Bolla AI and Digital

A successful first Global Dialogue on AI Governance would be defined not merely by participation, but by the production of credible, actionable, and inclusive outcomes that shape both policy and practice. First, consensus on foundational principles would be essential. While complete global alignment is unrealistic, agreement on core norms such as transparency, accountability, human oversight, and safety would establish a shared ethical baseline. This should be accompanied by a commitment to align with existing international frameworks while avoiding regulatory fragmentation. Second, the dialogue should produce a clear roadmap for implementation, moving beyond abstract declarations. This would include timelines for developing standards, mechanisms for cross-border cooperation, and pathways for translating principles into national policies. The inclusion of monitoring and evaluation structures would further enhance credibility. Third, meaningful representation of the Global South would be a critical indicator of success. AI governance must not be shaped solely by technologically advanced economies; rather, it should reflect diverse socio-economic realities. This includes ensuring that African, Latin American, and other underrepresented regions actively influence outcomes, particularly in areas such as data sovereignty, infrastructure, and capacity building. Fourth, the establishment of multi-stakeholder collaboration mechanisms bringing together governments, industry, academia, and civil society would be vital. Effective governance requires sustained dialogue beyond a single event, potentially through the creation of a permanent international working group or observatory. Fifth, tangible commitments to capacity building and equitable access should emerge, including funding initiatives, knowledge-sharing platforms, and support for developing AI ecosystems in emerging economies. Ultimately, success would be measured by whether the dialogue catalyses trust, coherence, and momentum laying the groundwork for a globally coordinated, ethically grounded, and practically implementable approach to AI governance.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Open-source software, open data and open AI models
• AI capacity-building

Capacity-building is foundational to any equitable AI future. Without investment in skills, infrastructure, and institutional readiness, many regions particularly across Africa risk remaining passive consumers of AI technologies rather than active producers. Prioritising this area enables the development of local expertise, supports digital sovereignty, and ensures that educators, policymakers, and learners are equipped to engage critically and productively with AI systems. For an entity focused on innovation and education, this is the most immediate lever for long-term transformation. Trust is a prerequisite for adoption. In educational and public sector contexts, concerns around data privacy, bias, misinformation, and system reliability are particularly acute. Prioritising safety and security ensures that AI deployment aligns with safeguarding frameworks, regulatory expectations, and ethical standards. This is especially critical when working with young people and vulnerable populations, where risk mitigation must be embedded by design. Beyond technical deployment, AI profoundly reshapes societies. This thematic area captures the need to contextualise AI within local cultures, languages, and socio-economic realities. For Africa and multilingual contexts such as Cameroon, this is critical to avoid technological exclusion and to ensure that AI systems are inclusive, culturally relevant, and ethically grounded.

Yes. While the listed themes are comprehensive, several cross-cutting and emerging issues warrant explicit recognition to ensure a more robust and future-oriented AI governance framework. First, AI and education system transformation remains underrepresented. Beyond capacity-building, there is a need to address how AI reshapes pedagogy, assessment integrity, curriculum design, and the role of educators. This includes the governance of AI use in learning environments, academic honesty, and the long-term implications for knowledge production and cognitive development. Second, digital inequality and infrastructure asymmetry require sharper focus. Capacity-building alone does not fully capture disparities in connectivity, compute access, and data infrastructure. Without addressing these structural gaps, global AI governance risks reinforcing existing inequalities between and within nations. Third, AI governance in fragile and low-resource contexts is an emerging concern. Many governance frameworks assume stable institutions and regulatory capacity, which may not exist universally. There is a need for adaptive, context-sensitive governance models that can operate effectively in diverse political and economic environments. Fourth, environmental sustainability of AI systems is increasingly critical. The energy consumption of large-scale models, data centres, and compute-intensive processes raises significant environmental concerns. Sustainable AI practices-such as efficient model design and green infrastructure-should be embedded within governance discussions. Fifth, labour market disruption and workforce transition deserves explicit attention. While socio-economic implications are noted, the pace of AI-driven automation requires proactive strategies for reskilling, job redesign, and social protection systems. Finally, AI sovereignty and geopolitical dynamics are becoming central. Nations are increasingly concerned with control over data, models, and infrastructure. This has implications for international collaboration, standard-setting, and equitable participation in the global AI ecosystem. In sum, these cross-cutting issues highlight the need for governance approaches that are not only ethical and technical, but also structural, environmental, and geopolitical in scope.

From the perspective of the Cameroon Artificial Intelligence and Digital Innovation Lab (CAIDIL), governance gaps in AI are already shaping both constraints and opportunities across Cameroon and the wider African context. A primary challenge lies in limited institutional and regulatory readiness. While interest in AI is growing, there is no fully articulated national AI governance framework that addresses safety, accountability, and ethical deployment. This creates uncertainty for educational institutions, public sector adoption, and private innovation, often leading to fragmented or cautious implementation. In education, for example, the absence of clear guidance on AI use in assessment and teaching risks both misuse and underutilisation. A second critical gap concerns capacity and infrastructure asymmetry. Despite increasing awareness, there remains a shortage of advanced AI skills, research ecosystems, and computational infrastructure. This limits the ability of local actors to develop indigenous solutions, reinforcing dependence on external technologies that may not align with local cultural, linguistic, or socio-economic realities. Third, data governance and trust deficits present significant barriers. Weak frameworks around data protection, data sharing, and algorithmic transparency hinder the development of trustworthy AI systems. This is particularly sensitive in sectors such as education, health, and public administration, where public confidence is essential. However, these challenges also create substantial opportunities. CAIDIL is uniquely positioned to act as a bridging institution, supporting policy development, capacity-building, and multi-stakeholder collaboration. The current governance gap enables the design of context-specific, Africa-centred AI frameworks that embed inclusivity, multilingualism, and ethical considerations from the outset. Furthermore, the global momentum around AI governance presents an opportunity for Cameroon to leapfrog legacy systems, adopting best practices while avoiding pitfalls experienced elsewhere. By aligning capacity-building with governance, CAIDIL can contribute to shaping a trusted, locally relevant, and globally connected AI ecosystem. In this sense, governance gaps are not only constraints but also catalysts for strategic innovation and leadership.

