这篇论文给出了AI系统生命周期可信度评估的具体方法，用决策树生成可读的规则和阈值，适合做治理合规的工程师看看。
该论文提出一种轻量级方法，用于AI治理中的可审计可信度水平评估。方法包括两个组件：基于上下文敏感协议的形式化框架，通过可解释规则（如决策树）学习可信度水平；以及面向合规的生命周期治理流程，涵盖设计标注、部署监控、重新评估与报告。在合成AI生命周期轨迹上验证了方法，涉及退化、冲击、更新等场景。方法不取代法律判断，而是为AI治理相关变化提供证据基础。
A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance
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. Yet existing work on AI trustworthiness remains either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect with governance needs. We therefore propose a lightweight methodology for auditable trustworthiness levels in AI governance. The methodology has two components: a formal framework for representing and learning trustworthiness levels, and a lightweight AI lifecycle governance procedure for documenting, monitoring, and reassessing them over time. The formal framework models governance-relative trustworthiness through a context-sensitive protocol of measurable dimensions and learns trustworthiness levels as interpretable rules over trustworthiness profiles. Using decision trees as an interpretable proof-of-concept model class, the methodology yields explicit trustworthiness plateaus, readable level transitions, and two simple lifecycle diagnostics: boundary margins and profile drift. The governance procedure embeds these formal objects in a conformity-oriented workflow for design-time labeling, post-deployment monitoring, reassessment, and reporting. It also assigns human responsibilities and control gates for protocol design, validation, monitoring, and reassessment. We illustrate the methodology on synthetic AI lifecycle traces involving degradation, shocks, updates, heterogeneous monitoring cadences, and system comparison. Our methodology does not replace legal or other expert judgment: it supports conformity documentation and lifecycle monitoring by providing an evidential basis for documenting and tracking AI governance-relevant changes over time.