In response to the pressing challenge of kidney allocation, characterized by
growing demands for organs, this research sets out to develop a data-driven
solution to this problem, which also incorporates stakeholder values. The
primary objective of this study is to create a method for learning both
individual and group-level preferences pertaining to kidney allocations.
Drawing upon data from the 'Pairwise Kidney Patient Online Survey.' Leveraging
two distinct datasets and evaluating across three levels - Individual, Group
and Stability - we employ machine learning classifiers assessed through several
metrics. The Individual level model predicts individual participant
preferences, the Group level model aggregates preferences across participants,
and the Stability level model, an extension of the Group level, evaluates the
stability of these preferences over time. By incorporating stakeholder
preferences into the kidney allocation process, we aspire to advance the
ethical dimensions of organ transplantation, contributing to more transparent
and equitable practices while promoting the integration of moral values into
algorithmic decision-making.

为了应对肾脏分配面临的紧迫挑战，本研究旨在开发一个数据驱动的解决方案，并融入利益相关者的价值观。通过使用 “两两对比肾脏患者在线调查” 数据，经过个人、团体和稳定性三个层面的评估，我们运用机器学习分类器来学习个人和团体层面对肾脏分配的偏好。通过将利益相关者的偏好融入肾脏分配过程，我们致力于推进器官移植的伦理维度，促进道德价值观融入算法决策，以实现更加透明和公平的实践。