  Among various aspects of ensuring the responsible design of AI tools for
healthcare applications, addressing fairness concerns has been a key focus
area. Specifically, given the wide spread of electronic health record (EHR)
data and their huge potential to inform a wide range of clinical decision
support tasks, improving fairness in this category of health AI tools is of key
importance. While such a broad problem (that is, mitigating fairness in
EHR-based AI models) has been tackled using various methods, task- and
model-agnostic methods are noticeably rare. In this study, we aimed to target
this gap by presenting a new pipeline that generates synthetic EHR data, which
is not only consistent with (faithful to) the real EHR data but also can reduce
the fairness concerns (defined by the end-user) in the downstream tasks, when
combined with the real data. We demonstrate the effectiveness of our proposed
pipeline across various downstream tasks and two different EHR datasets. Our
proposed pipeline can add a widely applicable and complementary tool to the
existing toolbox of methods to address fairness in health AI applications such
as those modifying the design of a downstream model. The codebase for our
project is available at https://github.com/healthylaife/FairSynth
