UN U4SSC

A successful first Global Dialogue on AI Governance should deliver outcomes that are both foundational and actionable, setting the stage for sustained global cooperation. First, it should establish a shared baseline of principles—including safety, human rights, transparency, and inclusivity—that are endorsed by a broad coalition of countries. Even if not legally binding, such alignment would reduce fragmentation and signal a collective commitment to responsible AI development. Second, the dialogue should result in a clear roadmap for ongoing collaboration, including the creation (or mandate) of a permanent, multistakeholder coordination mechanism. This body should facilitate knowledge-sharing, track risks, and support alignment across national and regional frameworks. Third, success would include agreement on a risk-based approach to AI governance, particularly identifying categories such as high-risk and frontier AI systems that require enhanced oversight, evaluation, and safeguards. Fourth, it should meaningfully advance inclusion of the Global South, through commitments to capacity-building, technical assistance, and equitable participation in shaping standards—ensuring governance is not dominated by a few technologically advanced nations. Fifth, the dialogue should initiate practical cooperation measures, such as voluntary safety standards, model evaluation benchmarks, and information-sharing protocols for AI-related incidents. Finally, a successful outcome would be the establishment of a structured process for continued input, ensuring that governments, industry, academia, and civil society can iteratively contribute as AI evolves. In essence, success lies not in resolving all challenges immediately, but in building trust, aligning direction, and creating durable mechanisms for collective global governance of AI.

• Safe, secure and trustworthy AI
• Transparency, accountability, and human oversight
• Protection and promotion of human rights
• AI capacity-building

The selected priorities reflect a balanced approach that addresses both the risks and opportunities of AI, while ensuring global inclusivity. Safe, secure and trustworthy AI is foundational, as the reliability and safety of AI systems underpin public trust and long-term adoption. Without robust safeguards, the potential harms of AI-ranging from technical failures to misuse-could outweigh its benefits. Transparency, accountability, and human oversight are essential to ensure that AI systems remain understandable and controllable. Clear accountability across the AI lifecycle helps build trust, enables redress in cases of harm, and prevents opaque decision-making that can undermine institutions. Protection and promotion of human rights is a critical priority, as AI systems increasingly influence access to opportunities, information, and services. Embedding human rights principles ensures that AI does not reinforce discrimination, bias, or inequality, and instead contributes to human dignity and fairness. Finally, AI capacity-building is vital to ensure that all countries-especially those in the Global South-can meaningfully participate in AI development and governance. Without this, global AI governance risks becoming uneven and exclusionary, limiting both innovation and equitable benefit-sharing. Together, these priorities emphasize a governance approach that is safe, rights-based, accountable, and inclusive, which is essential for ensuring that AI serves the collective interests of humanity.

Yes, while the listed themes are comprehensive, several important cross-cutting and emerging issues deserve explicit attention. First, compute governance and concentration of power is an increasingly critical issue. The development of advanced AI systems depends on access to large-scale computational resources, which are concentrated among a small number of actors. This raises concerns about unequal influence, barriers to entry, and systemic risks if a few entities dominate frontier AI capabilities. Second, environmental sustainability of AI is often underemphasized. Training and deploying large AI models require significant energy and water resources. Governance frameworks should incorporate sustainability standards to ensure that AI development aligns with global climate and environmental goals. Third, AI in information integrity and democratic processes is a rapidly evolving challenge. The proliferation of synthetic media and automated content generation can amplify misinformation, undermine public trust, and affect electoral systems. This issue cuts across safety, human rights, and governance, but merits distinct and focused attention. Fourth, evaluation and measurement standards remain underdeveloped globally. There is a need for shared benchmarks and methodologies to assess AI systems' safety, bias, robustness, and societal impact in a consistent and comparable manner. Finally, long-term and systemic risks from highly capable AI systems-including unintended consequences and loss of human control-require proactive international dialogue, even as near-term issues are addressed. Addressing these cross-cutting issues would strengthen the overall effectiveness, foresight, and resilience of global AI governance efforts.

