Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives
arXiv:2602.04990v3 Announce Type: replace 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.
The accelerating deployment of AI and machine learning in critical, real-world systems like healthcare allocation is forcing a confrontation with human behavioral factors and incentives. The academic community is recognizing real-world complexity beyond pure optimization.
This highlights a crucial challenge for AI deployment in high-stakes environments: technical optimization alone is insufficient without considering the 'game theory' of human interaction. It implies a more nuanced, interdisciplinary approach is required for effective AI integration into complex societal systems.
The focus for AI in resource allocation will shift from purely technical optimization to incorporating game theory, behavioral economics, and understanding the incentives of all stakeholders. This alters the design principles and validation metrics for such AI systems.
- · Machine Learning researchers incorporating game theory
- · Healthcare ethicists
- · Patients benefiting from more robust allocation systems
- · Interdisciplinary AI research programs
- · Purely optimization-focused AI developers
- · Healthcare systems with rigid, unadaptive AI policies
AI models for critical resource allocation will begin to integrate game theory and incentive structures into their design.
Greater ethical oversight and interdisciplinary collaboration will become standard for AI deployed in sensitive domains like healthcare.
This integrated approach could lead to more equitable and efficient resource distribution, potentially influencing public trust in AI decision-making.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG