  The escalating integration of machine learning in high-stakes fields such as
healthcare raises substantial concerns about model fairness. We propose an
interpretable framework - Fairness-Aware Interpretable Modeling (FAIM), to
improve model fairness without compromising performance, featuring an
interactive interface to identify a "fairer" model from a set of
high-performing models and promoting the integration of data-driven evidence
and clinical expertise to enhance contextualized fairness. We demonstrated
FAIM's value in reducing sex and race biases by predicting hospital admission
with two real-world databases, MIMIC-IV-ED and SGH-ED. We show that for both
datasets, FAIM models not only exhibited satisfactory discriminatory
performance but also significantly mitigated biases as measured by
well-established fairness metrics, outperforming commonly used bias-mitigation
methods. Our approach demonstrates the feasibility of improving fairness
without sacrificing performance and provides an a modeling mode that invites
domain experts to engage, fostering a multidisciplinary effort toward tailored
AI fairness.
