# Fairness-aware kidney exchange and kidney paired donation

arXiv: https://arxiv.org/abs/2503.06431 · HTML: https://mlsanity.com/paper/2503.06431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [Adapting a Kidney Exchange Algorithm to Align with Human Values](https://mlsanity.com/paper/2005.09755/) (2020)
- [Fair Policy Targeting](https://mlsanity.com/paper/2005.12395/) (2020)
- [Fair-by-design matching](https://mlsanity.com/paper/1802.02562/) (2018)
- [On Fairness and Stability in Two-Sided Matchings](https://mlsanity.com/paper/2111.10885/) (2021)
- [Scalable Robust Kidney Exchange](https://mlsanity.com/paper/1811.03532/) (2018)
- [Fast and Fair Simultaneous Confidence Bands for Functional Parameters](https://mlsanity.com/paper/1910.00131/) (2019)
- [Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs](https://mlsanity.com/paper/2207.00241/) (2022)
- [Game theoretical analysis of Kidney Exchange Programs](https://mlsanity.com/paper/1911.09207/) (2019)

## Abstract (arXiv)

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.