Defining Global Standards for AI Safety through Multi-Stakeholder Consensus Frameworks Integrating Technical Robustness and Ethical Sovereignty
DOI:
https://doi.org/10.66280/cis.v4i1.133Abstract
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.
References
1.Shi, C., Li, S., Lu, W., Wu, W., Wang, C., Cheng, Z., ... & Chua, T. S. (2026). TraceRouter: Robust Safety for Large Foundation Models via Path-Level Intervention. arXiv preprint arXiv:2601.21900.
2.Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
3.Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
4.Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.
5.Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review.
6.Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
7.Hendrycks, D., & Dietterich, T. (2019). Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. ICLR.
8.Dafoe, A. (2018). AI Governance: A Research Agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford.
9.European Commission. (2021). Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act).
10.Brundage, M., et al. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint arXiv:1802.07228.
11.Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards Deep Learning Models Resistant to Adversarial Attacks. ICLR.
12.Hadfield-Menell, D., et al. (2016). Cooperative Inverse Reinforcement Learning. Advances in Neural Information Processing Systems.
13.O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
14.Awad, E., et al. (2018). The Moral Machine experiment. Nature, 563(7729), 59-64.
15.Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of Machine Learning Research.
16.Mittelstadt, B. (2019). AI Ethics – Too Principles-Based for the Real World? Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.
17.Whittlestone, J., et al. (2019). The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.
18.Leslie, D. (2019). Understanding artificial intelligence ethics and safety. The Alan Turing Institute.
19.Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.
20.Rahwan, I., et al. (2019). Machine behaviour. Nature, 568(7753), 477-486.
21.Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
22.Wallach, W., & Allen, C. (2008). Moral Machines: Teaching Robots Right from Wrong. Oxford University Press.
23.Christian, B. (2020). The Alignment Problem: Machine Learning and Human Values. W. W. Norton & Company.
24.Gabriel, I. (2020). Artificial Intelligence, Values, and Alignment. Minds and Machines, 30(3), 411-437.
25.Leike, J., et al. (2018). Scalable agent alignment via reward modeling: a research direction. arXiv preprint arXiv:1811.07871.
26.Perez, E., et al. (2022). Red Teaming Language Models with Language Models. arXiv preprint arXiv:2202.03286.
27.Raji, I. D., et al. (2020). Closing the AI Accountability Gap: Defining Challenges for Internal Algorithmic Auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
28.Selbst, A. D., et al. (2019). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency.
29.Winner, L. (1980). Do Artifacts Have Politics? Daedalus, 109(1), 121-136.
30.Jasanoff, S. (2016). The Ethics of Invention: Technology and the Human Future. W. W. Norton & Company.
31.Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
32.Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computational Intelligence Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
