The Social Responsibility Stack: A Control-Theoretic Architecture for Governing Socio-Technical AI
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesSimilar Papers 제목 키워드 기반
Towards a Socially Acceptable Competitive Equilibrium in Energy Markets
This paper addresses the problem of energy sharing between a population of price-taking agents who adopt decentralized primal-dual gradient dynamics to find the Competitive Equilibrium (CE). Although the CE is efficient,…
FairnessLearning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
From autonomous driving to package delivery, ensuring safe yet efficient multi-agent interaction is challenging as the interaction dynamics are influenced by hard-to-model factors such as social norms and contextual cues…
Autonomous DrivingCorporate Social Responsibility and Corporate Governance: A cognitive approach
This chapter aims to critically review the existing literature on the relationship between corporate social responsibility (CSR) and corporate governance features. Drawn on management and corporate governance theories, w…
DiversityManagementLearning Probabilistic Responsibility Allocations for Multi-Agent Interactions
Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Understanding how people allocate responsibility, i.e., how much one deviates…
Trajectory ForecastingAutonomy for Older Adult-Agent Interaction
As the global population ages, artificial intelligence (AI)-powered agents have emerged as potential tools to support older adults' caregiving. Prior research has explored agent autonomy by identifying key interaction st…