发表机构
Charles Darwin University（查尔斯达尔文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发机构AI治理压力测试（IAGST），结合多种逻辑与规则，以133份西澳大学文献和15个场景测试，发现AI治理权限、流程等随能力发展需保持关联，该工具用于政策学习。
AI 中文摘要
高校制定人工智能原则和使用政策的速度，快于其为不熟悉的人工智能主体形式构建决策路径的速度。本研究开发了机构人工智能治理压力测试（IAGST），这是一种用于定位公开记录的治理在何处不再产生问责回应的前瞻性文献方法。IAGST 改编自成熟的政策压力测试和风洞测试逻辑，其独创性在于结合了可控能力升级、冻结的文献库、六维治理回应链、非补偿性决策规则以及案例级断点诊断。该方法使用来自五所西澳大利亚大学的133份实质性公开文献和15个经质量筛选的场景进行演示，共产生75个大学-场景组合：6个案例得到解决，14个通过结构化裁量解决，55个不确定。治理路径在25个增强案例中占16个，在4个委托案例中下降，在自主替代案例中为0。主要弱点并非完全缺乏责任角色：所有50个权限缺口案例均以通用级别命名了角色，但缺乏足够的决策标准或流程。研究结果表明，高校可通过测试随着AI能力发展，权限、流程、保障措施和审查是否仍保持关联，从而超越政策清单和校长声明。IAGST是一种用于政策学习的可重复诊断工具，而非实施情况的排名或衡量标准。
英文摘要
Universities are producing AI principles and use policies faster than they are building decision pathways for unfamiliar forms of AI agency. This study develops Institutional AI Governance Stress Testing (IAGST), a prospective documentary method for locating where publicly documented governance ceases to yield an accountable response. IAGST adapts established policy stress-testing and wind-tunneling logic. Its originality lies in combining controlled capability escalation, a frozen documentary corpus, a six-dimensional governance response chain, non-compensatory decision rules, and case-level breakpoint diagnosis. The method was demonstrated using 133 substantive public documents from five Western Australian universities and 15 quality-screened scenarios, resulting in 75 university-scenario encounters. Six cases were resolved, 14 were resolved through structured discretion, and 55 were indeterminate. Governed pathways fell from 16 of 25 augmentation cases to four delegation cases and none at autonomous substitution. The dominant weakness was not the complete absence of responsible roles: all 50 authority-gap cases named a role at only a generic level but lacked sufficient decision criteria or process. The findings show how universities can move beyond policy inventories and principal statements by testing whether authority, procedures, safeguards, and reviews remain connected as AI capabilities evolve. IAGST is a reproducible diagnostic for policy learning, not a ranking or measure of implementation.
