 -> 0.93 | Helped |
| Replace parity-based fairness with preferred impact | RBF SVM on non-linearly separable synthetic data · Non-linearly separable synthetic data | accuracy: 0.61 -> 0.84 | Helped |
| Require both preferred treatment and preferred impact instead of preferred impact alone | Classifier on synthetic data · Synthetic data | accuracy | No clear effect |
| Require both preferred treatment and preferred impact | Classifier across real-world datasets · Adult, COMPAS, and NYPD SQF | accuracy | Mixed |
| Use preferred treatment instead of parity-based fairness | Classifier across real-world datasets · Adult, COMPAS, and NYPD SQF | accuracy | Helped |
| Use preferred impact instead of parity-based fairness | Classifier across real-world datasets · Adult, COMPAS, and NYPD SQF | accuracy | Helped |
Closest papers by meaning
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.90)
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- Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification (AAAI 2024 · similarity 0.88)
- Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors (NeurIPS 2024 · similarity 0.87)
- Identifying Reasons for Bias: An Argumentation-Based Approach (AAAI 2024 · similarity 0.87)
- Fairness under Competition (NeurIPS 2025 · similarity 0.87)
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