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Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
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  • Resmi Ramachandranpillai ,
  • Resmi Ramachandranpillai,
  • Fahim Sikder,
  • David Bergström,
  • Fredrik Heintz
Resmi Ramachandranpillai

Corresponding Author:[email protected]

Author Profile
Resmi Ramachandranpillai
Department of Computer and Information Science (IDA), Department of Computer and Information Science (IDA), Linköping University
Fahim Sikder
Department of Computer and Information Science (IDA), Linköping University
David Bergström
Department of Computer and Information Science (IDA), Linköping University
Fredrik Heintz
Linköping University


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 subgroups. To address these concerns, we present Bias-transforming Gen-erative 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 (DGP) 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 subgroup representations (ii), we incentivize the generator to preserve subgroup densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using the Medical Information Mart for Intensive Care (MIMIC-III) database. Our results demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly improving fairness and minimizing bias amplification. Furthermore, we 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.