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A successful first Global Dialogue on AI Governance would produce outcomes that are both practical and widely accepted across countries with different political, economic, and technological contexts. First, success would mean establishing a shared baseline of principles for AI development and use. These should include transparency, accountability, fairness, safety, and respect for human rights. Even if not legally binding, a clear global consensus on these values would guide future national policies and reduce fragmentation. Second, the dialogue should result in a roadmap for cooperation. This includes mechanisms for information sharing, joint research on AI safety, and coordination in responding to risks such as misuse, bias, or large-scale societal disruption. Creating working groups or permanent forums would ensure the conversation continues beyond a single event. Third, inclusion would be a key indicator of success. The dialogue must meaningfully involve not only major technological powers, but also developing countries, civil society, academia, and the private sector. AI governance cannot be effective if it reflects only a small group of interests. Fourth, tangible commitments would strengthen credibility. These could include agreements on auditing high-risk AI systems, investing in AI capacity building in underserved regions, or aligning certain regulatory approaches.

• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Transparency, accountability, and human oversight

Selected priorities: Safe, secure and trustworthy AI AI capacity-building Social, economic, ethical, cultural, linguistic and technical implications of AI Explanation: Safe, secure, and trustworthy AI is a top priority because the rapid deployment of AI systems without adequate safeguards can lead to harm, including bias, misinformation, and security risks. Ensuring reliability and accountability is essential to building public trust. AI capacity-building is equally urgent, especially for developing countries and underserved communities. Without targeted investment in skills, infrastructure, and education, the global AI landscape will deepen existing inequalities. Empowering youth, entrepreneurs, and institutions with digital and AI skills enables inclusive participation in the AI economy. The social, economic, ethical, cultural, linguistic, and technical implications of AI must also be addressed to ensure that AI systems reflect diverse realities. Many communities, particularly in Africa and other underrepresented regions, risk being excluded due to language barriers and lack of local data. Ethical and culturally sensitive AI is necessary for fairness and relevance.

Yes, several important cross-cutting and emerging issues are not fully captured by the listed themes. One key issue is data governance and ownership. Questions around who owns data, how it is collected, and how communities can benefit from its use are central to fair AI systems. Without clear frameworks, there is a risk of data extraction, where data from developing regions is used without meaningful return or consent. Another emerging concern is AI and labor market transformation. While AI can create opportunities, it also risks displacing jobs, especially for low and middle-skilled workers. Proactive strategies for reskilling, social protection, and youth employment are essential to ensure that AI contributes to inclusive economic growth rather than widening inequality. Access to AI infrastructure is also critical. Beyond skills, many regions lack the computing power, internet connectivity, and financial resources needed to participate in AI development. This creates a structural imbalance in who can innovate and benefit from AI technologies. Additionally, environmental sustainability is an often overlooked issue. AI systems, particularly large scale models, consume significant energy and resources. Governance discussions should include standards and incentives for energy efficient and environmentally responsible AI development.

Governance gaps in AI are already shaping both challenges and opportunities in our region, particularly for youth, entrepreneurs, and underserved communities. A major challenge is the limited regulatory and institutional capacity to guide safe and responsible AI adoption. In the absence of clear standards, there is a risk of deploying AI systems that may reinforce bias, spread misinformation, or operate without accountability. This weakens public trust and can lead to misuse. Another key challenge is the digital and AI skills gap. Many young people and small businesses lack access to training, tools, and infrastructure needed to engage with AI. This limits their ability to compete, innovate, or benefit from emerging opportunities in the digital economy. Infrastructure constraints such as limited access to reliable internet, computing resources, and funding also hinder meaningful participation in AI development. As a result, most AI solutions are imported and not adapted to local realities, including language and cultural context. However, these gaps also present important opportunities. With the right investments, there is strong potential to build local capacity, empower youth, and support entrepreneurship through AI-driven innovation. Tailored training programs and digital inclusion initiatives can help bridge inequalities and unlock talent. There is also an opportunity to develop context-specific governance frameworks that reflect local values, languages, and socio economic realities. This can position the region as a contributor not just a consumer of ethical and inclusive AI.

The AI Dialogue can play a pivotal role in advancing international cooperation by serving as a neutral and inclusive platform where governments, private sector actors, academia, and civil society align on shared priorities and collective action. First, it can help build global consensus around core principles for AI governance, such as safety, transparency, accountability, and respect for human rights. Establishing a common foundation reduces fragmentation and supports more coherent policy development across countries. Second, the Dialogue can facilitate practical cooperation mechanisms, including knowledge sharing, joint research initiatives, and coordinated responses to emerging risks. This is particularly important in addressing cross-border challenges such as misinformation, cybersecurity threats, and the misuse of AI technologies. Third, it can strengthen inclusion and equity by ensuring that developing countries and underrepresented communities have a voice in shaping global AI governance. This helps prevent a governance landscape dominated by a few actors and promotes more balanced and context-sensitive outcomes. Additionally, the Dialogue can support capacity-building efforts, connecting countries with technical expertise, funding opportunities, and partnerships needed to develop local AI ecosystems and governance frameworks.

