Defining Global Standards for AI Safety through Multi-Stakeholder Consensus Frameworks Integrating Technical Robustness and Ethical Sovereignty - Samuel Higgins; Patrick Hawthorne (Computational Intelligence Systems, 2026)
The rapid escalation of generative artificial intelligence and large-scale foundation models has outpaced the development of international regulatory frameworks, creating a fragmented landscape of safety protocols. This paper proposes a comprehensive global standard for AI safety that moves beyond localized governance toward a multi-stakeholder consensus framework. By integrating the divergent requirements of technical robustness—defined as the quantifiable resilience of systems against adversarial and systemic failures—with ethical sovereignty, which respects the cultural and political autonomy of diverse jurisdictions, this research establishes a structural blueprint for international cooperation. The discussion explores the architectural trade-offs inherent in balancing centralized safety audits with decentralized deployment needs. We argue that global AI safety cannot be achieved through a monocultural ethical lens or a purely technocratic approach; rather, it requires a socio-technical infrastructure that supports path-level interventions, transparent auditing, and inclusive governance models. Through a deep analysis of systemic risks, including the potential for catastrophic failure in socio-technical infrastructures, this paper delineates the necessary requirements for cross-border alignment. The proposed framework emphasizes the importance of robust safety interventions at the architectural level while maintaining the flexibility required for sovereign states to implement localized ethical guardrails. Ultimately, this work serves as a foundational roadmap for policy makers, engineers, and ethicists to harmonize the dual imperatives of innovation and security in an increasingly automated global economy.