New Metric Reframes AI Governance as Optimization Problem

Current AI governance frameworks, such as the EU AI Act, primarily focus on safety, transparency, and accountability but often lack quantitative measures for macro-socioeconomic stability. This oversight means AI systems could meet regulatory standards while still contributing to issues like job displacement and rising inequality. To address this, a new metric called the Human Utility Factor (HUF) has been proposed. HUF is a differentiable welfare metric that models the interplay between individual agency, societal wellbeing, and economic stability. It links these factors to three actionable policy levers: the depth of automation, the intensity of wealth redistribution, and the coverage of employment. By doing so, HUF transforms abstract governance objectives into concrete, computable constraints. The metric can identify an optimal level of automation and a minimum redistribution threshold below which no level of automation would be welfare-positive. Evaluated using a multi-agent reinforcement learning framework across different policy regimes (U.S., Canadian, Nordic), HUF consistently identified welfare-optimal operating regions. The research highlights a critical failure mode: welfare metrics without explicit redistribution constraints can lead to high-automation scenarios that appear beneficial by the metric but undermine actual societal objectives. This suggests AI governance is fundamentally an optimization problem, not just a compliance exercise.