发表机构
Institute of Biomedical Ethics and History of Medicine, University of Zürich; SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA); ETH Zürich（生物医学伦理与医学史研究所，苏黎世大学; SUPSI人工智能研究所; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能治理中系统可信度判断问题，提出轻量级方法，含形式框架与治理程序两部分，用决策树建模，能产生可信度平台等，通过合成轨迹说明方法可支持合规文档编制和生命周期监测。
AI 中文摘要
人工智能治理日益需要判断人工智能系统随时间推移是否仍具有足够的可信度，观察到的变化是否可容忍，以及如何以透明且可质疑的方式记录此类判断。然而，现有的人工智能可信度工作要么过于高层次，无法支持生命周期监测和重新评估，要么过于狭隘地由指标驱动，无法满足治理需求。因此，我们提出了一种用于人工智能治理中可审计可信度水平的轻量级方法。该方法有两个组成部分：一个用于表示和学习可信度水平的形式框架，以及一个用于随时间记录、监测和重新评估它们的轻量级人工智能生命周期治理程序。形式框架通过可测量维度的上下文敏感协议对与治理相关的可信度进行建模，并将可信度水平学习为关于可信度概况的可解释规则。使用决策树作为可解释的概念验证模型类，该方法产生明确的可信度平台、可读的水平转换以及两种简单的生命周期诊断：边界余量和概况漂移。治理程序将这些形式对象嵌入到一个面向合规的工作流程中，用于设计时标记、部署后监测、重新评估和报告。它还为协议设计、验证、监测和重新评估分配了人员职责和控制门。我们通过涉及退化、冲击（shocks）、更新、异构监测节奏和系统比较的合成人工智能生命周期轨迹来说明该方法。我们的方法并不取代法律或其他专家判断：它通过为随时间记录和跟踪与人工智能治理相关的变化提供证据基础，来支持合规文档编制和生命周期监测。
英文摘要
AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way. Existing approaches remain either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect multidimensional trustworthiness evidence with governance decisions. We propose a lightweight methodology centered on \emph{trustworthiness level functions}: auditable rules that map measured trustworthiness profiles to governance-relevant levels. The methodology separates the underlying trustworthiness evidence from the governance rule used to interpret it and treats that rule as a lifecycle governance object. The rule may remain expert-defined or, when available evidence warrants empirical learning, be approximated by an interpretable candidate model. An AI lifecycle governance procedure embeds this choice in explicit decision gates for determining whether learning should be attempted and whether a learned candidate should become operative. The resulting rule supports lifecycle monitoring through level transitions, boundary margins, and profile drift, with explicit human responsibilities for validation, approval, and reassessment. We illustrate the methodology on synthetic AI lifecycle scenarios involving degradation, shocks, updates, heterogeneous monitoring cadences, and system comparison. Our methodology does not replace expert or legal judgment, but makes the governance interpretation of trustworthiness evidence more explicit, auditable, and contestable over time.
CommentsVersion v2. Clarified lifecycle governance procedure and expanded synthetic experiments. 26 pages; 8 figures