In emerging economies such as India and the broader Global South, governance gaps in AI are creating a mix of significant challenges and strategic opportunities. A key challenge is the uneven capacity to develop, deploy, and regulate AI systems. While AI adoption is accelerating across sectors such as healthcare, education, and public services, institutional and technical capacity to ensure safety, auditing, and oversight remains limited. This creates risks of deploying systems that may be biased, unreliable, or insufficiently tested in local contexts. Gaps in transparency and accountability further complicate this landscape. Many AI systems are imported or built on global platforms, making it difficult to fully understand their design, data sources, or decision-making processes. This can reduce trust and limit the ability of local regulators to enforce standards or provide redress in cases of harm. From a human rights perspective, there are concerns that AI systems may inadvertently reinforce existing inequalities—particularly in areas such as access to finance, employment, and public services—if they are not adapted to diverse linguistic, cultural, and socioeconomic contexts. At the same time, there are major opportunities. AI has the potential to accelerate inclusive development, improve service delivery, and enable innovation across sectors. With appropriate governance, countries can leapfrog traditional development barriers. Importantly, the current moment presents an opportunity to shape governance frameworks proactively, rather than retroactively. By investing in capacity-building, fostering local innovation ecosystems, and engaging in global standard-setting processes, emerging economies can ensure that AI governance reflects their needs and priorities. Overall, bridging these governance gaps is essential to unlocking AI's benefits while safeguarding public trust and societal well-being.

The AI Dialogue can play a pivotal role as a neutral, inclusive, and action-oriented platform for advancing international cooperation on AI governance. First, it can serve as a convening space that brings together governments, industry, academia, and civil society to build shared understanding and trust. Given the global and rapidly evolving nature of AI, such dialogue is essential to bridge differing national priorities and reduce fragmentation in governance approaches. Second, the Dialogue can help align principles and standards by facilitating convergence around core norms such as safety, human rights, transparency, and accountability. Even without immediate binding agreements, soft alignment can significantly improve interoperability across jurisdictions. Third, it can act as a catalyst for practical cooperation, enabling initiatives such as joint research on AI safety, shared evaluation frameworks, and information-sharing mechanisms for risks and incidents. These collaborative efforts can enhance global preparedness and responsiveness. Fourth, the Dialogue can promote equitable participation, particularly by amplifying the voices of developing countries. By supporting capacity-building and knowledge exchange, it can help ensure that AI governance is inclusive and reflects diverse perspectives. Fifth, it can provide continuity and adaptability by establishing an ongoing, iterative process rather than a one-time event. This is critical given the pace of technological change, allowing governance approaches to evolve in step with new developments. Ultimately, the AI Dialogue can move the international community from fragmented discussions toward coordinated, sustained, and trust-based cooperation, which is essential for ensuring that AI development remains safe, inclusive, and beneficial for all.

The AI Dialogue should build upon and connect with a range of existing international initiatives and governance efforts to avoid duplication and accelerate progress. Key initiatives include the OECD AI Principles, which provide widely endorsed norms for trustworthy AI; the UNESCO Recommendation on the Ethics of AI, which emphasizes human rights and ethical safeguards; the G7 Hiroshima AI Process, which focuses on advanced AI governance among major economies; and the Global Partnership on AI (GPAI), which facilitates multistakeholder collaboration and research. Additionally, regional regulatory efforts such as the European Union AI Act and national AI strategies across multiple countries offer valuable practical experience in implementation. The AI Dialogue can also connect with technical and standards-setting bodies such as the International Organisation for Standardization (ISO) and the International Telecommunication Union (ITU), which are developing technical benchmarks and interoperability frameworks. The added value of the AI Dialogue lies in its universal and inclusive mandate under the United Nations, bringing together all countries—including those not represented in smaller groupings like the G7 or OECD. This enables broader legitimacy and more equitable participation. Furthermore, the Dialogue can act as a bridging platform between high-level principles and practical implementation by aligning policy discussions with technical standards and real-world use cases. It can also enhance coordination across fragmented initiatives, reducing duplication and promoting coherence. Importantly, the AI Dialogue can provide continuity and political momentum, ensuring that governance efforts remain adaptive and responsive to emerging challenges, while fostering trust and collaboration across diverse stakeholders. In this way, it can transform a fragmented ecosystem into a more coherent, inclusive, and action-oriented global AI governance framework.

