Post-processing fairness interventions (ROC, EqOdds) provide the most stable fairness-utility trade-offs when training on differentially private synthetic tabular data, partially recovering DP-induced fairness degradation.
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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Post-processing fairness interventions (ROC, EqOdds) provide the most stable fairness-utility trade-offs when training on differentially private synthetic tabular data, partially recovering DP-induced fairness degradation.