The Social Responsibility Stack: A Control-Theoretic Architecture for Governing Socio-Technical AI

TL;DR

The paper addresses the gap between normative responsible AI guidance and enforceable engineering practice by introducing the Social Responsibility Stack (SRS), a six-layer closed-loop control architecture that grounds societal values as explicit constraints, safeguards, monitoring, and governance across the AI lifecycle. It integrates value grounding, socio-technical impact modeling, design-time safeguards, behavioral interfaces, continuous auditing, and governance into a unified framework, with a formal constraint model and threat taxonomy. Through case studies in AI triage, cooperative autonomous vehicles, and e-government, the authors illustrate how SRS translates normative objectives into actionable engineering and organizational controls. The work aims to improve accountability, adaptivity, and auditable oversight of socio-technical AI systems and calls for reference implementations and regulatory collaboration to operationalize closed-loop responsibility at scale.

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.

Figures (1)

• Figure 1: The Social Responsibility Stack (SRS). A hierarchical control architecture in which societal values are grounded as explicit constraints and propagated upward through design-time safeguards, behavioral feedback, continuous social auditing, and supervisory governance.