arXiv:2607. 15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings.
By Trisevgeni Papakonstantinou, Cansu Canca, Farah Nanji, Waheedullah Pardess, Jen Weedon, Jasmijn Remmers, Eliza Krigman, Matthew Ball, Yalda Daryani, Kiran Iqbal, Francielle Vargas, Mar\'ia Llorente S\'anchez, Joe Humphreys, Fendi Tsim, Kelly Fitzpatrick, Jeff Dunn, Catherine Feldman
The paper discusses how autonomous AI systems are moving from advisory to agentic roles in medication prescribing, citing recent U.S. legislation and a Utah pilot program. It argues that three architectural features—calibrated per‑prediction confidence, clear differentiation between epistemic and aleatoric uncertainty, and inferential transparency—are essential for safe autonomous prescribing. A survey of 136 U.S. clinicians shows they require a confidence‑based escalation mechanism, prefer different handling of uncertainty types, and will only accept liability when transparency allows informed decision‑making.
By Eileanor LaRocco, Sarah Tan, Adarsh Subbaswamy, Anne Andrews, Andrew Taylor, Cree Gaskin, Chirag Agarwal
Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a market that rewards trustworthiness.
The paper introduces a source‑grounded integrity gate for AI‑assisted personal health records, ensuring that data generated by large language models remains provisional until a deterministic monitor verifies it against the source document. The monitor only accepts candidates that contain a unique supporting quotation, appear within the same laboratory row, and preserve provenance, preventing the model from approving its own output. In Medical DataCloud, the system passed all 22 conformance and mutation tests and, in a replay of nine historical lab reports, admitted 72 of 97 numeric candidates while retaining 25 for human review.
By Nora Girda, Adrian Groza
The paper introduces Runtime Assurance Contracts (RAC) as a formal policy framework for high‑risk AI agents, addressing the "assurance‑transition gap" by binding autonomy boundaries, component eligibility, evidence state, transition policy, human‑review capacity, and non‑compensatory gates. RAC allows soft metrics to influence routing while mandating retries, switches, escalations, deferrals, or stops when mandatory gates fail or are unknown, ensuring aggregate performance cannot alone authorize action. The authors define the contract, evidence record, permission rule, and five invariants, and evaluate RAC through deterministic failure‑injection studies, hand‑authored traces, and a prospective synthetic holdout, comparing it to score‑only and restricted protocol baselines.
By Serhii Zabolotnii
arXiv:2407. 11823v4 Announce Type: replace Abstract: The United States Food and Drug Administration's (FDA's) 510(k) pathway allows manufacturers to gain medical device approval by demonstrating substantial equivalence to a legally marketed device.
By Mohammad Zhalechian, Soroush Saghafian, Omar Robles