  Synthetic data generation offers a promising solution to enhance the
usefulness of Electronic Healthcare Records (EHR) by generating realistic
de-identified data. However, the existing literature primarily focuses on the
quality of synthetic health data, neglecting the crucial aspect of fairness in
downstream predictions. Consequently, models trained on synthetic EHR have
faced criticism for producing biased outcomes in target tasks. These biases can
arise from either spurious correlations between features or the failure of
models to accurately represent sub-groups. To address these concerns, we
present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based
synthetic data generator specifically designed for the healthcare domain. In
order to tackle spurious correlations (i), we propose an
information-constrained Data Generation Process that enables the generator to
learn a fair deterministic transformation based on a well-defined notion of
algorithmic fairness. To overcome the challenge of capturing exact sub-group
representations (ii), we incentivize the generator to preserve sub-group
densities through score-based weighted sampling. This approach compels the
generator to learn from underrepresented regions of the data manifold. We
conduct extensive experiments using the MIMIC-III database. Our results
demonstrate that Bt-GAN achieves SOTA accuracy while significantly improving
fairness and minimizing bias amplification. We also perform an in-depth
explainability analysis to provide additional evidence supporting the validity
of our study. In conclusion, our research introduces a novel and professional
approach to addressing the limitations of synthetic data generation in the
healthcare domain. By incorporating fairness considerations and leveraging
advanced techniques such as GANs, we pave the way for more reliable and
unbiased predictions in healthcare applications.
