The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
Sivasathivel Kandasamy
Why It Matters
What makes this one worth your time
Understanding and optimizing the socioeconomic impacts of AI deployment is crucial for policymakers and researchers to ensure AI benefits society equitably.
The paper proposes a new welfare metric, HUF, to optimize AI governance policies.
Summary
The paper introduces the Human Utility Factor (HUF), a welfare metric that models interactions between agency, wellbeing, and economic stability, and reframes AI governance as a constrained optimization problem. It uses a multi-agent reinforcement learning framework to evaluate the metric across different policy regimes.
Key contributions
- Introduction of the Human Utility Factor (HUF) as a welfare metric.
- Evaluation of HUF using a multi-agent reinforcement learning framework.
- Identification of critical failure modes in welfare metrics without redistribution constraints.
Notable insights
- The HUF metric transforms high-level governance objectives into computable constraints.
- The study reveals a critical failure mode where welfare metrics without explicit redistribution constraints can lead to undesirable high-automation equilibria.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.26068v1 Announce Type: cross Abstract: 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.