Research map / Learning theory and algorithms / Fairness
Non-Discriminatory Machine Learning Through Convex Fairness Criteria
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply weighted sum of logs fairness technique to logistic regression | Logistic regression · COMPAS | p-rule value: 64% -> 90% | Helped |
| apply weighted sum of logs fairness technique to logistic regression | Logistic regression · COMPAS | accuracy: 67% -> 66% | Hurt |
| apply weighted sum of logs fairness technique to logistic regression | Logistic regression · Adult | p-rule value: 34% -> 90% | Helped |
| apply weighted sum of logs fairness technique to logistic regression | Logistic regression · Adult | accuracy: 85% -> 82% | Hurt |
| use binarized weights instead of exponentially scaled weights (ablation) | Weighted sum of logs · COMPAS | p-rule vs accuracy trade-off | No clear effect |
| use regularizer form instead of constrained form (ablation) | Weighted sum of logs · COMPAS and Adult | p-rule vs accuracy trade-off | No clear effect |
| use weighted sum of logs instead of Zafar et al. covariance-constraint methods | Fairness-constrained logistic regression · COMPAS and Adult | p-rule vs accuracy trade-off | No clear effect |
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