Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives

TL;DR

This paper emphasizes that machine learning approaches to heart transplant allocation must incorporate incentive considerations to address strategic behaviors and improve fairness and efficiency.

Contribution

It highlights the importance of incentive-aware policy design in organ allocation and proposes integrating mechanism design and related fields into ML research.

Findings

Identifies incentive misalignments in US heart transplant allocation.

Shows adverse effects of current incentive structures.

Calls for incentive-aware ML policy development.

Abstract

The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators. Focusing on US adult heart transplant allocation, we identify critical incentive misalignments across the decision-making pipeline, and present data showing that they are having adverse consequences today. Our main position is that the next generation of allocation policies should be incentive aware. We outline a research agenda for the machine learning community, calling for the integration of mechanism design, strategic classification, causal inference, and social choice to ensure robustness, efficiency, fairness, and trust in the face of strategic behavior from the various constituent groups.