Business Development Fund

A successful Global Dialogue on AI Governance would produce clear, actionable, and inclusive outcomes rather than remaining purely conceptual. First, it should establish a shared set of guiding principles for responsible AI that are globally recognized, yet flexible enough to reflect regional contexts and levels of development. Second, it should result in a roadmap for international cooperation, including mechanisms for knowledge-sharing, regulatory alignment, and capacity-building particularly for developing countries. Another key outcome would be the creation of multi-stakeholder partnerships involving governments, private sector actors, academia, and civil society, ensuring that AI governance is not dominated by a small group of actors. The dialogue should also prioritize practical tools, such as model policies, risk assessment frameworks, and accountability standards that countries can adopt or adapt. Importantly, success would include concrete commitments to bridge the global AI divide by investing in infrastructure, skills development, and equitable access to AI technologies. Finally, the dialogue should define a process for continuity such as regular convenings or a permanent coordination platform to ensure that discussions translate into sustained action and measurable progress

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

These priorities reflect the need to balance innovation with responsibility and inclusivity. Ensuring safe, secure, and trustworthy AI is foundational, as public trust determines the long-term viability and adoption of AI systems. Without robust safeguards, the risks of misuse, bias, and unintended harm increase significantly. AI capacity-building is equally critical, particularly for developing regions that risk being left behind. Strengthening local expertise, infrastructure, and institutional readiness enables more equitable participation in the global AI ecosystem and reduces dependency on external technologies. The protection and promotion of human rights must remain central to AI governance. AI systems increasingly influence decisions that affect fundamental rights, including privacy, freedom of expression, and access to services. Embedding human rights principles helps prevent harm and ensures ethical alignment. Finally, transparency, accountability, and human oversight are essential for operationalizing these values. Clear accountability mechanisms, explainable systems, and meaningful human control help ensure that AI systems remain aligned with societal goals and can be challenged or corrected when necessary

Yes, several important cross-cutting issues deserve greater attention. One is the environmental impact of AI, including the energy consumption of large-scale models and data centers. Sustainable AI development should be integrated into governance discussions. Another emerging issue is data sovereignty and equitable data governance. Many countries lack control over how their data is collected, stored, and used, raising concerns about digital dependency and fairness in value distribution. The concentration of AI power among a small number of corporations and countries is also a critical concern. This raises questions about market competition, geopolitical imbalance, and the inclusivity of global decision-making processes. Additionally, the rapid advancement of generative AI introduces challenges related to misinformation, intellectual property, and the future of work. Governance frameworks must adapt quickly to address these evolving risks. Finally, there is a need to strengthen mechanisms for global accountability and enforcement. While principles and guidelines are important, their effectiveness depends on implementation, monitoring, and consequences for non-compliance.

Governance gaps in AI are already shaping both the risks and opportunities in my country and the broader region. One of the most significant challenges is limited regulatory and institutional capacity to effectively oversee AI systems. While AI adoption is growing in sectors such as healthcare, agriculture, and public services, the absence of clear standards for safety, accountability, and data protection increases the risk of biased outcomes, privacy violations, and misuse. Another key challenge is the global imbalance in AI development. Most advanced AI systems are designed outside the region, often without sufficient consideration of local languages, cultural contexts, or societal needs. This creates risks of exclusion and reinforces digital dependency, while limiting local innovation. At the same time, there are important opportunities. AI has the potential to accelerate development by improving service delivery, enabling data-driven policymaking, and supporting economic growth. For example, AI tools can enhance agricultural productivity, expand access to education, and strengthen healthcare systems. Advances in global AI governance discussions also present an opportunity for greater inclusion. If effectively leveraged, international cooperation can support capacity-building, technology transfer, and the development of context-appropriate regulatory frameworks. This would enable countries in the region to participate more actively in shaping AI governance, rather than only adopting external standards. Overall, addressing governance gaps is essential to ensuring that AI systems are safe, inclusive, and aligned with national development priorities, while also enabling the region to fully benefit from AI-driven innovation.

The AI Dialogue can play a pivotal role as a neutral, inclusive, and action-oriented platform for advancing international cooperation on AI governance. Its primary value lies in bringing together diverse stakeholders governments, private sector actors, academia, and civil society from both developed and developing countries to foster mutual understanding and shared priorities. One key role of the Dialogue is to facilitate convergence around core principles and standards for responsible AI, while allowing flexibility for local adaptation. By encouraging interoperability between national and regional governance frameworks, it can help reduce regulatory fragmentation and support more consistent global practices. The Dialogue can also serve as a hub for knowledge-sharing and capacity-building. Many countries face similar challenges but lack access to technical expertise or policy experience. Structured exchanges, best practice repositories, and technical assistance initiatives can help bridge these gaps. Additionally, the Dialogue can promote trust and transparency between countries, which is essential for collaboration on cross-border issues such as data governance, AI safety, and risk management. It can also support the development of collaborative mechanisms, including joint research initiatives, shared infrastructure, and coordinated responses to emerging risks. Importantly, the AI Dialogue should emphasize inclusivity by ensuring that the perspectives and needs of developing countries are meaningfully represented. By doing so, it can contribute to a more equitable global AI ecosystem and strengthen collective capacity to harness AI for sustainable development.

