提出可计算的人类福祉指标，让AI治理从合规变成有约束的优化问题。
The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
- 用三个政策杠杆建模人类福祉，生成可微分的福利指标。
- 发现自动化水平超过阈值后，再高也无益于整体福祉。
- 适合关注AI社会影响、政策制定与伦理治理的研究者。
现有AI治理框架（如欧盟AI法案、NIST AI RMF）虽涵盖安全、透明与问责，但未对宏观经济稳定设置量化约束。导致系统可能满足合规要求却加剧就业替代、不平等和经济脆弱性。本文提出人类福祉因子（HUF），一个可微分的福利度量，将主体能动性、福祉与经济稳定性建模为三个可操作政策杠杆——自动化深度、再分配强度与就业覆盖范围的函数。HUF推导出最优自动化水平及最低再分配阈值，使高阶治理目标可转化为可计算约束。在美、加、北欧三种政策制度下，通过三智能体多智能体强化学习框架评估，分析与PPO代理均识别出福利最优区域，并揭示关键失败模式：若福利度量未显式约束再分配，系统会收敛至高自动化均衡，表面符合指标却违背初衷。结果表明，AI治理本质是受约束的优化问题，而非单纯合规。HUF提供量化工具以评估自动化政策、识别社会经济稳定边界，支持加速部署下的治理决策。
原文摘要 · Abstract (English)
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.
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