Global Alliance for Artificial Intelligence

A successful first Global Dialogue on AI Governance would produce concrete, actionable, and inclusive outcomes rather than purely declarative statements. First, it should establish a shared baseline of principles for AI governance that are globally relevant yet adaptable to regional contexts, especially for developing countries. Second, the Dialogue should lead to the creation of a multi-stakeholder coordination mechanism involving governments, private sector actors, academia, and civil society to ensure continuous collaboration beyond the event. Third, it should generate clear commitments for capacity-building, particularly for countries in the Global South, to bridge gaps in infrastructure, skills, and regulatory readiness. Without this, global AI governance risks deepening inequalities. Fourth, success would include practical guidance on risk management, including frameworks for safety, accountability, and human oversight that can be implemented at national and organizational levels. Finally, the Dialogue should result in a roadmap with measurable milestones, ensuring follow-up, monitoring, and accountability. In essence, success lies in moving from dialogue to implementation, ensuring that AI governance becomes inclusive, equitable, and effective at a global scale.

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

These priorities reflect the need to balance innovation with responsibility and inclusion. "Safe, secure and trustworthy AI" is fundamental to building public trust and ensuring that AI systems do not cause harm, whether through bias, misuse, or unintended consequences. "AI capacity-building" is critical, particularly for developing regions, to avoid widening the global digital divide. Many countries lack the infrastructure, expertise, and institutional readiness to fully participate in or regulate AI systems. "Protection and promotion of human rights" ensures that AI development aligns with universal values such as dignity, privacy, and non-discrimination. Without this foundation, AI risks reinforcing systemic inequalities. "Transparency, accountability, and human oversight" are essential to governance. AI systems must be explainable and subject to human control, with clear mechanisms for responsibility when harm occurs. Together, these priorities create a balanced framework that promotes innovation while safeguarding societies and ensuring equitable global participation.

Yes, several important cross-cutting and emerging issues deserve greater attention. First, AI sovereignty and data ownership are increasingly critical. Countries are concerned about who controls data, infrastructure, and AI systems, especially in contexts where technological dependence may create geopolitical imbalances. Second, the environmental impact of AI is an emerging concern. The energy consumption of large-scale AI models raises sustainability challenges that should be integrated into governance discussions. Third, access to compute resources is becoming a defining factor in global AI inequality. Beyond skills and data, limited access to high-performance computing restricts meaningful participation for many countries. Fourth, the rise of AI in fragile and conflict-affected settings presents unique risks, including misinformation, surveillance misuse, and destabilization, which require tailored governance approaches. Finally, cultural and linguistic diversity in AI systems remains under-addressed. Many AI models underrepresent non-dominant languages and cultural contexts, leading to exclusion and bias. Addressing these issues is essential to ensure that AI governance is not only effective but also equitable, sustainable, and globally representative.

Governance gaps in the selected thematic areas are already shaping both the risks and opportunities of AI adoption in my country and across the Central African region. A major challenge lies in limited regulatory frameworks for safe and trustworthy AI. In the absence of clear standards, organizations often adopt AI tools without sufficient risk assessment, exposing institutions to issues such as biased decision-making, data misuse, and cybersecurity vulnerabilities. This is compounded by weak enforcement capacity and limited technical expertise among regulators. Capacity constraints remain one of the most significant barriers. There is a shortage of skilled professionals, inadequate digital infrastructure, and limited access to advanced computing resources. As a result, many countries in the region are primarily consumers of AI technologies rather than active contributors, increasing dependency on external providers. From a human rights perspective, gaps in data protection laws and oversight mechanisms raise concerns about privacy, surveillance, and potential misuse of AI in sensitive sectors such as public administration and security. However, these gaps also present important opportunities. AI can accelerate financial inclusion, healthcare delivery, education, and public service efficiency, particularly in underserved communities. With the right governance frameworks, the region can leapfrog traditional development pathways. There is also a strong opportunity to build context-specific AI solutions, especially in local languages and informal economies, which are often overlooked by global systems. Ultimately, addressing governance gaps through targeted capacity-building, regional cooperation, and inclusive policy design can transform AI from a source of risk into a powerful driver of sustainable and equitable development.

The AI Dialogue can serve as a global coordination platform that bridges fragmented efforts and fosters inclusive, multilateral cooperation on AI governance. Its primary role should be to align diverse national and regional approaches, reducing regulatory fragmentation while respecting different development contexts and priorities. First, the Dialogue can facilitate consensus-building on core principles and standards, enabling interoperability between governance frameworks across jurisdictions. This is essential for managing cross-border AI risks and ensuring that innovation can scale responsibly. Second, it can act as a convening space for multi-stakeholder engagement, bringing together governments, private sector actors, academia, and civil society—especially voices from underrepresented regions such as Africa and the Global South. Third, the Dialogue can promote knowledge-sharing and best practices, helping countries learn from existing policies, regulatory sandboxes, and technical standards. This reduces duplication of efforts and accelerates policy maturity. Fourth, it can catalyze international cooperation on capacity-building, mobilizing technical assistance, funding, and partnerships to support countries with limited resources. Finally, the Dialogue can contribute to trust-building between nations, which is critical in a context where AI is increasingly linked to economic competitiveness and geopolitical dynamics. In essence, the AI Dialogue can move global governance from fragmented initiatives toward a more coherent, inclusive, and action-oriented ecosystem.

