The Urban Training and Studies Institute

The first Global Dialogue aims to move from high-level declarations to practical implementation. Several initiatives have been proposed or highlighted to illustrate how AI governance can address both opportunities and challenges. One prominent output is the Global Digital Facility, a cooperative model inspired by the European Organization for Nuclear Research (CERN). This facility is intended to provide a primary avenue for developing countries to access AI infrastructure and knowledge, allowing nations to share computing resources that they could not individually reproduce. Complementing this is the Global AI Safety Repository, which aims to host open evaluation suites, red-team scripts, and incident reporting tools aligned with international risk management frameworks, localized for low-resource languages. Another critical initiative is the UNESCO Readiness Assessment Methodology (RAM), a tool that helps countries evaluate their preparedness to adopt ethical and responsible AI. RAM identifies institutional and regulatory gaps, allowing UNESCO to provide tailored support for governments. Reports launched in February 2026 for India, Curaçao, and Trinidad and Tobago demonstrate how this methodology can result in actionable recommendations, such as the creation of a "Public AI Stack" and the integration of environmental sustainability into infrastructure planning. Key delivrables; 1) Global Frontier AI Evaluation Framework, A collaborative testing framework co-developed with regulators and scientific bodies to establish shared risk categories and disclosure thresholds; 2)Institutional Coherence Options Paper, A mapping of existing bodies and coordination models—such as a council, inter-agency mechanism, or observatory—to ensure a coherent global institutional architecture; 3)Responsible Public-Sector AI Guidelines, Minimum operational standards for procurement, transparency, and human oversight in government AI systems; 4)AI Infrastructure Access Framework, A global framework linking regional capacity hubs and facilitating shared computing access through a voluntary trust fund or credit mechanism.

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

safe and secure AI, encompasses the management of frontier models and the mitigation of emerging risks identified in recent scientific assessments. Discussion must move beyond high-level principles to define specific technical standards, audit protocols, and "defense-in-depth" strategies that combine multiple layers of safeguards. Capacity building, can involve leverage existing UN mechanisms and multi-stakeholder partnerships to support high-performance computing access and skills development in developing countries. Practical initiatives within this theme include the proposal for a Global Digital Facility, a cooperative model to share infrastructure and knowledge, and the expansion of open-source AI implementations for Global South. On ethical, social and technical AI implications This includes addressing algorithmic bias, protecting privacy, and ensuring robust human oversight. Discussions must examine how it affects labor markets and democratic safeguards.This thematic cluster should embed ethical principles by design across the AI lifecycle, from research to decommissioning. The interoperability cluster should addresses the need for compatibility between different national and regional governance approaches. It seeks to prevent a fragmented landscape that could lead to an "AI arms race" or a "race to the bottom" on safety and rights. This theme also prioritizes the development of open-source software, open data, and open models as public goods that foster innovation while maintaining accountability.

Convergence Gains and the Risks of Economic Divergence is an important area The UNDP and UNCTAD reports from late 2025 and early 2026 reinforce the economic imperative for inclusive governance. For half a century, lower-income countries narrowed the development gap through an "era of convergence" in technology and trade. However, the AI transition risks reversing these gains. Developing economies, particularly those relying on labor-intensive industries like manufacturing and agriculture, face significant exposure to automation. In South Asia and the Pacific, millions of jobs held by women and youth are at risk if ethical and inclusive governance is not prioritized. The Global Dialogue is a platform to ensure that the 4.8 trillion AI market does not remain in the hands of a "privileged few". This requires deliberate policy choices regarding technology transfer, investment in digital literacy, and the creation of "enabling innovation-based environments" in the Global South. Initiatives like Ghana's "One Million Coders" or Indonesia's digital trade roadmap illustrate how countries can transform job displacement fears into opportunities through proactive governance.

