Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs
Margarida Carvalho, Alison Caulfield, Yi Lin, Adrian Vetta

TL;DR
This paper demonstrates that in kidney exchange programs, assigning penalties to non-directed donors significantly improves fairness, number of transplants, and reduces waiting times, outperforming reward-based strategies.
Contribution
It introduces a dynamic learning algorithm that emphasizes penalties over rewards, revealing the critical role of penalties in enhancing kidney exchange outcomes.
Findings
Penalties to non-directed donors greatly improve fairness and efficiency.
Dynamic algorithms outperform static approaches in kidney exchange.
Reward-based strategies are less effective than penalty-based ones.
Abstract
A kidney exchange program, also called a kidney paired donation program, can be viewed as a repeated, dynamic trading and allocation mechanism. This suggests that a dynamic algorithm for transplant exchange selection may have superior performance in comparison to the repeated use of a static algorithm. We confirm this hypothesis using a full scale simulation of the Canadian Kidney Paired Donation Program: learning algorithms, that attempt to learn optimal patient-donor weights in advance via dynamic simulations, do lead to improved outcomes. Specifically, our learning algorithms, designed with the objective of fairness (that is, equity in terms of transplant accessibility across cPRA groups), also lead to an increased number of transplants and shorter average waiting times. Indeed, our highest performing learning algorithm improves egalitarian fairness by 10% whilst also increasing the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOrgan Donation and Transplantation · Renal Transplantation Outcomes and Treatments · Global Maternal and Child Health
