Federal university of Technology Akure

A successful first Global Dialogue on AI Governance would be defined by outcomes that are practical, inclusive, and capable of influencing real policies and implementation across countries with varying levels of technological advancement. To begin with, it should result in a common set of guiding principles for responsible AI, addressing issues such as transparency, accountability, fairness, data privacy, and human control. These principles should go beyond theory by including clear strategies for implementation in different national contexts. In addition, success would involve creating a structure for global collaboration, with systems for sharing knowledge, aligning regulations, and encouraging cross-border partnerships. This would help avoid fragmented governance approaches that could slow innovation or create ethical gaps. Another key outcome would be the active inclusion of developing countries, particularly in regions like Africa, ensuring they play a meaningful role in shaping AI governance. This should be supported by commitments to capacity building, funding, and technology transfer to prevent widening global inequalities. Furthermore, the dialogue should produce concrete outputs, such as a roadmap or action plan with defined timelines, pilot projects, and accountability measures to ensure follow-through. Engaging a broad range of stakeholders—including governments, academia, the private sector, and civil society—would also be essential to ensure well-rounded and practical policies. Lastly, establishing a standing platform or working group to maintain momentum, track progress, and respond to emerging challenges would be a strong indicator of success. Overall, the dialogue should lead to actionable, inclusive, and globally relevant governance frameworks that promote trust and responsible AI development.

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

These priorities reflect the most urgent areas needed to ensure that AI development is both responsible and inclusive, particularly for developing countries. First, safe, secure and trustworthy AI is fundamental to building public confidence and minimizing risks associated with misuse, bias, or unintended consequences. Without trust, AI adoption will remain limited and potentially harmful. Second, AI capacity-building is critical, especially in regions like Africa where gaps in technical expertise, infrastructure, and funding persist. Strengthening local capacity ensures that countries can actively participate in AI development, governance, and deployment rather than remaining passive consumers. Third, transparency, accountability, and human oversight are essential to ensure that AI systems operate ethically and can be scrutinized. Clear accountability mechanisms help prevent abuse and ensure that human values remain central in decision-making processes. Finally, the social, economic, ethical, cultural, linguistic, and technical implications of AI must be addressed to avoid widening inequalities. AI systems often reflect the biases of their training data, which can marginalize underrepresented communities. Considering these broader impacts ensures that AI solutions are inclusive, culturally sensitive, and beneficial across diverse populations. Together, these priorities support a balanced approach that promotes innovation while safeguarding human welfare and equity.

Yes, several important cross-cutting and emerging issues deserve greater attention beyond the listed themes. One key issue is the digital divide and infrastructure inequality, which significantly affects the ability of developing countries to adopt and govern AI effectively. Without reliable internet access, data systems, and computing infrastructure, many regions risk being excluded from AI benefits. Another critical area is data sovereignty and ownership, particularly for developing nations. There is a growing concern that data generated in these regions is extracted and controlled by external entities, limiting local value creation and governance control. Environmental sustainability is also an emerging concern, as AI systems, especially large-scale models, requires significant energy and computational resources. Governance frameworks should address the environmental footprint of AI to ensure alignment with global sustainability goals. Additionally, the localization of AI systems, including support for indigenous languages and cultural contexts, remains underrepresented. Without this, AI risks reinforcing global inequalities and cultural erosion. Finally, the integration of AI into public sector governance, such as urban planning, housing, and service delivery, requires more focused attention to ensure transparency, efficiency, and accountability in real-world applications. Addressing these cross-cutting issues will help create a more equitable, sustainable, and globally inclusive AI governance ecosystem.

Governance gaps in the selected thematic areas, safe and trustworthy AI, capacity building, transparency and accountability, and the broader societal implications of AI are already shaping both challenges and opportunities in my country and region. A major challenge is the limited regulatory and institutional framework for AI. The absence of clear standards for safety, accountability, and oversight creates risks of misuse, bias, and unregulated deployment, particularly in sensitive sectors such as housing, urban planning, and public service delivery. This weak governance environment can undermine public trust and slow adoption. Another critical gap is insufficient technical capacity and infrastructure. Many institutions lack the expertise, data systems, and digital infrastructure required to develop, deploy, or effectively regulate AI technologies. This places the region at risk of becoming dependent on external technologies that may not reflect local realities or priorities. There are also ethical and socio-cultural challenges, including algorithmic bias, exclusion of local languages, and limited consideration of cultural contexts. Without deliberate intervention, AI systems may reinforce existing inequalities, particularly for vulnerable and underrepresented populations. However, these gaps also present important opportunities. There is significant potential to leapfrog traditional development pathways by integrating AI into sectors such as urban planning, agriculture, and public administration. With the right investments in capacity-building and governance frameworks, AI can improve efficiency, decision-making, and service delivery. Furthermore, the current stage offers an opportunity to co-design context-specific governance models that reflect local needs, values, and development priorities, rather than adopting unsuitable external frameworks. Overall, addressing these governance gaps can transform AI into a tool for inclusive growth, innovation, and sustainable development in the region.