The AI Dialogue should build upon and connect with a range of existing global and regional initiatives to avoid duplication and accelerate progress. Key initiatives include the United Nations Educational, Scientific and Cultural Organization Recommendation on the Ethics of AI, which provides a comprehensive normative framework; the Organisation for Economic Co operation and Development AI Principles, which guide responsible AI development; and the Global Partnership on Artificial Intelligence, which promotes international collaboration on applied AI research and governance. The International Telecommunication Union also plays a key role through its work on AI standards and global dialogue platforms. In addition, regional regulatory efforts such as the European Union AI Act offer practical models for risk-based governance approaches. The AI Dialogue can add value by acting as a bridge across these fragmented efforts, creating stronger alignment between normative frameworks, technical standards, and policy implementation. Unlike existing initiatives that may be limited by geography or membership, the Dialogue can provide a more inclusive, globally representative platform, particularly amplifying the voices of developing countries and underserved communities. It can also enhance coordination and coherence by identifying common priorities, reducing regulatory divergence, and promoting interoperability between different governance approaches. Furthermore, the Dialogue can translate high level principles into actionable commitments, including capacity-building programs, funding mechanisms, and shared tools for AI auditing and risk assessment.

Different stakeholders can contribute to the AI Dialogue by bringing their unique expertise, perspectives, and resources into a collaborative and action-oriented process. Governments should provide policy leadership, share regulatory experiences, and commit to aligning national frameworks with agreed global principles. Private sector actors can contribute technical expertise, innovation, and best practices, while committing to responsible AI development and transparency. Academia and research institutions should offer evidence based insights, risk assessments, and independent evaluations of AI systems. Civil society organizations play a critical role in representing public interests, advocating for human rights, and ensuring accountability and inclusiveness. Youth and local innovators, especially from developing regions, should be actively engaged to bring fresh perspectives and ensure that AI governance reflects diverse realities. In terms of format and structure, the AI Dialogue should be: Multi-stakeholder and inclusive : Ensure balanced participation across regions and sectors, with dedicated space for underrepresented groups. Thematic and action oriented: Organize discussions around key priority areas, each leading to clear outputs such as recommendations, guidelines, or commitments. Hybrid and accessible: Combine in-person and virtual participation to maximize global engagement and reduce barriers to entry. Structured around working groups: Establish ongoing working groups or task forces that continue collaboration between sessions and track progress. Outcome driven: Include measurable goals, timelines, and follow-up mechanisms to ensure accountability and implementation.

Several voices remain underrepresented in global AI governance discussions, limiting the inclusiveness and effectiveness of resulting policies. Communities from developing countries, particularly in Africa, parts of Asia, and Latin America, are often excluded due to limited resources, connectivity, and institutional capacity. Their absence risks creating frameworks that do not reflect local realities. Youth and grassroots innovators are also underrepresented, despite being key drivers of digital adoption and future AI use. Their perspectives are essential for shaping forward looking and practical solutions. Local communities and marginalized groups, including rural populations, women, and linguistic minorities, are frequently overlooked. This leads to AI systems that fail to address cultural diversity, local languages, and social inequalities. Civil society organizations especially smaller, community-based groups often lack the funding and access needed to participate meaningfully in global forums, even though they play a crucial role in advocating for human rights and accountability. To improve inclusion, the AI Dialogue should adopt several measures. First, provide financial and logistical support(such as travel grants and connectivity access) to enable participation from underrepresented regions. Second, ensure multilingual engagement, including translation and support for local languages. Third, create dedicated participation channels for youth, grassroots innovators, and civil society, such as forums, fellowships, or advisory panels. Additionally, leveraging hybrid and decentralized formats including regional consultations can bring discussions closer to local contexts. Finally, ensuring that contributions from these groups are not only heard but integrated into decision making is essential for building truly inclusive and effective AI governance.

Several existing policies, practices, and platforms offer concrete examples of effective AI governance and can inform future efforts. The United Nations Educational, Scientific and Cultural Organization Recommendation on the Ethics of AI provides a comprehensive global framework grounded in human rights, inclusivity, and accountability. It supports countries in translating ethical principles into national policies. The European Union AI Act is a leading example of a risk based regulatory approach, classifying AI systems by levels of risk and imposing corresponding obligations. This model offers practical guidance for balancing innovation with safety. The Organisation for Economic Cooperation and Development AI Principles promote responsible stewardship of trustworthy AI and have been widely adopted, helping align international policy approaches. Multi-stakeholder platforms such as the Global Partnership on Artificial Intelligence facilitate collaboration between governments, industry, and researchers, generating applied insights and best practices. In terms of practical tools, algorithmic impact assessments (AIAs) are increasingly used to evaluate risks before deploying AI systems, particularly in public sector applications. Similarly, AI auditing and transparency mechanisms, including model documentation and explainability standards, help ensure accountability. Capacity building initiatives, including digital skills training and support for local innovation ecosystems, are also critical. These approaches empower communities especially in developing regions to actively participate in AI development and governance.