Authority-Based Constraints for Scalable Human–AI Interaction

As large-scale human–AI interaction systems expand across domains, users, and decision horizons, the problem of maintaining coherent and reliable interaction structures becomes increasingly critical. In particular, ambiguity in responsibility attribution and decision ownership can introduce instability, coordination failure, and degradation of system trust at scale. This paper proposes an authority-based constraint framework for scalable human–AI interaction. Rather than treating authority as an emergent or purely contextual property, the framework models authority attribution as an explicit structural constraint that stabilizes interaction dynamics across heterogeneous users and long-term deployments. By formalizing authority as a first-class coordination mechanism, the proposed approach aims to reduce decision volatility, improve consistency under delegation, and support predictable interaction patterns in large-scale systems. We analyze how authority-based constraints can serve as a unifying layer for managing delegation, responsibility distribution, and system extensibility, particularly in environments where human oversight is partial or intermittent. The framework emphasizes scalability and operational clarity over individualized adaptation, and is intended to support robust deployment in real-world, multi-user settings. This work contributes a conceptual foundation for understanding authority not as a social afterthought, but as a necessary structural element for scalable human–AI interaction.