The AI Dialogue can serve as a neutral, inclusive global platform that brings together governments, private sector actors, academia, and civil society to align priorities and coordinate action on AI governance. Its primary role should be to bridge the gap between fragmented national and regional approaches by promoting shared understanding and convergence of standards. It can facilitate policy harmonization by encouraging interoperability between different regulatory frameworks, reducing conflicts, and enabling smoother cross-border innovation and data flows. This is particularly important in preventing regulatory fragmentation that could disadvantage developing countries. The Dialogue can also act as a knowledge-sharing hub, enabling countries to exchange best practices, lessons learned, and technical expertise. For developing regions, this provides access to global insights and strengthens their ability to design context-appropriate governance systems. Another key role is to promote equitable participation, ensuring that underrepresented regions, especially in the Global South have a voice in shaping global AI norms. This helps to avoid governance models that are overly influenced by a few advanced economies. In addition, the AI Dialogue can drive capacity-building partnerships, mobilizing resources, funding, and technical support for countries with limited capabilities. It can also support collaborative research and pilot initiatives that demonstrate practical governance solutions. Finally, by establishing monitoring and follow-up mechanisms, the Dialogue can ensure accountability and continuity, transforming discussions into measurable progress. Overall, the AI Dialogue can act as a catalyst for coordinated, inclusive, and action-oriented global cooperation on AI governance.

The AI Dialogue should build upon and connect with existing global and regional initiatives to avoid duplication and strengthen collective impact. Key frameworks include international principles on AI ethics, regional data protection regulations, and multi-stakeholder partnerships that focus on responsible AI development. For instance, global efforts on AI ethics and governance principles have already established foundational norms around fairness, accountability, and transparency. Similarly, regional regulatory frameworks on data protection and digital governance provide practical models for implementation. Multi-stakeholder initiatives involving governments, technology companies, and research institutions also offer valuable experience in collaborative governance. The Dialogue can add value by serving as a coordinating platform that brings these fragmented efforts into a more coherent global ecosystem. Rather than replacing existing initiatives, it can enhance interoperability and alignment, ensuring that different frameworks can work together effectively. Another key contribution is the inclusion of underrepresented voices, particularly from developing countries, which are often not fully integrated into existing governance structures. The Dialogue can amplify these perspectives and ensure that global AI governance reflects diverse realities. It can also provide a structured mechanism for implementation, translating broad principles into actionable strategies, timelines, and measurable outcomes. By linking policy discussions with real-world applications, the Dialogue can bridge the gap between theory and practice. Ultimately, its added value lies in fostering coordination, inclusivity, and practical action, helping to create a more balanced and effective global AI governance landscape.

Different stakeholders should play clearly defined and complementary roles. Governments can contribute by sharing policy experiences and aligning regulatory priorities. The private sector can provide technical expertise, innovation insights, and real-world deployment perspectives. Academia can offer research-based evidence and independent analysis, while civil society can represent public interest, ethical concerns, and community impacts. To ensure effectiveness, the AI Dialogue should adopt a multi-layered and inclusive structure. This could include high-level plenary sessions for strategic direction, thematic working groups for in-depth discussions, and regional consultations to capture context-specific issues. Each thematic area should have co-chairs from different regions and sectors to ensure balance. The process should be continuous rather than one-off, with clear timelines, deliverables, and follow-up mechanisms. Pre-dialogue consultations and submission of written inputs can help shape discussions, while post-dialogue reporting should outline actionable recommendations. In addition, the Dialogue should integrate hybrid participation formats (physical and virtual) to enable broader global access, especially for participants from developing regions. Transparent documentation and open access to outcomes will also enhance accountability and trust. Overall, a structured, inclusive, and action-oriented approach will ensure meaningful contributions from all stakeholders and sustained impact.

