Fairness Without Discrimination: Individually Fair Outcomes in the Kidney Exchange Problem

The kidney exchange problem (KEP) determines a set of planned kidney transplants, an exchange plan, for a pool of nondirected donors and biologically incompatible patient–donor pairs (PDPs) that maximizes weighted transplant quantity. Exchange pool members with fewer compatible donors experience bias, being excluded from exchange plans more often. To reduce bias, KEP optimization considers fairness at the group level, which obscures individual differences, or at the individual level, which is resource intensive. To bridge the gap between these approaches, we develop a model, individually fair outcomes for the KEP (IFO-KEP), which uses a novel prioritization schema, to individually prioritize PDPs, based on their probability of being included in fairness-agnostic optimization, within a hierarchical optimization approach balancing planned transplant quantity and fairness. Analysis compares IFO-KEP to existing individual and group fairness KEP approaches in myopic contexts, using metrics related to exchange performance and measures of parity, and dynamic contexts, evaluating exchange pool dynamics over time. In myopic contexts, IFO-KEP generates KEP solutions that achieve a high degree of parity without decreasing expected utility. In dynamic testing, IFO-KEP achieves fairness without causing negative impacts on long-term exchange performance. IFO-KEP is a novel method to achieve individually fair KEP exchange plans and reduce the bias against PDPs with low access to a compatible donor, without high-resource requirements. Global growth in KEP exchange pools elevates the importance of our contributions.

Funding: This work was supported by the National Science Foundation [Awards DGE-2125362 and OISE-2420109].