Post-processing fairness methods (ROC and EqOdds) provide the most stable fairness-utility trade-offs when training classifiers on DP synthetic tabular data, outperforming pre- and in-processing interventions.
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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Post-processing fairness methods (ROC and EqOdds) provide the most stable fairness-utility trade-offs when training classifiers on DP synthetic tabular data, outperforming pre- and in-processing interventions.