Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
This addresses fairness concerns in high-stakes healthcare applications, offering an incremental improvement with an interactive interface for domain expert engagement.
The authors tackled the problem of model fairness in healthcare by proposing FAIM, an interpretable framework that reduces sex and race biases in hospital admission predictions using MIMIC-IV-ED and SGH-ED datasets, significantly mitigating biases while maintaining performance.
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.