# Adapting a Kidney Exchange Algorithm to Align with Human Values

arXiv: https://arxiv.org/abs/2005.09755 · HTML: https://mlsanity.com/paper/2005.09755/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 30 of 36)

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- [Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions](https://mlsanity.com/paper/2503.00940/) (2025)
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- [Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment](https://mlsanity.com/paper/2509.04445/) (2025)
- [The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions](https://mlsanity.com/paper/2608.05583/) (2026)
- [Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values](https://mlsanity.com/paper/2506.00079/) (2025)
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- [Intuitions of Compromise: Utilitarianism vs. Contractualism](https://mlsanity.com/paper/2410.05496/) (2024)
- [Democratizing Reward Design for Personal and Representative Value-Alignment](https://mlsanity.com/paper/2410.22203/) (2024)
- [Evaluating Large Language Models for Fair and Reliable Organ Allocation](https://mlsanity.com/paper/2504.03716/) (2025)
- [The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisions](https://mlsanity.com/paper/2605.12717/) (2026)
- [Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values](https://mlsanity.com/paper/2608.28620/) (2026)
- [Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs](https://mlsanity.com/paper/2309.13421/) (2023)
- [Towards Stable Preferences for Stakeholder-aligned Machine Learning](https://mlsanity.com/paper/2401.15268/) (2024)
- [Why should we ever automate moral decision making?](https://mlsanity.com/paper/2407.07671/) (2024)
- [Revisiting Technical Bias Mitigation Strategies](https://mlsanity.com/paper/2410.17433/) (2024)
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## Abstract (arXiv)

The efficient and fair allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who gets what--and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices, and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply. However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments.