提出新公平准则，让肾移植匹配不因种族性别而偏倚
Fairness-aware kidney exchange and kidney paired donation
- 基于敏感度水平，使匹配结果与受保护特征条件独立
- 在真实数据和模拟中均提升公平性，且不影响匹配效率
- 适合关注医疗公平、算法伦理的研究者与政策制定者
肾配对捐赠（KPD）通过匹配不相容的供体-患者对，解决肾移植中的相容性难题。为应对移植机会不均问题，现有公平性标准包括群体公平与个体公平，但均未考虑受保护特征（如种族、性别）。受机器学习校准原则启发，本文提出新公平准则：匹配结果应给定敏感度水平后，与受保护特征条件独立。将该准则作为约束集成至KPD优化框架，并采用线性化策略与列生成方法实现高效求解。理论层面，利用随机图模型分析公平性代价；实证层面，通过仿真与真实数据对比，验证该准则优于传统公平标准。
原文摘要 · Abstract (English)
The kidney paired donation (KPD) program provides an innovative solution to overcome incompatibility challenges in kidney transplants by matching incompatible donor-patient pairs and facilitating kidney exchanges. To address unequal access to transplant opportunities, there are two widely used fairness criteria: group fairness and individual fairness. However, these criteria do not consider protected patient features, which refer to characteristics legally or ethically recognized as needing protection from discrimination, such as race and gender. Motivated by the calibration principle in machine learning, we introduce a new fairness criterion: the matching outcome should be conditionally independent of the protected feature, given the sensitization level. We integrate this fairness criterion as a constraint within the KPD optimization framework and propose a computationally efficient solution using linearization strategies and column-generation methods. Theoretically, we analyze the associated price of fairness using random graph models. Empirically, we compare our fairness criterion with group fairness and individual fairness through both simulations and a real-data example.
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