🤖 AI Summary
Existing AI governance research predominantly emphasizes normative principles, lacking executable engineering mechanisms spanning the full AI lifecycle. This paper proposes a novel paradigm—“Responsibility as Closed-Loop Supervisory Control”—and introduces a six-layer control-theoretic architecture that formally encodes societal values—including fairness, autonomy, cognitive load, and explainability—into modelable, monitorable, and enforceable closed-loop constraints. Methodologically, it innovatively integrates safety envelope modeling, feedback-driven explanation frameworks, and end-to-end mapping of ethical objectives to control parameters, combining constrained optimization, runtime monitoring, behavioral interface design, and multi-tiered auditing. Empirical validation across clinical decision support, cooperative autonomous driving, and public-sector systems demonstrates real-time monitoring and dynamic assurance of normative objectives, enabling accountable, adaptive, and auditable AI deployment.
📝 Abstract
Artificial intelligence systems are increasingly deployed in domains that shape human behaviour, institutional decision-making, and societal outcomes. Existing responsible AI and governance efforts provide important normative principles but often lack enforceable engineering mechanisms that operate throughout the system lifecycle. This paper introduces the Social Responsibility Stack (SRS), a six-layer architectural framework that embeds societal values into AI systems as explicit constraints, safeguards, behavioural interfaces, auditing mechanisms, and governance processes. SRS models responsibility as a closed-loop supervisory control problem over socio-technical systems, integrating design-time safeguards with runtime monitoring and institutional oversight. We develop a unified constraint-based formulation, introduce safety-envelope and feedback interpretations, and show how fairness, autonomy, cognitive burden, and explanation quality can be continuously monitored and enforced. Case studies in clinical decision support, cooperative autonomous vehicles, and public-sector systems illustrate how SRS translates normative objectives into actionable engineering and operational controls. The framework bridges ethics, control theory, and AI governance, providing a practical foundation for accountable, adaptive, and auditable socio-technical AI systems.