The AI Dialogue should build upon and connect with existing global and regional initiatives to avoid duplication and enhance coherence. Key frameworks include the OECD AI Principles, UNESCO's Recommendation on the Ethics of AI, and the work of the Global Partnership on Artificial Intelligence, which provide valuable foundations for responsible AI governance. It should also engage with regional efforts such as the African Union's digital transformation strategy, as well as emerging regulatory models like the European Union AI Act. In addition, collaboration with standard-setting bodies like International Organisation for Standardization and International Telecommunication Union is essential to ensure technical alignment. The added value of the AI Dialogue lies in its ability to act as a neutral, inclusive platform that connects these initiatives, fostering interoperability rather than competition among frameworks. It can elevate perspectives from developing countries that are often underrepresented in existing forums and ensure that global standards reflect diverse realities. Moreover, the Dialogue can translate high-level principles into practical implementation pathways, including capacity-building programs, shared tools, and policy guidance. It can also promote coordination across silos, linking ethical, technical, economic, and human rights dimensions of AI governance. Ultimately, its unique contribution is to create coherence, inclusivity, and actionable alignment across the global AI governance landscape.

Different stakeholders can contribute by leveraging their unique roles and expertise. Governments can provide regulatory perspectives and national priorities; the private sector can share technical knowledge and real-world implementation insights; academia can contribute research and evidence-based analysis; and civil society can ensure that ethical, social, and human rights considerations are fully represented. To maximize impact, the AI Dialogue should adopt a multi-layered and inclusive structure. This could include: High-level plenary sessions to define strategic directions and political commitments; Thematic working groups focused on key areas such as safety, human rights, and capacity-building; Regional consultations to capture context-specific challenges and priorities; Multi-stakeholder roundtables to encourage practical, solution-oriented discussions. The process should be continuous rather than one-off, with clear follow-up mechanisms, interim outputs, and progress tracking. Hybrid participation (in-person and virtual) is essential to ensure accessibility, especially for stakeholders from developing countries. Finally, structured outputs—such as policy recommendations, toolkits, and roadmaps—should be produced to translate dialogue into tangible action and long-term collaboration.

Several key voices remain underrepresented in global AI governance discussions. These include stakeholders from developing countries, particularly in Africa, as well as small and medium-sized enterprises (SMEs), local innovators, and grassroots organizations. In addition, linguistic and cultural minorities, indigenous communities, and non-English-speaking populations are often excluded, leading to governance frameworks that do not fully reflect global diversity. Youth voices and practitioners working in informal economies are also insufficiently represented, despite being directly impacted by AI-driven transformations. To address this, the AI Dialogue should prioritize inclusive participation mechanisms. This includes providing financial support (travel grants, stipends), enabling remote participation, and offering multilingual engagement formats. Regional pre-dialogues and consultations can help surface local priorities and ensure that contributions are not limited to global elites. Partnerships with local institutions, universities, and civil society organizations can further broaden participation. Moreover, inputs from underrepresented groups should not only be collected but also meaningfully integrated into decision-making processes, with transparent feedback loops demonstrating how contributions influence outcomes. Ensuring inclusivity is essential for building legitimate, equitable, and globally relevant AI governance frameworks.

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 policy co-creation labs, where diverse stakeholders collaboratively design solutions to specific governance challenges. These sessions encourage practical outputs and shared ownership. Scenario-based simulations or "AI governance exercises" can also be valuable, allowing participants to respond to real-world cases (e.g., AI system failures, cross-border data disputes) and explore coordinated responses. Interactive digital platforms can support continuous engagement before, during, and after the Dialogue, enabling participants to submit ideas, vote on priorities, and co-develop recommendations in real time. In addition, fireside chats and small-group roundtables can create more open and candid exchanges compared to formal panels. Regional innovation showcases can highlight context-specific solutions, particularly from underrepresented regions, helping to balance global narratives with local realities. Finally, incorporating youth-led sessions and hackathons can bring fresh perspectives and practical prototypes into the discussion. By combining these formats, the AI Dialogue can become more inclusive, action-oriented, and results-driven, ensuring that participation translates into meaningful contributions and concrete outcomes.

Effective AI governance can draw on a range of existing policies and practical approaches across jurisdictions and sectors. One notable example is the European Union AI Act, which introduces a risk-based framework categorizing AI systems by their potential harm and applying proportionate obligations. This approach offers a scalable model for balancing innovation and safety. At the global level, the UNESCO Recommendation on the Ethics of AI provides a values-based foundation, emphasizing human rights, inclusivity, and ethical safeguards, and has been adopted by a broad range of countries. Another good practice is the use of regulatory sandboxes, as implemented in several countries, allowing innovators to test AI systems under regulatory supervision. This encourages innovation while managing risks in a controlled environment. From a technical and operational perspective, AI audit and impact assessment frameworks are increasingly important. These tools help organizations evaluate bias, fairness, transparency, and potential societal impacts before deployment. Open collaboration initiatives, such as the Global Partnership on Artificial Intelligence, promote knowledge-sharing and joint research, enabling countries to benefit from collective expertise. In addition, capacity-building platforms and public-private partnerships are essential, particularly in developing regions, to strengthen technical skills, institutional readiness, and governance capabilities. Finally, promoting open standards and interoperability, supported by bodies like the International Organisation for Standardization, ensures consistency and compatibility across systems and jurisdictions. Together, these approaches demonstrate that effective AI governance requires a combination of regulation, ethics, technical tools, and international collaboration.