AI 中文总结
研究提出人类效用因子（HUF）这一可计算福利度量，将AI治理转化为约束优化问题，经多智能体强化学习评估，发现需明确约束再分配以避免高自动化均衡损害社会目标。
AI 中文摘要
现有的AI治理框架，包括《欧盟人工智能法案》和美国国家标准与技术研究院人工智能风险管理框架（NIST AI RMF），虽涉及安全性、透明度和问责制，但未对宏观社会经济稳定性实施量化约束。因此，AI系统可能满足监管要求，却加剧劳动力置换、不平等加剧和经济韧性下降。我们提出人类效用因子（Human Utility Factor, HUF），这是一种可微分的福利度量，将能动性、福祉和经济稳定性之间的相互作用建模为三个可操作政策杠杆的函数：自动化深度、再分配强度和就业覆盖率。HUF得出闭式最优自动化水平和最低再分配阈值，低于该阈值时任何自动化水平均不具有正福利效应，将高层治理目标转化为可计算约束。我们使用三智能体多智能体强化学习框架，在美国、加拿大和北欧政策体制下评估HUF。解析型智能体和基于近端策略优化（PPO）的智能体均识别出福利最优运行区域，并揭示关键失效模式：未明确约束再分配的福利度量可能收敛于高自动化均衡，满足度量要求却破坏预期社会目标。我们的结果表明，AI治理本质上是约束优化问题而非合规练习。HUF提供了一个量化框架，用于评估自动化政策、识别社会经济稳定性边界，并在AI加速部署下支持治理决策。
英文摘要
Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability. As a result, AI systems may satisfy regulatory requirements while contributing to labor displacement, rising inequality, and reduced economic resilience. We introduce the Human Utility Factor (HUF), a differentiable welfare metric that models the interaction between Agency, Wellbeing, and Economic Stability as functions of three actionable policy levers: automation depth, redistribution intensity, and employment coverage. HUF yields a closed-form optimal automation level and a minimum redistribution threshold below which no level of automation is welfare-positive, transforming high-level governance objectives into computable constraints. We evaluate HUF using a three-agent multi-agent reinforcement learning framework across U.S., Canadian, and Nordic policy regimes. Both analytical and PPO-based agents identify welfare-optimal operating regions and reveal a critical failure mode: welfare metrics that do not explicitly constrain redistribution can converge to high-automation equilibria that satisfy the metric while undermining its intended societal objectives. Our results suggest that AI governance is fundamentally a constrained optimization problem rather than a compliance exercise. HUF provides a quantitative framework for evaluating automation policies, identifying socioeconomic stability boundaries, and supporting governance decisions under accelerating AI deployment.
