  Liver transplantation often faces fairness challenges across subgroups
defined by sensitive attributes like age group, gender, and race/ethnicity.
Machine learning models for outcome prediction can introduce additional biases.
To address these, we introduce Fairness through the Equitable Rate of
Improvement in Multitask Learning (FERI) algorithm for fair predictions of
graft failure risk in liver transplant patients. FERI constrains subgroup loss
by balancing learning rates and preventing subgroup dominance in the training
process. Our experiments show that FERI maintains high predictive accuracy with
AUROC and AUPRC comparable to baseline models. More importantly, FERI
demonstrates an ability to improve fairness without sacrificing accuracy.
Specifically, for gender, FERI reduces the demographic parity disparity by
71.74%, and for the age group, it decreases the equalized odds disparity by
40.46%. Therefore, the FERI algorithm advances fairness-aware predictive
modeling in healthcare and provides an invaluable tool for equitable healthcare
systems.

Machine Learning;Artificial Intelligence