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A successful outcome for the Global Dialogue on AI Governance would be the establishment of practical, inclusive, and actionable frameworks for AI governance that reflect the realities of both developed and emerging ecosystems. The dialogue should move beyond high-level discussions to produce clear recommendations that governments, industry, and communities can adopt. Key outcomes should include: 1. A shared baseline for safe, secure, and trustworthy AI systems 2. Commitments to equitable AI capacity-building, particularly for underrepresented regions 3. Mechanisms to support open collaboration, including open-source AI and shared infrastructure 4. Clear pathways for multi-stakeholder participation, ensuring voices from the Global South are actively included 5. It would also be valuable to define how accountability and transparency can be operationalized in real-world systems, not just as principles. Ultimately, success would mean creating a foundation for sustained global cooperation, where AI governance is not fragmented but evolves through inclusive dialogue, shared responsibility, and practical implementation.

• Safe, secure and trustworthy AI
• Transparency, accountability, and human oversight
• Open-source software, open data and open AI models
• AI capacity-building

These priorities reflect the need to balance innovation with responsibility while ensuring global inclusivity. Safe, secure, and trustworthy AI is essential, particularly as AI systems are increasingly integrated into critical infrastructure and high-impact sectors. Without strong security and reliability, risks scale quickly. AI capacity-building is critical for ensuring that emerging economies are not left behind. Many regions lack the resources and infrastructure needed to meaningfully participate in AI development and governance, which can increase global inequalities. Transparency, accountability, and human oversight are necessary to build trust and ensure that AI systems can be audited, understood, and governed effectively. This is especially important in high-stakes environments such as finance, healthcare, and public services. Finally, open-source software, open data, and open AI models play a key role in democratizing access to AI. Open ecosystems enable collaboration, innovation, and knowledge sharing, while also allowing more stakeholders to scrutinize and improve systems. Together, these areas support a more inclusive, secure, and collaborative approach to AI governance.

One important cross-cutting issue is the growing divide in access to AI infrastructure, including compute resources, datasets, and skilled talent. Without intentional intervention, this gap risks concentrating AI development and governance power within a small number of regions and organizations. Another emerging issue is the intersection of AI with cybersecurity. As AI systems become more embedded in critical systems, they introduce new attack surfaces and risks, including adversarial attacks, model manipulation, and data poisoning. Governance frameworks must account for these evolving threats. There is also a need to address the sustainability of AI systems, particularly the environmental and economic costs associated with large-scale model training and deployment. This is especially relevant for regions with limited resources. Finally, the role of communities and grassroots ecosystems in shaping AI governance is often overlooked. Local communities play a critical role in contextualizing AI systems, identifying risks, and driving adoption. Ensuring their inclusion in governance processes is essential for building systems that are both effective and equitable.

Governance gaps in AI are already shaping outcomes in Ghana and across the African tech ecosystem, particularly in fintech, cybersecurity, and emerging data-driven services. One major challenge is the lack of clear, localized regulatory frameworks for AI systems. While global standards exist, they are often not adapted to local contexts, creating uncertainty for organizations building and deploying AI solutions. This is especially critical in high-impact sectors such as digital payments, where issues of fraud detection, model reliability, and user trust are central. Another key gap is limited access to AI infrastructure, quality datasets, and specialized talent. This restricts the ability of local developers and organizations to build, test, and scale AI systems effectively, and risks increasing dependence on external technologies that may not reflect local needs or realities. From a cybersecurity perspective, the growing use of AI introduces new risks, including adversarial attacks and automated threat scaling, while governance frameworks for securing AI systems remain underdeveloped. However, these challenges also present opportunities. There is strong potential to build inclusive, context-aware AI systems that address local problems, particularly in finance, agriculture, and public services. Open-source ecosystems are playing a critical role in enabling access, collaboration, and innovation despite resource constraints. Additionally, the region has an opportunity to proactively shape AI governance by embedding principles of transparency, accountability, and inclusivity early in the adoption process. With the right investments in capacity building and infrastructure, Africa can move from being a consumer of AI technologies to an active contributor to global AI development and governance.

The AI Dialogue can play a critical role as a neutral, inclusive platform that brings together governments, industry, academia, and civil society to align on practical approaches to AI governance. Its value lies in bridging gaps between regions with differing levels of capacity, regulatory maturity, and access to AI infrastructure. It can advance cooperation by facilitating knowledge-sharing on what works in practice, not just in theory, and by promoting interoperability between national and regional governance frameworks. This is particularly important to reduce fragmentation and ensure that organizations operating across borders are not subject to conflicting requirements. The dialogue can also help elevate perspectives from underrepresented regions, ensuring that global governance approaches are not shaped solely by a small number of countries. By supporting capacity-building and encouraging participation from emerging ecosystems, it can contribute to more equitable and inclusive governance outcomes. Additionally, it can play a coordinating role by connecting existing initiatives, identifying gaps, and encouraging collaborative approaches to shared challenges such as AI safety, security, and accountability. Ultimately, the AI Dialogue can serve as a foundation for sustained international cooperation, where governance evolves through continuous engagement, shared learning, and collective responsibility.

