Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare

• URL: http://arxiv.org/abs/2403.05235v1
• Date: Fri, 8 Mar 2024 11:51:00 GMT
• Title: Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
• Authors: Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
• Abstract summary: We propose Fairness-Aware Interpretable Modeling (FAIM) to improve model fairness without compromising performance. FAIM features an interactive interface to identify a "fairer" model from a set of high-performing models. We show FAIM models not only exhibited satisfactory discriminatory performance but also significantly mitigated biases as measured by well-established fairness metrics.
• Score: 6.608905791768002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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