This paper introduces the Human-AI Governance (HAIG) framework, contributing to the AI governance (AIG) field by foregrounding the relational dynamics between human and AI actors rather than treating AI systems as objects of governance alone. Current categorical frameworks (e.g., human-in-the-loop models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems are deployed across contexts, agency redistributes in complex patterns that are better represented as positions along continua rather than discrete categories. The HAIG framework operates across three levels: dimensions (Decision Authority, Process Autonomy, and Accountability Configuration), continua (continuous positional spectra along each dimension), and thresholds (critical points along the continua where governance requirements shift qualitatively). The framework's dimensional architecture is level-agnostic, applicable from individual deployment decisions and organisational governance through to sectorial comparison and national and international regulatory design. Unlike risk-based or principle-based approaches that treat governance primarily as a constraint on AI deployment, HAIG adopts a trust-utility orientation - reframing governance as the condition under which human-AI collaboration can realise its potential, calibrating oversight to specific relational contexts rather than predetermined categories. Case studies in healthcare and European regulation demonstrate how HAIG complements existing frameworks while offering a foundation for adaptive regulatory design that anticipates governance challenges before they emerge.

翻译：本文提出了人机协同治理（HAIG）框架，通过强调人类与人工智能参与者之间的动态关系而非仅将AI系统视为治理对象，为人工智能治理领域做出贡献。当前分类框架（例如人在回路模型）未能充分捕捉AI系统如何从工具演变为合作伙伴，尤其是在基础模型展现出涌现能力、多智能体系统表现出自主目标设定行为的背景下。随着系统在不同情境中部署，主体性以复杂模式重新分配，这更适合用连续谱上的位置而非离散类别来表征。HAIG框架在三个层面运作：维度（决策权威、过程自主性与责任配置）、连续谱（各维度上的连续位置谱）以及阈值（连续谱上治理要求发生质变的关键点）。该框架的维度架构具有层级无关性，可适用于从个体部署决策与组织治理，到行业比较乃至国家与国际监管设计的各个层面。与将治理主要视为AI部署约束的风险导向或原则导向方法不同，HAIG采用信任-效用导向——将治理重新定义为实现人机协作潜能的先决条件，使监督机制能够根据具体关系情境而非预设类别进行校准。医疗健康与欧洲监管领域的案例研究表明，HAIG如何与现有框架形成互补，同时为预见治理挑战的适应性监管设计奠定基础。