Optimizing for Fairness in Generalized Kidney Exchange: Theory and Computations
This work provides theoretical and computational foundations for fairness in generalized kidney exchange, benefiting patients and policymakers by enabling equitable allocation in more realistic exchange settings.
The authors extend fairness guarantees from maximum-cardinality matching to weighted matching and 2-paths in kidney exchange, and show that any optimization subroutine can be bootstrapped to achieve analogous fairness properties. Computational results on synthetic and real data demonstrate advantages of incorporating fairness.
The seminal work of Roth, Sönmez, & Ünver shows that the Edmonds-Gallai structure theorem for non-bipartite matching can be leveraged to yield a randomized algorithm to match patient-donor pairs in kidney exchange with extraordinarily strong properties. This breakthrough led to randomized polynomial-time algorithms to find a maximum-cardinality matching maximizing individual fairness objectives--measured by the probability that nodes are matched--such as Nash social welfare. But the exchanges allowed in practice go beyond cardinality matching, generalizing to weighted variants and allowing structures such as paths and 3-cycles. We show that strongly polynomial algorithms guaranteeing the same fairness properties can be obtained in weighted settings for matching and 2-paths. While even maximum cardinality coverage with cycles and paths of length at least three is NP-hard, we provide a general result showing that any optimization subroutine (for whichever structure is allowed) can be bootstrapped using a polynomial number of calls to yield a mechanism that has analogous fairness properties to those obtained for matching. We complement these theoretical results with computational results, both on well-studied synthetic data-sets and on samples drawn from real data, that demonstrate the striking advantages of adding fairness considerations to more general kidney-exchange mechanisms.