Different stakeholders—governments, industry, academia, civil society, and technical experts—can contribute to the AI Dialogue in complementary ways, ensuring that discussions are both inclusive and actionable. Governments can provide regulatory perspectives, share national AI strategies, and contribute to aligning global norms with local priorities. They can also identify sector-specific risks and opportunities, enabling targeted policy development. Industry and private sector actors bring technical expertise, practical insights into AI deployment, and knowledge of operational risks. Their participation can inform realistic standards, safety protocols, and innovation-friendly regulations. Academia and research institutions can contribute evidence-based analysis, risk assessments, and scenario modeling. They can help identify emerging threats, evaluate governance approaches, and propose ethical frameworks grounded in rigorous research. Civil society and non-governmental organizations can advocate for public interests, human rights, and equitable access. They provide oversight, amplify marginalized voices, and ensure that societal implications are fully considered. Technical standardization bodies can offer expertise on interoperability, evaluation benchmarks, and safety protocols, helping bridge the gap between high-level principles and practical implementation. Recommendations for format and structure: 1. Multistakeholder plenaries for principle-setting, high-level policy discussions, and consensus-building. 2. Thematic working groups addressing specific areas such as safety, human rights, interoperability, and frontier AI, allowing deep technical and policy engagement. 3. Iterative consultations and public input mechanisms, including written submissions, workshops, and open forums, to capture diverse perspectives. 4. Knowledge-sharing platforms for research, case studies, and best practices. 5. Regular review cycles to assess progress, update guidelines, and address emerging challenges dynamically. By combining inclusive governance, thematic depth, and iterative engagement, the AI Dialogue can ensure that all stakeholders contribute meaningfully to shaping a globally coherent, safe, and equitable AI ecosystem.

Several voices and communities remain underrepresented in global AI governance discussions, limiting the inclusivity and legitimacy of decision-making. 1. Global South and developing countries: Many low- and middle-income countries lack technical capacity, regulatory experience, and access to frontier AI technologies. Their exclusion risks governance frameworks that reflect the priorities of technologically advanced nations while neglecting local needs and contexts. Inclusion strategies: provide capacity-building programs, technical assistance, and funding support; establish regional hubs to facilitate participation; and ensure equitable representation in working groups and decision-making bodies. 2. Marginalized and vulnerable populations: Women, indigenous communities, people with disabilities, and economically disadvantaged groups often have limited visibility in AI policy discussions. AI systems can exacerbate existing inequalities if their perspectives are not considered. Inclusion strategies: conduct targeted consultations, promote community-led impact assessments, and integrate social equity metrics into AI evaluation and policy design. 3. Civil society organizations and independent ethics bodies: While some NGOs are active in AI governance, many smaller or regionally focused organizations are excluded from high-level discussions. Inclusion strategies: create structured mechanisms for civil society input, such as advisory councils, public hearings, and open submission platforms. 4. Technical experts from diverse domains: Fields like social sciences, linguistics, and human rights are often underrepresented compared to computer science and engineering. Inclusion strategies: establish interdisciplinary working groups to ensure that AI policies are informed by both technical and societal perspectives. 5. Youth and future-focused perspectives: Younger generations and future-oriented think tanks are rarely involved, despite being most affected by AI's long-term impact. Inclusion strategies: include youth representatives, organize innovation labs, and solicit scenario-based policy input to capture forward-looking perspectives. By proactively addressing these gaps, global AI governance can become more equitable, culturally aware, and responsive, ensuring that AI serves the broadest possible range of communities and interests.

