The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem

• URL: http://arxiv.org/abs/2607.26068v1
• Date: Tue, 23 Jun 2026 04:47:33 GMT
• Title: The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
• Abstract summary: Existing AI governance frameworks address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability.<n>We introduce the Human Utility Factor (HUF), a differentiable welfare metric that models the interaction between Agency, Wellbeing, and Economic Stability.<n>HUF yields a closed-form optimal automation level and a minimum redistribution threshold below which no level of automation is welfare-positive.
• Score: 3.4328929125111514
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The AI Resilience Gap: Bringing Artificial Intelligence Inside the Operational Resilience Perimeter [0.0]
  We argue that AI adoption creates a resilience obligation distinct from, and inadequately covered by, the trustworthy AI stack.<n>We propose the AI Resilience Framework: a regime-agnostic method for bringing AI dependencies inside the operational resilience perimeter.
  arXiv  Detail & Related papers  (2026-07-08T12:54:11Z)
• Foundation Protocol: A Coordination Layer for Agentic Society [78.01727114524464]
  Foundation Protocol (FP) is a graph-first coordination layer for an emerging human-AI society.<n>FP unifies heterogeneous entities, including agents, tools, resources, humans, institutions, and organizations.<n>The aim is to keep autonomous agency composable while keeping accountability non-negotiable.
  arXiv  Detail & Related papers  (2026-05-22T04:20:35Z)
• Position: agentic AI orchestration should be Bayes-consistent [92.04373485710285]
  This paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system.<n>It provides examples and design patterns to illustrate how beliefs and utility-aware policies can improve agentic AI orchestration.
  arXiv  Detail & Related papers  (2026-05-01T15:43:43Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• Institutional AI: A Governance Framework for Distributional AGI Safety [1.3763052684269788]
  We identify three structural problems that emerge from core properties of AI models.<n>The solution is Institutional AI, a system-level approach that treats alignment as a question of effective governance of AI agent collectives.
  arXiv  Detail & Related papers  (2026-01-15T17:08:26Z)
• AI Autonomy Coefficient ($α$): Defining Boundaries for Responsible AI Systems [0.15293427903448018]
  Integrity of contemporary AI systems is compromised by misuse of Human-in-the-Loop models.<n>We introduce the AI-First, Human-Empowered (AFHE) paradigm, which requires AI systems to demonstrate a quantifiable level of functional independence.<n>We conclude that AFHE provides a metric-driven approach for ensuring verifiable autonomy, transparency, and sustainable operational integrity in modern AI systems.
  arXiv  Detail & Related papers  (2025-12-12T05:41:20Z)
• Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare [87.06241096619112]
  Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.<n>We introduce the Social Welfare Function Benchmark, a dynamic simulation environment where an LLM acts as a sovereign allocator.<n>We evaluate 20 state-of-the-art LLMs and present the first leaderboard for social welfare allocation.
  arXiv  Detail & Related papers  (2025-10-01T17:52:31Z)
• LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra [29.627070781534698]
  We present a novel framework that uses agent-based modeling to design and assess economic policies.<n>At the lower level, bounded rational worker agents choose labor supply to maximize text-based utility functions learned in-context.<n>At the upper level, a planner agent employs in-context reinforcement learning to propose piecewise-linear marginal tax schedules anchored to the current U.S. federal brackets.
  arXiv  Detail & Related papers  (2025-07-21T17:21:14Z)
• Societal Adaptation to AI Human-Labor Automation [0.0]
  This paper analyzes how society can adapt to AI-driven human-labor automation.<n>The threat model is centered on mass unemployment and its socioeconomic consequences.<n>The analysis explores both "capability-modifying interventions" (CMIs) that shape how AI develops, and "adaptation interventions" (ADIs) that help society adjust.
  arXiv  Detail & Related papers  (2024-12-07T15:08:11Z)
• PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback [106.63518036538163]
  We present a novel unified bilevel optimization-based framework, textsfPARL, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning. Our framework addressed these concerns by explicitly parameterizing the distribution of the upper alignment objective (reward design) by the lower optimal variable. Our empirical results substantiate that the proposed textsfPARL can address the alignment concerns in RL by showing significant improvements.
  arXiv  Detail & Related papers  (2023-08-03T18:03:44Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations [110.72725220033983]
  Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps. ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations. In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
  arXiv  Detail & Related papers  (2021-06-10T04:32:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.