The AI Dialogue should build on and connect with existing global and regional initiatives such as the OECD AI Principles, UNESCO's Recommendation on the Ethics of Artificial Intelligence, and ongoing work within regional bodies and standards organizations. It should also engage with open-source communities and industry-led initiatives that are actively shaping how AI systems are built and deployed in practice. In addition, it would benefit from connecting with cybersecurity and digital infrastructure efforts, as AI governance increasingly overlaps with issues of system security, resilience, and trust. The added value of the AI Dialogue lies in its ability to bring these fragmented efforts into a more coherent, inclusive framework. Rather than duplicating existing work, it can act as a coordination layer that identifies synergies, aligns priorities, and promotes practical implementation across different contexts. A key contribution would be translating high-level principles into actionable guidance that is accessible to countries and organizations with varying levels of capacity. It can also provide a platform for sharing real-world case studies, lessons learned, and implementation challenges. By integrating perspectives from both policy and practice, including those from emerging ecosystems, the AI Dialogue can help ensure that global AI governance is both effective and equitable.

Different stakeholders should be engaged through clearly defined, accessible participation pathways that reflect their expertise and context. Governments can contribute by sharing policy approaches, regulatory experiences, and national priorities. Industry stakeholders can provide insights into real-world system design, deployment challenges, and risk management practices. Academia can contribute research, evaluation methods, and long-term perspectives on societal impact. Civil society and community organizations can surface lived experiences, highlight risks, and ensure accountability. To support this, the AI Dialogue should adopt a multi-layered structure: High-level plenaries for strategic alignment and global priorities Thematic working groups focused on specific issues such as safety, capacity-building, and open ecosystems Regional sessions to capture context-specific challenges and solutions Open submission channels for written inputs and case studies Participation should not be limited to in-person attendance. Hybrid formats, asynchronous contributions, and accessible documentation are essential to ensure global inclusion.

Voices from the Global South, particularly from Africa, Latin America, and parts of Asia, remain underrepresented in global AI governance discussions. This includes not only policymakers, but also grassroots technologists, open source contributors, community organizers, and small-scale innovators who are actively building and deploying solutions in resource-constrained environments. Additionally, non-technical stakeholders, such as educators, local businesses, and civil society groups, are often excluded despite being directly impacted by AI systems. Linguistic diversity is also a barrier, as many discussions are conducted primarily in a limited number of global languages. To improve inclusion, the AI Dialogue should prioritize regional representation through targeted outreach, travel support, and partnerships with local tech communities. It should also enable remote participation and accept contributions in multiple languages. Engaging open source communities is particularly important, as they play a key role in shaping accessible and transparent AI systems. Creating pathways for these contributors to share practical insights will strengthen the overall quality and relevance of the Dialogue.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and incorporate more interactive and practice-oriented formats. Workshops and collaborative labs can allow participants to co-design solutions, governance models, or policy recommendations in real time. Scenario-based exercises and simulations can help stakeholders explore the real-world implications of governance decisions, particularly in areas such as AI safety, cybersecurity, and ethics. Hackathon-style sessions or innovation challenges can bring together technical and non-technical participants to develop practical, open solutions to shared problems. These formats encourage collaboration and produce tangible outputs. Lightning talks and case study showcases can provide diverse, experience-driven insights in a concise format, ensuring that more voices are heard. In addition, structured roundtables with smaller groups can enable deeper, more focused discussions. Finally, digital collaboration platforms should be used to support ongoing engagement before and after the event, allowing participants to contribute asynchronously, share resources, and continue discussions beyond the formal sessions.

Effective AI governance is emerging through a combination of policy frameworks, technical practices, and collaborative platforms. At the policy level, the EU AI Act provides a risk-based approach that categorizes AI systems based on potential harm and applies proportionate obligations. Similarly, the OECD AI Principles and the UNESCO Recommendation on the Ethics of Artificial Intelligence establish widely adopted standards for transparency, accountability, and human-centered AI. From a technical and operational perspective, practices such as model documentation and auditing are critical. Frameworks like Model Cards and Datasheets for Datasets improve transparency by clearly outlining model limitations, intended use, and dataset characteristics. In cloud environments, governance is strengthened through monitoring and logging tools that support traceability and incident response. Open-source platforms also play a key role in enabling accountable and collaborative AI development. Communities around tools such as TensorFlow and PyTorch, and platforms like Hugging Face, encourage peer review, reproducibility, and shared responsibility. Open access allows a wider range of stakeholders to audit and improve systems. Additionally, organizational practices such as internal AI governance boards, secure development lifecycles, and red-teaming exercises are becoming more common, particularly in high-risk sectors. Together, these approaches demonstrate that effective AI governance requires alignment between policy, technical implementation, and open collaboration, ensuring that systems are not only innovative but also safe, transparent, and accountable.