Improving Fairness in AI Models on Electronic Health Records: The Case
  for Federated Learning Methods
Raphael Poulain Mirza Farhan Bin Tarek Rahmatollah Beheshti
Developing AI tools that preserve fairness is of critical importance,
specifically in high-stakes applications such as those in healthcare. However,
health AI models' overall prediction performance is often prioritized over the
possible biases such models could have. In this study, we show one possible
approach to mitigate bias concerns by having healthcare institutions
collaborate through a federated learning paradigm (FL; which is a popular
choice in healthcare settings). While FL methods with an emphasis on fairness
have been previously proposed, their underlying model and local implementation
techniques, as well as their possible applications to the healthcare domain
remain widely underinvestigated. Therefore, we propose a comprehensive FL
approach with adversarial debiasing and a fair aggregation method, suitable to
various fairness metrics, in the healthcare domain where electronic health
records are used. Not only our approach explicitly mitigates bias as part of
the optimization process, but an FL-based paradigm would also implicitly help
with addressing data imbalance and increasing the data size, offering a
practical solution for healthcare applications. We empirically demonstrate our
method's superior performance on multiple experiments simulating large-scale
real-world scenarios and compare it to several baselines. Our method has
achieved promising fairness performance with the lowest impact on overall
discrimination performance (accuracy).