Metraipolis › archive › dc_ecb6b2b248aa

When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation — arXiv:2610.39045

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39045
License (as archived): CC0-1.0
Archived: 2026-10-31T06:09:09.939Z
SHA-256 of the archived text: 5d4d25dde6291599f290ce6b0e9ee9e776a381198394190bd1942872177e53c8
Stable URL of this page: https://metraipolis.com/documento/dc_ecb6b2b248aa

How to cite

When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation — arXiv:2610.39045 — arXiv (en), https://arxiv.org/abs/2610.39045 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ecb6b2b248aa (Metraipolis archive dc_ecb6b2b248aa, sha256 5d4d25dde6291599f290ce6b0e9ee9e776a381198394190bd1942872177e53c8)

BibTeX

@misc{dcecb6b2b248aa,
  title = {When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation — arXiv:2610.39045},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39045},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ecb6b2b248aa; sha256 5d4d25dde6291599f290ce6b0e9ee9e776a381198394190bd1942872177e53c8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39045. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.39045 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T06:07:08.503Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).