The AI Dialogue should build upon and connect with a range of existing international initiatives and partnerships to avoid duplication and maximize impact. These include efforts by the United Nations system, particularly UNESCO's work on AI ethics, as well as the OECD AI Principles, which provide widely recognized guidelines for trustworthy AI. Regional initiatives, such as the African Union's digital transformation and AI strategies, are also critical for ensuring context-specific approaches. Multi-stakeholder platforms like the Global Partnership on Artificial Intelligence and standards-setting bodies such as the International Organization for Standardization and International Telecommunication Union offer valuable technical and policy expertise that the Dialogue can leverage. In addition, ongoing regulatory developments in various jurisdictions provide practical lessons that can inform global discussions. The added value of the AI Dialogue lies in its ability to connect these fragmented efforts into a more coherent and inclusive global framework. Unlike many existing initiatives, it has the potential to elevate voices from underrepresented regions and ensure that global governance reflects diverse perspectives and development needs. Furthermore, the Dialogue can act as a bridge between high-level principles and practical implementation by promoting coordination, tracking progress, and encouraging accountability. It can also foster synergies across initiatives, enabling more efficient use of resources and stronger collective impact in addressing the global challenges and opportunities posed by AI.

Different stakeholders can contribute to the AI Dialogue by bringing complementary expertise, experiences, and priorities. Governments can share regulatory approaches and policy needs; the private sector can provide technical knowledge and insights on implementation; academia can contribute research and evidence-based analysis; and civil society can highlight societal impacts, ethical concerns, and community perspectives. To ensure meaningful participation, the AI Dialogue should adopt a multi-layered and inclusive structure. This could include high-level plenary sessions for strategic direction, alongside thematic working groups focused on specific issues such as safety, human rights, and capacity-building. Regional consultations should be integrated into the process to reflect diverse contexts and priorities. The format should also enable continuous engagement rather than one-off discussions. A hybrid model combining in-person and virtual participation would improve accessibility, especially for stakeholders with limited resources. Clear mechanisms for submitting inputs, sharing best practices, and co-developing outputs such as policy recommendations or toolkits are essential. Finally, transparency and feedback loops should be built into the process, ensuring that contributions are reflected in outcomes. This will strengthen trust, accountability, and sustained engagement across all stakeholder groups.

Several voices remain underrepresented in global AI governance discussions, particularly those from developing countries, least developed countries, and small island developing states. These regions often face the greatest risks from AI-related inequalities but have limited influence over global decision-making processes. In addition, marginalized communities including rural populations, women, youth, persons with disabilities, and linguistically diverse groups are frequently excluded. Their lived experiences are critical for understanding how AI systems affect different segments of society, especially in areas such as access to services, employment, and digital inclusion. Small and medium-sized enterprises (SMEs) and local innovators are also underrepresented, despite their important role in shaping grassroots innovation ecosystems. Similarly, frontline public sector practitioners who implement AI systems often lack a platform to share practical challenges and lessons learned. To address these gaps, the AI Dialogue should proactively design inclusive participation mechanisms. This includes providing financial and technical support for participation, offering multilingual engagement formats, and organizing regional and community-level consultations. Partnerships with local organizations can help amplify grassroots voices. Incorporating participatory approaches such as citizen panels or community consultations can also ensure that governance frameworks reflect real-world needs. Ultimately, inclusion must be intentional, structured, and sustained to ensure equitable representation in shaping AI governance.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional conference formats and adopt more interactive and participatory approaches. One effective format is scenario-based workshops, where participants collaboratively explore real-world AI governance challenges and develop practical solutions. Another approach is the use of multi-stakeholder labs or "policy sandboxes," where governments, companies, and researchers can test governance frameworks, tools, or regulatory approaches in a controlled environment. This encourages experimentation and learning-by-doing. Digital engagement platforms can also play a key role. Interactive online portals could allow stakeholders to submit proposals, vote on priorities, and collaborate on documents in real time. This would make participation more continuous and accessible beyond formal sessions. Fireside chats, roundtables, and small group dialogues can create space for deeper, more candid discussions compared to large plenary sessions. Additionally, incorporating youth forums and innovation challenges can bring fresh perspectives and encourage forward-looking solutions. To further enhance inclusivity and creativity, the Dialogue could integrate storytelling, case study showcases, and even cross-disciplinary exchanges involving technologists, policymakers, and social scientists. Overall, a mix of structured and flexible formats supported by digital tools will be essential to ensure that the AI Dialogue is not only informative, but also collaborative, solution oriented, and impactful

Several existing policies and approaches offer valuable lessons for effective and inclusive AI governance. The UNESCO Recommendation on the Ethics of Artificial Intelligence is a notable example, providing a comprehensive, human rights based framework that emphasizes transparency, accountability, and inclusivity. Its global adoption demonstrates the importance of shared principles grounded in ethical values. Similarly, the OECD AI Principles have helped shape national AI strategies by promoting trustworthy AI, including fairness, robustness, and explainability. These principles have influenced policy alignment across multiple countries and regions. At the regulatory level, the European Union's AI Act (developed by the European Union) offers a risk-based approach, categorizing AI systems according to their potential impact and applying proportionate regulatory requirements. This model provides a practical framework for balancing innovation with safeguards. Multi-stakeholder initiatives such as the Global Partnership on Artificial Intelligence demonstrate the value of collaboration between governments, industry, and academia in advancing research and best practices. In addition, regulatory sandboxes and pilot programs implemented in various countries allow for controlled experimentation with AI technologies under regulatory supervision. These approaches support innovation while enabling policymakers to better understand emerging risks. Open-source platforms and data-sharing initiatives also contribute to more transparent and inclusive AI ecosystems, particularly when combined with strong data governance standards. Overall, effective AI governance combines ethical frameworks, risk-based regulation, multi-stakeholder collaboration, and adaptive policy tools that can evolve alongside technological advancements while ensuring that societal values remain central.