🤖 AI Summary
This study addresses the systemic economic, psychological, and political risks arising from AI-driven labor displacement, highlighting a critical gap in current governance frameworks: the lack of effective safeguards for “meaningful human oversight.” Through an interdisciplinary systems analysis, the research offers the first clear conceptualization of meaningful human oversight and exposes a fundamental disconnect between nominal and substantive oversight in existing AI governance. Drawing on policy evaluation, institutional design, and human–AI interaction principles, the work proposes five structural requirements for robust governance and identifies a crucial 10–15 year window for intervention. The aim is to establish a governance framework that is both technically feasible and institutionally resilient, thereby preventing society from locking into irreversible path dependencies.
📝 Abstract
The accelerating displacement of human labor by artificial intelligence (AI) and robotic systems represents a structural transformation whose societal consequences extend far beyond conventional labor market analysis. This paper presents a systematic multi-domain examination of the likely effects on economic structure, psychological well-being, political stability, education, healthcare, and geopolitical order. We identify a critical and underexamined dimension of this transition: the governance gap between nominal human oversight of AI systems -- where humans occupy positions of formal authority over AI decisions -- and genuine human oversight, where those humans possess the cognitive access, technical capability, and institutional authority to meaningfully understand, evaluate, and override AI outputs. We argue that this distinction, largely absent from current governance frameworks including the EU AI Act and NIST AI Risk Management Framework 1.0, represents the primary architectural failure mode in deployed AI governance. The societal consequences of labor displacement intensify this problem by concentrating consequential AI decision-making among an increasingly narrow class of technical and capital actors. We propose five architectural requirements for genuine human oversight systems and characterize the governance window -- estimated at 10-15 years -- before current deployment trajectories risk path-dependent social, economic, and institutional lock-in.