  Liver transplant is an essential therapy performed for severe liver diseases.
The fact of scarce liver resources makes the organ assigning crucial. Model for
End-stage Liver Disease (MELD) score is a widely adopted criterion when making
organ distribution decisions. However, it ignores post-transplant outcomes and
organ/donor features. These limitations motivate the emergence of machine
learning (ML) models. Unfortunately, ML models could be unfair and trigger bias
against certain groups of people. To tackle this problem, this work proposes a
fair machine learning framework targeting graft failure prediction in liver
transplant. Specifically, knowledge distillation is employed to handle dense
and sparse features by combining the advantages of tree models and neural
networks. A two-step debiasing method is tailored for this framework to enhance
fairness. Experiments are conducted to analyze unfairness issues in existing
models and demonstrate the superiority of our method in both prediction and
fairness performance.
