From Moral Gatekeeping to Social Autopilot: Revealing the Normative Substitution Paradox in AI Delegation

Abstract

The rise of agentic AI systems, which are autonomous, proactive, and capable of multi-step task execution, has transformed how individuals interact with intelligent technologies. While these systems promise efficiency and enhanced decisionmaking, they also introduce new ethical vulnerabilities. This study investigates a paradoxical mechanism in AI-assisted academic task delegation: as social acceptance of AI delegation increases, individuals rely less on internal moral regulation. Drawing on Moral Disengagement Theory and Social Norms Theory, we test a normative substitution model using SEM data from 280 European university students and find that subjective norms function as both mediator and moderator, amplifying delegation intentions while reducing the influence of moral disengagement. Shame proneness emerges as a secondary moderator that buffers the normative pull for individuals with strong internal moral emotions. These findings highlight a critical socio-technical risk: proactive AI systems may unintentionally erode moral accountability as their use becomes socially normalized. We discuss implications for responsible agentic AI design, governance, and human-AI collaboration.