New York Institute of Emerging Technologies

A successful first Global Dialogue on AI Governance should establish a shared foundation for coordinated international action while respecting national sovereignty and diverse regulatory contexts. The most important outcome would be convergence on minimum global principles for safe, secure, and trustworthy AI systems, including agreed norms for risk management, transparency, and accountability across the AI lifecycle. Equally important is the creation of a durable multilateral coordination mechanism that enables ongoing technical exchange between governments, academia, civil society, and industry. This mechanism should support interoperability between regulatory frameworks rather than enforcing uniform regulation, allowing flexibility while reducing fragmentation. A successful dialogue should also produce concrete commitments toward global AI capacity-building, particularly for low- and middle-income countries, ensuring equitable access to AI infrastructure, talent development, and datasets. Finally, the dialogue should produce a roadmap for addressing high-risk AI applications, including clear escalation pathways for incidents involving safety, human rights, or systemic harm. The success of the forum should be measured not only by declarations, but by the establishment of actionable follow-up mechanisms and measurable milestones.

Safe, secure and trustworthy AI;AI capacity-building;Interoperability of governance approaches;Social, economic, ethical, cultural, linguistic and technical implications of AI;

These priorities reflect the need to ensure that AI development remains aligned with fundamental human values while enabling sustainable innovation. Safe, secure and trustworthy AI is essential to mitigate risks associated with increasingly autonomous and high-impact systems. Without robust safety standards, the scalability of AI systems introduces systemic risks. Protection and promotion of human rights is central to ensuring that AI systems do not reinforce bias, discrimination, or surveillance overreach. AI governance must be grounded in international human rights frameworks. Transparency, accountability, and human oversight are critical for maintaining public trust and ensuring that AI-driven decisions remain explainable, auditable, and contestable where necessary. These principles are also essential for regulatory compliance and liability frameworks. AI capacity-building ensures equitable participation in the global AI ecosystem. Without investment in skills, infrastructure, and institutional capacity, many regions risk being excluded from shaping and benefiting from AI advancements, exacerbating global inequality. Together, these themes provide a balanced approach that addresses safety, fairness, governance integrity, and inclusivity.

Several cross-cutting issues are not fully captured in the listed themes. First, environmental sustainability of AI systems is an emerging concern, particularly regarding the energy consumption and carbon footprint of large-scale model training and deployment. Second, geopolitical competition in AI development is shaping fragmented governance approaches, raising risks of regulatory divergence and technology silos that may undermine global cooperation. Third, data governance and sovereignty remain insufficiently addressed. Questions around data ownership, consent, cross-border data flows, and indigenous data rights are becoming increasingly important. Fourth, labor market transformation and economic displacement due to AI automation require stronger global coordination to manage transitions, reskilling, and social protection mechanisms. Finally, cybersecurity risks associated with AI systems-including model manipulation, adversarial attacks, and misuse for disinformation-require more explicit and coordinated international response frameworks. These issues cut across technical, ethical, and geopolitical dimensions and should be integrated into future iterations of the global AI governance dialogue.