Trustworthy AI Governance Framework for Autonomous and AI-Driven Networked Systems
Abdinasir Ismael Hashi, Abdirizak Mohamed Hashi, Osman Abdullahi Jama — International Journal of Computer Trends and Technology, 74(1), 12–27, 2026
The rapid expansion of autonomous and AI-driven networked systems across smart cities, transportation, healthcare, and cyber–physical infrastructures has intensified concerns related to trust, safety, transparency, and regulatory compliance. Existing approaches often address governance, ethics, and technical assurance in isolation, leaving a gap between high-level principles and practical system implementation. To address this challenge, this study proposes a comprehensive trustworthy AI governance framework that integrates system-theoretic modeling with governance constrained decision-making. The framework models autonomous systems as distributed cyber–physical–social systems and embeds ethical, safety, and legal constraints directly into the learning and optimization process using constrained Markov decision processes and Lagrangian optimization. A composite trustworthiness metric is formulated by aggregating Fairness, Robustness, Privacy, Explainability, Security, and Accountability dimensions. The methodology combines analytical modeling with simulated datasets representing multi-agent autonomous networks. Experimental results demonstrate a composite trust score of approximately 0.813, explainability stability of 0.905, and near-zero governance violations, confirming improved compliance, reduced risk, and enhanced trust compared to unguided AI systems.