Research map / Learning theory and algorithms / Fairness
Residual Unfairness in Fair Machine Learning from Prejudiced Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply Equal Opportunity adjustment using SQF training-population rates rather than target-population rates | Logistic regression for SQF targeting · NYC target | FNR: 0.11 -> 0.20 | Hurt |
| Apply Equal Opportunity adjustment using SQF training-population rates rather than target-population rates | Logistic regression for SQF targeting · NYC target | FPR: 0.13 to 0.22 | Mixed |
| Apply Equalized Odds adjustment using SQF training-population rates rather than target-population rates | Logistic regression for SQF targeting · NYC target | FNR: 0.15 -> 0.22 | Hurt |
| Use Equalized Odds adjustment instead of Equal Opportunity adjustment | Logistic regression for SQF targeting · NYC target | FNR | Mixed |
| Reweight training observations to account for target-population censoring | Fairness metric estimation and adjustment · Synthetic loan application | true positive rate | Helped |
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