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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 30·5 min read
The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
TL;DR
A new metric called the Human Utility Factor (HUF) has been developed to quantitatively assess the welfare impacts of AI systems, addressing gaps in existing governance frameworks. HUF integrates factors like Agency and Wellbeing to ensure AI contributes positively to socioeconomic stability.
✦ Why It Matters
Consider integrating the Human Utility Factor in your AI project assessments to enhance socioeconomic outcomes.
Key Takeaways
- Implement the Human Utility Factor in AI governance frameworks.
- Assess automation policies against socioeconomic stability metrics.
- Ensure redistribution measures are included in AI deployment strategies.
- Utilize multi-agent reinforcement learning for policy evaluation.
How It Works
HUF quantifies welfare by modeling the relationships between Agency, Wellbeing, and Economic Stability. It uses three policy levers: automation depth, redistribution intensity, and employment coverage, to derive optimal automation levels and minimum redistribution thresholds.
This approach allows policymakers to transform abstract governance goals into actionable, computable constraints.