发表机构
University of Zürich; SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA); ETH Zürich（苏黎世大学; 瑞士南部应用科学与艺术大学，达勒·莫勒人工智能研究所; 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
针对AI治理中多系统群体问题，提出凸AI组合性形式化方法，利用凸空间表示配置变化，兼容生命周期可达性与身份关系，并在医疗和招聘场景中验证。
AI 中文摘要
AI治理日益要求提供者和公共机构对多个AI实例、替代版本以及多个AI系统的部署配置进行推理。然而，现行监管仍以单一系统为中心，仅在少数情况下承认这种多重性，而未将相关AI系统的集合视为治理对象。这产生了AI群体治理问题：确定哪些实例可以被有意义地视为一个整体，以及如何表示和监控它们不断变化的配置。第一个需求最近已通过基于可信度的AI身份理论得到解决。我们通过引入凸AI组合性来解决第二个需求：一种使用凸空间对有限AI系统群体生成的配置进行形式化表示的方法。核心思想是，AI系统实例群体随时间可能占据的操作状态的凸组合，与整个群体的生命周期可达性兼容，并能保持这些系统之间的形式身份关系。众所周知的统计和几何构造，如加权状态分布和凸包，成为AI治理工具，用于区分不同治理模式下的操作状态、群体权重、异质性和AI配置变化，同时在所述条件下与生命周期可达性和AI身份保持兼容。我们通过分布式医疗部署和招聘AI变体的受控部署来展示我们的AI群体治理框架。
英文摘要
AI governance increasingly requires providers and public authorities to reason about multiple AI instantiations, alternative versions, and deployment configurations of multiple AI systems. Yet current regulation remains predominantly single-system-centric, acknowledging such multiplicity only sparsely without treating collections of related AI systems as governance objects. This creates an AI population governance problem: determining which instantiations can be meaningfully considered together and how their changing configurations can be represented and monitored. The first requirement has recently been addressed through trustworthiness-based accounts of AI identity. We address the second by introducing convex AI compositionality: a formal representation of the configurations generated by finite AI system populations that uses convex spaces. The core idea is that convex compositions of the operational states that a population of AI system instantiations may occupy over time are compatible with lifecycle reachability across the population and can preserve the formal identity relations between these systems. Well-known statistical and geometric constructions, such as weighted state distributions and convex hulls, become AI governance tools for distinguishing operational states, population weights, heterogeneity, and AI configuration change across different governance modes while remaining compatible, under stated conditions, with lifecycle reachability and AI identity. We illustrate our AI population governance framework through distributed healthcare deployments and controlled deployment of recruitment AI variants.
Comments20 pages, 5 figures, v1
