Research map / Learning theory and algorithms / Fairness
Fair Learning with Private Demographic Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply the two-step correction after the reductions-based predictor | Adult Income logistic regression · Adult Income | discrimination violation | Helped |
| apply the two-step correction after the reductions-based predictor | Adult Income logistic regression · Adult Income | accuracy | Hurt |
| increase privacy | step 1 reductions-based predictor · Adult Income | error | Mixed |
Closest papers by meaning
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- Can Private Machine Learning Be Fair? (AAAI 2025 · similarity 0.85)
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- Differentially Private Post-Processing for Fair Regression (ICML 2024 · similarity 0.85)
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