Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine
  Learning in Healthcare
Mingxuan Liu Yilin Ning Yuhe Ke Yuqing Shang Bibhas Chakraborty Marcus Eng Hock Ong Roger Vaughan Nan Liu
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.