Research map / Learning theory and algorithms / Fairness
Characterizing Fairness Over the Set of Good Models Under Selective Labels
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Search the set of good models for lower statistical parity disparity | FaiRS versus COMPAS · COMPAS test set, SP | predictive disparity: 0.194 -> -0.060 | Helped |
| Search the set of good models for lower balance-for-positive-class disparity | FaiRS versus COMPAS · COMPAS test set, BFPC | predictive disparity: 0.156 -> 0.049 | Helped |
| Search the set of good models for lower balance-for-negative-class disparity | FaiRS versus COMPAS · COMPAS test set, BFNC | predictive disparity: 0.174 -> 0.044 | Helped |
| Use extrapolation-based selective-label handling instead of KGB | FaiRS on CommBank lending data · All applicants in held-out test set | AUC | Helped |
| Use FaiRS with RIE or IE to optimize disparity under the benchmark performance constraint | FaiRS on CommBank lending data · All applicants in held-out test set | disparity | Helped |
| Use extrapolation rather than iterative reweighing | FaiRS versus TFCS on CommBank lending data · All applicants in held-out test set | disparity | Helped |
| Evaluate only funded applicants rather than the full target population | TFCS and KGB models · CommBank lending data | AUC | Mixed |
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