To foster meaningful and dynamic engagement during the AI Dialogue, innovative formats should combine interactivity, inclusivity, and evidence-based deliberation, moving beyond traditional plenary sessions. 1. Multistakeholder workshops and scenario labs: Small, interactive sessions where participants collaboratively explore hypothetical AI scenarios—such as autonomous systems in healthcare or frontier AI risks—can help stakeholders identify practical governance challenges and solutions in real time. 2. Thematic hackathons and innovation sprints: Bringing together technologists, policymakers, and civil society to co-design prototypes for AI auditing tools, transparency dashboards, or ethical compliance frameworks encourages hands-on problem-solving and cross-sector collaboration. 3. Digital consultation platforms: Online portals for submitting written inputs, reviewing drafts, and providing feedback allow broad participation from underrepresented regions, youth, and marginalized groups who may not be able to attend in person. Gamified elements or discussion forums can encourage engagement and iterative dialogue. 4. Interactive simulations and role-playing exercises: Participants can assume the roles of regulators, developers, and affected communities to explore trade-offs, decision-making challenges, and systemic risks. This experiential approach helps stakeholders internalize the real-world implications of AI governance choices. 5. Multi-round Delphi or expert polling exercises: Structured, iterative surveys of experts can generate consensus on emerging issues, risk priorities, and policy options, complementing qualitative discussions. 6. Regional and sectoral "deep dives": Parallel sessions focused on specific sectors (e.g., healthcare, finance) or regions (e.g., Africa, Latin America) can identify localized challenges and feed insights into global discussions. 7. Continuous learning and knowledge hubs: Maintaining an open-access repository of research, case studies, and discussion outputs can sustain engagement between Dialogue sessions and encourage evidence-based decision-making. Combining these formats ensures that the AI Dialogue is inclusive, participatory, and solution-oriented, enabling stakeholders to co-create governance frameworks that are both practical and globally relevant.

Several existing policies, practices, and platforms provide effective models for AI governance and offer concrete approaches to addressing its challenges: 1. OECD AI Principles: These widely endorsed guidelines promote trustworthy AI through principles such as transparency, accountability, and human-centric design. They have influenced national AI strategies and provide a common reference point for cross-border alignment. 2. UNESCO Recommendation on the Ethics of AI: This framework emphasizes ethical safeguards, human rights, and inclusivity, offering concrete guidance on embedding values into AI design, deployment, and regulation. 3. European Union AI Act (proposed): The EU AI Act establishes a risk-based regulatory approach, categorizing AI systems by potential harm and mandating conformity assessments, documentation, and post-deployment monitoring. It is a practical example of applying oversight proportionally to risk levels. 4. Global Partnership on AI (GPAI): This multistakeholder initiative enables governments, academia, and industry to collaborate on responsible AI research, share best practices, and develop technical and governance solutions, fostering international cooperation. 5. Algorithmic Impact Assessments (Canada & New York City): These assessments require organizations to evaluate potential risks of AI systems before deployment, ensuring accountability, fairness, and transparency in government and public sector AI applications. 6. Open-source platforms and AI model sharing: Platforms such as Hugging Face or OpenMined encourage transparency and collaboration in AI development. Open-source models allow independent auditing, knowledge sharing, and wider participation in innovation. 7. Red-teaming and stress-testing practices: Applied by organizations like OpenAI and DeepMind, red-teaming evaluates AI models for misuse, bias, or safety failures, providing actionable insights to mitigate risks before deployment. 8. National AI strategies with capacity-building programs: Countries like India, Singapore, and Canada invest in workforce development, research infrastructure, and digital literacy, enabling broader participation in AI governance and fostering local innovation ecosystems. Together, these examples demonstrate that effective AI governance combines ethical frameworks, risk-based regulation, technical safeguards, international collaboration, and capacity-building, providing both normative guidance and practical solutions.