Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
<|reference_start|>Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare: 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.<|reference_end|>
@article{liu2024fairness-aware,
    title={Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine
  Learning in Healthcare},
    author={Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty,
  Marcus Eng Hock Ong, Roger Vaughan, Nan Liu},
    journal={arXiv preprint arXiv:2403.05235},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05235},
    primaryClass={cs.LG cs.AI cs.CY}
}