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原文摘要

Synthetic data augmentation has been proposed as a way to artificially supplement clinical datasets to address fairness concerns in downstream classification algorithms. This approach is closely related to upweighting or upsampling methods employed for the problem of class imbalance, which have shown mixed empirical results in practice. Synthetic data augmentation also runs the risk of overfitting to the noise present in subgroups with relatively small sample sizes, raising both generalizability and transportability concerns. We present an empirical evaluation of synthetic data augmentation for fairness in tabular data using simulation studies as well as a real-world application of in-hospital mortality classification for heart failure patients in two distinct data samples. We implement upsampling and upweighting methods as benchmarks for the implicit fairness objective of synthetic data augmentation. Our results suggest that the fairness impacts of data augmentation are sensitive to the design of the downstream algorithm as well as the deployment context and its similarity to the training data. It is crucial that studies making generalizability and transportability claims should provide empirical support for these assertions.