The Global Dialogue on AI Governance is designed to serve as the definitive multilateral platform for international cooperation, evidence-sharing, and the facilitation of inclusive discussions on the governance of civilian artificial intelligence. Historically, the governance of these technologies has been characterized by fragmentation across different regions, industries, and standards-setting bodies. The primary added value of this Dialogue lies in its capacity to create a unified "center of gravity" within the United Nations framework, anchoring technological development in international law and human rights. By providing a science-based and inclusive process, the Dialogue allows for a coherent global signal that transcends the voluntary or localized nature of previous efforts. A critical gap that the Global Dialogue must prioritize is the "Next Great Divergence," a term identifying the risk that AI might widen existing inequality gaps between countries. While advanced economies have rapidly deployed AI to enhance industrial productivity, many developing nations remain at the "starting line," lacking the digital infrastructure, high-quality local data, and skilled workforce necessary to participate in the AI transition. The Dialogue is mandated to address these capability gaps specifically to prevent a scenario where AI benefits are concentrated within a small number of countries and corporations. Statistics from UNCTAD suggest that just 100 companies are responsible for 40% of private R&D investment in AI, while 118 countries—largely in the Global South—have been entirely absent from global governance discussions.

The Global Dialogue on AI Governance serves as the definitive multilateral platform for international cooperation, designed to anchor technological development in international law and human rights.Its role in advancing cooperation is defined by several key functions: Inclusive Multi-Stakeholder Platform: It provides the first truly global forum where all 193 UN Member States, alongside the private sector, civil society, and the technical community, have an equal seat at the table to discuss AI regulation. This inclusivity is critical for legitimizing global norms that reflect the needs of the "Global Majority" rather than just advanced economies. Closing the Digital and Capability Divides: A central role of the Dialogue is to facilitate the implementation of the Sustainable Development Goals (SDGs) by closing the "AI divide". It promotes cooperation on capacity-building, high-performance computing access, and skills development for developing nations that might otherwise be left behind by the rapid pace of AI innovation. Promoting Governance Interoperability: By serving as a "center of gravity" for governance discussions, the Dialogue works to reduce regulatory fragmentation. It facilitates the development of interoperable and compatible governance approaches, ensuring that different national or regional rules do not create friction or "races to the bottom" regarding safety and human rights. Scientific-Policy Interface: The Dialogue operationalizes the findings of the Independent International Scientific Panel on AI (IISP-AI). By integrating evidence-based assessments into policy deliberations, it helps Member States move from competing geopolitical claims to a shared, fact-based understanding of AI risks and opportunities. Normative and Ethical Leadership: It provides a space to reinforce transparency, accountability, and robust human oversight of AI systems in a manner that complies with international law.

The Global Dialogue is designed to complement, not replace, existing governance efforts. Its success depends on its ability to build upon established mechanisms while providing a unique global "home" for coordination. Existing Initiatives and Partnerships to Build Upon: Global Digital Compact and Pact for the Future: These provide the foundational mandates and shared vision for an open, safe, and inclusive digital future. UNESCO's Ethical Frameworks: The Dialogue leverages the UNESCO Recommendation on the Ethics of AI and the Readiness Assessment Methodology (RAM), which helps countries evaluate their institutional and regulatory gaps. ITU's AI for Good Global Summit: The Dialogue is held back-to-back with this summit to tap into its vast technical community and its focus on applying AI to global challenges. OECD and G7 Hiroshima Process: It builds upon the OECD AI Principles and the G7's voluntary codes of conduct and reporting frameworks for developers. Technical Standards Bodies: Collaboration with ISO, ITU, and IEEE is essential for aligning technical standards on content provenance, safety benchmarks, and audit protocols. The Added Value of the Global Dialogue: Universal Legitimacy: Unlike the G7 or OECD, the UN Global Dialogue is the most globally inclusive approach to AI oversight, giving all nations—particularly those in the Global South—a formal role in shaping the rules. Institutional Coherence: It addresses the "coordination gap" by providing an inclusive, stable home for aligning fragmented regional and sectoral initiatives into a coherent global architecture. Comprehensive Transboundary Reach: Because AI is "transboundary in structure and application," the UN-anchored Dialogue is uniquely positioned to address risks—such as global information integrity and systemic safety failures—that single states or groups of states cannot control alone. Action-Oriented Practical Outputs: The Dialogue aims to deliver tangible results, such as the proposed "Global Digital Facility" for shared compute access and the "Global AI Safety Repository" for open-source evaluation tools.