Several important voices remain underrepresented in global AI governance discussions. These include stakeholders from developing countries, particularly in Africa and parts of Asia and Latin America, where participation is often limited by resource and access constraints. Other underrepresented groups include local communities, indigenous populations, women, youth, and non-technical professionals who are directly affected by AI systems but rarely involved in decision-making processes. Additionally, small and medium-sized enterprises (SMEs) and public sector practitioners in areas like urban planning, education, and healthcare are often excluded despite being key implementers of AI solutions. To address this, deliberate efforts are needed to lower participation barriers. This includes providing financial support, travel grants, and digital access for participants from low-resource settings. The use of regional consultations and local forums can help gather inputs that reflect diverse contexts. Language accessibility is also critical in offering multilingual platforms and translation services ensures broader inclusion. Capacity-building initiatives, such as training and preparatory workshops, can empower underrepresented groups to engage effectively. Furthermore, governance structures should ensure equitable representation, such as quotas or targeted inclusion mechanisms in panels and working groups. By intentionally broadening participation, the AI Dialogue can better reflect global diversity and produce more inclusive and legitimate governance outcomes.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 simulations, where participants collaboratively explore real-world AI governance challenges such as data misuse, algorithmic bias, or cross-border regulation and develop practical solutions. This encourages problem-solving and deeper understanding. Another approach is multi-stakeholder labs or co-creation workshops, where diverse participants work together to design policy frameworks, tools, or pilot initiatives. These sessions can produce tangible outputs rather than just discussions. Digital engagement platforms can also enhance participation, allowing for live polling, Q&A sessions, and collaborative document drafting in real time. This ensures that even virtual participants can actively contribute. In addition, regional dialogue hubs connected to the global event can enable localized discussions while feeding into the broader process. This helps balance global coordination with local relevance. Storytelling and case study showcases can bring real-world experiences into the dialogue, highlighting both successes and challenges in AI governance across different contexts. Finally, incorporating youth forums and innovation challenges can engage younger generations and surface fresh ideas. By combining interactive, inclusive, and outcome-oriented formats, the AI Dialogue can create a more engaging and impactful experience for all participants.

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 simulations, where participants collaboratively explore real-world AI governance challenges—such as data misuse, algorithmic bias, or cross-border regulation—and develop practical solutions. This encourages problem-solving and deeper understanding. Another approach is multi-stakeholder labs or co-creation workshops, where diverse participants work together to design policy frameworks, tools, or pilot initiatives. These sessions can produce tangible outputs rather than just discussions. Digital engagement platforms can also enhance participation, allowing for live polling, Q&A sessions, and collaborative document drafting in real time. This ensures that even virtual participants can actively contribute. In addition, regional dialogue hubs connected to the global event can enable localized discussions while feeding into the broader process. This helps balance global coordination with local relevance. Storytelling and case study showcases can bring real-world experiences into the dialogue, highlighting both successes and challenges in AI governance across different contexts. Finally, incorporating youth forums and innovation challenges can engage younger generations and surface fresh ideas. By combining interactive, inclusive, and outcome-oriented formats, the AI Dialogue can create a more engaging and impactful experience for all participants.

Effective AI governance is already being advanced through a range of policies, frameworks, and collaborative approaches that offer practical solutions. One example is the risk-based regulatory approach, where AI systems are classified according to their potential impact. This allows stricter oversight for high-risk applications (such as in healthcare or public services) while supporting innovation in lower-risk areas. Such models provide a balanced pathway between regulation and technological growth. Another strong practice is the development of ethical AI guidelines and principles, which emphasize fairness, accountability, transparency, and human oversight. When backed by enforcement mechanisms such as audits and impact assessments these principles become actionable tools for governance rather than abstract ideals. Algorithmic impact assessments (AIAs) are also gaining traction as a practical governance tool. They require organizations to evaluate potential risks, biases, and societal impacts of AI systems before deployment, promoting responsible design and accountability. In addition, multi-stakeholder governance platforms have proven effective in bringing together governments, private sector actors, academia, and civil society to co-develop policies and share best practices. These platforms enhance collaboration and reduce fragmentation. Open approaches, such as open data and transparent AI models, can also strengthen accountability and innovation, provided that privacy and security safeguards are in place. They enable broader participation, independent scrutiny, and localized adaptation. Capacity-building initiatives, including training programs and institutional strengthening, are equally important, especially in developing countries. They empower local actors to design, implement, and regulate AI systems effectively. Finally, public sector pilot projects for example in urban planning or service delivery demonstrate how AI can be applied responsibly in real-world contexts, providing lessons for scaling and governance. Together, these examples highlight practical, scalable approaches to achieving inclusive and effective AI governance.