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

TL;DR

The paper presents the Social Responsibility Stack (SRS), a control-theoretic framework for embedding societal values into AI systems through layered governance, monitoring, and enforcement mechanisms to ensure accountability and societal alignment.

Contribution

It introduces a novel six-layer architectural framework that operationalizes responsible AI principles as enforceable engineering controls within socio-technical systems.

Findings

SRS models responsibility as a closed-loop control problem.

It enables continuous monitoring of fairness, autonomy, and explanation quality.

Case studies demonstrate practical implementation in diverse domains.

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.