arXiv cs.AI· Madeleine Pollack, Thomas Daillak, Mehmet Toner, Korkut Uygun, Heidi Yeh, Swati Gupta·· 2 小时前AI 评分47
AI 何时应决策、交由人类还是偏离惯例？死者捐献肾脏分配的负责任自动化框架 Arbitrator
When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation
AI 导读
一项 arXiv 研究提出 Arbitrator 框架，判断基于历史决策训练的 AI 何时应给出建议、交由人类专家或偏离惯例，并以死者捐献肾脏分配为案例。该框架基于 OPTN 2016-2019 与 2023-2025 年 match-run 数据，用可解释模型在 93.5% 的分配决策上直接给出结果，其余 6.5% 交由人类或更复杂模型。
正文 · 原文
Abstract:We ask when an AI trained on historical decisions should be allowed to recommend decisions, defer to human experts, or in other cases, flag concerning patterns in data and deviate from past practice. This can be challenging because past decisions record what was done rather than what should have been done, and where data is sparse, a model's predictions may be too uncertain to act on. We provide a framework for addressing this problem using a case study in deceased-donor kidney allocation. Nearly 30% of kidneys recovered from deceased donors in the United States are never transplanted, largely because they are hard to place through the standard waitlist sequence. One can use AI to flag kidneys at risk of delayed or failed placement for out-of-sequence (OOS) allocation, but this comes at the cost of bypassing candidates higher on the waitlist. Thus, OOS allocation should only be used when the organ is demonstrably at high risk of discard on the standard sequence. Using Organ Procurement and Transplantation Network match-run data from 2016-2019 and 2023-2025 (excluding COVID-19-disrupted years), we build the Arbitrator framework, which combines various interpretable models and acts only when the uncertainty in their predictions cannot change the preferred action. Our Arbitrator resolves 93.5% of organ placement decisions using explicit medical criteria learned from data, and defers the remaining 6.5% to a human or a more complex model. We show that the Arbitrator correctly identifies 70.1% of historically discarded low-KDPI kidneys as OOS candidates, projecting that up to 43.9% of historically discarded kidneys could be flagged for expedited placement and potentially transplanted. We then exploit the interpretability of the Arbitrator to create an agentic audit comparing the learned rules to published clinical literature in order to flag decision-making patterns that are contentious.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39045 [cs.AI] |
| (or arXiv:2610.39045v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39045 arXiv-issued DOI via DataCite |
Submission history
From: Madeleine Pollack [view email]
[v1]
Wed, 28 Oct 2026 23:17:58 UTC (2,917 KB)
来源：arXiv cs.AI · arxiv.org