  Artificial intelligence supports healthcare professionals with predictive
modeling, greatly transforming clinical decision-making. This study addresses
the crucial need for fairness and explainability in AI applications within
healthcare to ensure equitable outcomes across diverse patient demographics. By
focusing on the predictive modeling of sepsis-related mortality, we propose a
method that learns a performance-optimized predictive model and then employs
the transfer learning process to produce a model with better fairness. Our
method also introduces a novel permutation-based feature importance algorithm
aiming at elucidating the contribution of each feature in enhancing fairness on
predictions. Unlike existing explainability methods concentrating on explaining
feature contribution to predictive performance, our proposed method uniquely
bridges the gap in understanding how each feature contributes to fairness. This
advancement is pivotal, given sepsis's significant mortality rate and its role
in one-third of hospital deaths. Our method not only aids in identifying and
mitigating biases within the predictive model but also fosters trust among
healthcare stakeholders by improving the transparency and fairness of model
predictions, thereby contributing to more equitable and trustworthy healthcare
delivery.
