FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare
Keywords: AI for healthcare, AI fairness, fairness in healthcare, interpretable machine learning, trustworthy machine learning
TL;DR: FAIM is a fairness-aware and interpretable modeling framework, designed to build wellperforming yet fair models as alternatives to performance-optimized, fairness-unaware models..
Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.
DOI: https://doi.org/10.1016/j.patter.2024.101059
Submission Number: 128
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