Research map / Learning theory and algorithms / Fairness
Pairwise Fairness for Ranking and Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Impose cross-group equal opportunity constraints | Linear ranking on simulated data with 2 groups · Sim. ranking (2 groups) | pairwise fairness violation: 0.28 -> 0.01 | Helped |
| Impose cross-group equal opportunity constraints | Linear ranking on simulated data with 2 groups · Sim. ranking (2 groups) | AUC: 0.92 -> 0.86 | Hurt |
| Impose marginal equal opportunity constraints | Business matching model · Business matching | pairwise fairness violation: 0.06 -> 0.00 | Helped |
| Impose marginal equal opportunity constraints | Business matching model · Business matching | AUC: 0.70 -> 0.68 | Hurt |
| Impose cross-group equal opportunity constraints | Convolutional ranking model · Wiki Talk Pages | pairwise fairness violation: 0.10 -> 0.01 | Helped |
| Impose cross-group equal opportunity constraints | Convolutional ranking model · Wiki Talk Pages | AUC: 0.97 -> 0.96 | Hurt |
| Impose cross-group equal opportunity constraints | Linear ranking model · W3C Experts | pairwise fairness violation: 0.96 -> 0.10 | Helped |
| Impose cross-group equal opportunity constraints | Linear ranking model · W3C Experts | AUC: 0.53 -> 0.54 | Helped |
| Impose continuous-attribute equal opportunity constraints | Linear ranking model · Communities and Crime ranking | pairwise fairness violation: 0.18 -> 0.04 | Helped |
| Impose continuous-attribute equal opportunity constraints | Linear ranking model · Communities and Crime ranking | AUC: 0.93 -> 0.81 | Hurt |
| Impose cross-group equal opportunity constraints | Regression model · Law School regression | pairwise fairness violation: 0.30 -> 0.02 | Helped |
| Impose cross-group equal opportunity constraints | Regression model · Law School regression | MSE: 0.142 -> 0.143 | Hurt |
Closest papers by meaning
- Fair Ranking with Noisy Protected Attributes (NeurIPS 2022 · similarity 0.91)
- Stability and Multigroup Fairness in Ranking with Uncertain Predictions (ICML 2024 · similarity 0.90)
- Individually Fair Rankings (ICLR 2021 · similarity 0.88)
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.87)
- A Sequentially Fair Mechanism for Multiple Sensitive Attributes (AAAI 2024 · similarity 0.87)
- The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning (NeurIPS 2024 · similarity 0.86)
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