The governance of AI affects all aspects of society, and therefore, governance frameworks must be co-created by a diverse array of actors. Resolution 79/325 explicitly identifies Member States, the private sector, civil society, academia, the technical community, and international organizations as essential contributors to the Dialogue. Member States provide the regulatory foundation and the political will to implement global norms. Their role involves establishing national focal points, commissioning risk-benefit assessments, and setting procurement requirements that prioritize ethical AI. The private sector, particularly organizations developing advanced systems, is expected to share safety evaluations, transparency reports, and technical insights while supporting capacity building in the Global South through voluntary funding and open tools. The technical community and academia are vital for the development of interoperable standards and the provision of objective evidence. Organizations like the ITU, UNESCO, ISO, and IEEE are already collaborating to categorize standards into clusters such as content provenance, watermarking, and rights declarations. Civil society and communities of practice ensure that governance remains human-centric and rooted in the lived complexity of real communities, acting as a safeguard against "automation bias" and algorithmic discrimination.

Developing Countries and the Global south

To ensure that Panel's report informs actionable policy, the Dialogue must include dynamic engagement formats, such as expert-level workshops and tabletop exercises simulating AI-enabled attack scenarios. These formats encourage the translation of technical risk assessments into specific regulatory requirements or institutional capacity-building efforts. Furthermore, the virtual consultations held in March 2026, divided into geographically and time-zone-inclusive sessions, demonstrate a commitment to accessibility that should be maintained in the July session to ensure a truly global perspective.

Effective AI governance is transitioning from high-level ethical principles to operational reality through a combination of binding regulations, voluntary international standards, and technical oversight tools. Below are concrete examples of policies, practices, and platforms currently addressing AI governance challenges. 1. Robust Policy Frameworks and Regulatory Approaches Risk-Based Regulation (EU AI Act): A landmark binding law that categorizes AI systems into four levels of risk (unacceptable, high, limited, and minimal). It requires high-risk systems to undergo conformity assessments and implement strict data governance and transparency measures. Voluntary International Standards (G7 Hiroshima Process): This multilateral framework provides "International Guiding Principles" and a "Code of Conduct" for organizations developing advanced AI. It includes a reporting framework where major developers like Amazon, Google, and OpenAI disclose structured data on risk management and testing protocols. National Operational Roadmaps: Singapore's Model AI Governance Framework provides a practical guide for organizations to define risk classifications, accountability structures, and lifecycle controls. Similarly, India has launched a hybrid governance framework focused on "People, Planet, and Progress". 2. Evidence-Based Practices and Methodologies AI Readiness Assessment Methodology (RAM): Developed by UNESCO, this tool allows governments to self-assess their institutional, legal, and technological preparedness. Recent implementations in India and Trinidad and Tobago have led to actionable recommendations on embedding "ethics by design" and strengthening center-state coordination. Defense-in-Depth: A technical safety strategy that layers multiple safeguards-such as pre-deployment content filters, red-teaming, and post-deployment monitoring-to ensure that a single failure does not lead to significant harm. Regulatory Sandboxes: Controlled environments where innovators test AI systems under regulatory supervision. For example, Brazil's sandbox focuses on generative AI overseen by its Data Protection Authority, while the EU mandates at least one operational sandbox per Member State by August 2026. 3. Platforms and Technical Solutions OECD.AI Policy Navigator: A live repository hosting over 900 national AI policies and initiatives from 80 jurisdictions, enabling policymakers to benchmark governance efforts and share best practices. Global AI Safety Repository: A proposed platform for hosting open evaluation suites, red-team scripts, and incident reporting tools aligned with international frameworks like the NIST AI RMF, localized for low-resource languages. Digital Public Infrastructure (DPI-for-AI): An approach to democratizing AI by extending public infrastructure principles to create "Data Commons" and "Open Model Stacks," such as the Bhashini platform for multilingual AI in India. Open-Source Governance Directories: Platforms like the Open Source AI Governance Directory track over 24 resources, including toolkits like AI Fairness 360 for detecting and mitigating algorithmic bias. 4. Technical Oversight and Monitoring Automated Policy Enforcement: Modern platforms allow for the consistent application of governance rules across teams, automatically blocking or quarantining AI actions that violate data-privacy or safety policies. Provenance and Watermarking: Technical standards for content authenticity-categorized into clusters like content provenance, rights declarations, and watermarking-help identify and track AI-generated media to combat misinformation. Reasoning Model Safeguards: As models achieve graduate-level performance in math and science. new frameworks are being developed to manage "agentic AI" that can autonomously perform complex software tasks.