AI Safety Should Prioritize the Future of Work

2025-04-16

AI Summary

Contemporary AI safety research overemphasizes content moderation and existential risk mitigation, neglecting AI’s long-term structural impacts on labor markets—exacerbating income inequality, devaluing creative labor, and intensifying resource extraction and innovation monopolies through closed development models. This paper proposes a “worker-centered” paradigm for global AI governance, integrating institutional economics, labor theory, and science & technology policy to construct an interdisciplinary analytical framework. It introduces the novel concepts of *international copyright anatomy* and *collective licensing mechanisms*, operationalizing fair compensation pathways for AI training data contributors. The work reframes the AI safety agenda—from narrow technical risk management toward safeguarding labor value, advancing economic justice, and fostering shared prosperity. These contributions deliver a normatively grounded yet practically implementable institutional innovation for global AI governance.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society. In this position paper, we identify the risks of overlooking the impact of AI on the future of work and recommend comprehensive transition support towards the evolution of meaningful labor with human agency. Through the lens of economic theories, we highlight the intertemporal impacts of AI on human livelihood and the structural changes in labor markets that exacerbate income inequality. Additionally, the closed-source approach of major stakeholders in AI development resembles rent-seeking behavior through exploiting resources, breeding mediocrity in creative labor, and monopolizing innovation. To address this, we argue in favor of a robust international copyright anatomy supported by implementing collective licensing that ensures fair compensation mechanisms for using data to train AI models. We strongly recommend a pro-worker framework of global AI governance to enhance shared prosperity and economic justice while reducing technical debt.

Problem

• AI safety overlooks impact on future of work
• AI exacerbates income inequality in labor markets
• Closed-source AI development monopolizes innovation unfairly

Innovation

• Comprehensive transition support for meaningful labor
• International copyright anatomy with collective licensing
• Pro-worker framework for global AI governance