<p>Liver transplantation is a critical treatment for end-stage liver diseases, but ensuring fair and effective allocation of scarce donor organs remains a major challenge in healthcare. Existing allocation policies often struggle to balance clinical outcomes with fairness across patients and demographic groups. To address this, we propose FairAlloc, a learning-based framework that formulates organ allocation as a ranking problem and jointly optimizes for post-transplant outcomes and fairness. Specifically, FairAlloc integrates two fairness objectives—group fairness across sensitive attributes such as gender and race and individual fairness between similar patients—into the ranking process. We evaluate FairAlloc using real-world data from the Organ Procurement and Transplantation Network (OPTN) and compare its performance to six baseline methods. Results show that FairAlloc improves group and individual fairness metrics by up to 37.9% and 39.9%, respectively, while maintaining competitive performance on key post-transplant outcomes such as graft failure and survival rates. This work contributes a novel, fairness-aware decision-making framework to the healthcare informatics community, with the potential to improve equity and efficiency in organ allocation systems.</p>