The AI Dialogue can serve as a critical platform for aligning global principles, fostering trust, and coordinating action across nations. It enables governments, industry, academia, and civil society to converge on shared standards while respecting regional diversity. By facilitating knowledge exchange, it supports capacity-building particularly for emerging economies ensuring more equitable participation in AI development. The Dialogue can also reduce regulatory fragmentation by promoting interoperability of governance approaches and encouraging collaborative frameworks for safety, accountability, and data governance. Ultimately, it acts as a catalyst for collective stewardship of AI, strengthening international cooperation while advancing innovation that is ethical, inclusive, and globally beneficial.

The AI Dialogue should build upon initiatives such as UNESCO's Recommendation on the Ethics of AI, the OECD AI Principles, the Global Partnership on AI (GPAI), and regional frameworks including the African Union AI Strategy. It should also connect with Jisc, the European Union AI Act, and multi-stakeholder forums like the Internet Governance Forum. The added value lies in bridging fragmentation, aligning these efforts into a more coherent global architecture. The Dialogue can amplify Global South voices, accelerate knowledge-sharing, and create actionable coordination mechanisms, ensuring that existing principles translate into practical, inclusive, and interoperable governance frameworks.

Different stakeholders bring complementary strengths to the AI Dialogue and should be engaged through clearly defined, participatory roles. Governments should provide regulatory leadership, policy alignment, and commitments to implementation. Industry should contribute technical expertise, transparency practices, and responsible innovation frameworks. Academia and research institutions can offer evidence-based insights, independent evaluation, and foresight on emerging risks. Civil society organisations ensure that human rights, inclusion, and societal impacts remain central, while education providers and capacity-building institutions—such as CAIDIL—play a critical role in developing skills, literacy, and local ecosystems. Importantly, Global South actors must be actively empowered as co-creators, not merely participants. In terms of format, the Dialogue should adopt a multi-layered and iterative structure. This could include: (1) high-level plenary sessions to establish shared principles and political commitment; (2) thematic working groups focused on priority areas (e.g., safety, capacity-building, governance interoperability); and (3) regional forums to contextualise global discussions. A permanent coordination mechanism or secretariat should be established to track progress, monitor commitments, and sustain engagement between annual dialogues. Additionally, outputs should move beyond declarations to include actionable roadmaps, measurable indicators, and funding mechanisms. This structured, inclusive approach would ensure that the AI Dialogue is both deliberative and implementation-oriented.

Global AI governance discussions often underrepresent Global South countries, indigenous communities, non-English-speaking populations, educators, and youth. Their exclusion risks reinforcing cultural bias, linguistic inequity, and policy frameworks misaligned with local realities. Inclusion can be strengthened through funded participation mechanisms, regional consultation forums, and multilingual engagement strategies. Capacity-building initiatives should enable these groups to contribute meaningfully, not symbolically. Additionally, embedding representatives within decision-making bodies—not just advisory roles—would ensure influence over outcomes. Leveraging institutions like CAIDIL can further amplify local expertise, ensuring that AI governance becomes globally representative, contextually relevant, and socially equitable.

Innovative engagement formats should prioritise interaction, co-creation, and inclusivity rather than passive dialogue. One effective approach is the use of policy labs and simulation exercises, where stakeholders collaboratively respond to real-world AI governance scenarios (e.g., bias incidents, data breaches), enabling practical problem-solving. Multi-stakeholder hackathons can generate rapid, solution-oriented outputs, particularly around capacity-building and ethical AI tools. Additionally, regional co-creation workshops both in-person and virtual can ensure that diverse voices shape outcomes before global consolidation. The use of AI-assisted deliberation platforms can synthesise large-scale inputs in real time, supporting evidence-based consensus-building. Structured "reverse panels", where youth, educators, or Global South representatives question policymakers and industry leaders, can rebalance power dynamics. Finally, living documents and iterative feedback loops updated throughout the Dialogue ensure that engagement is continuous, transparent, and action-oriented rather than confined to a single event.

At the policy level, the UNESCO Recommendation on the Ethics of AI (2021) provides a comprehensive, human-centred framework adopted by over 190 countries, emphasising human rights, inclusivity, and environmental sustainability. Similarly, the OECD AI Principles (2019) establish widely recognised standards for trustworthy AI, including transparency, accountability, and robustness. The EU AI Act represents a significant regulatory advance, introducing a risk-based classification model that links obligations to the level of societal risk, offering a practical template for other regions. In terms of practice, algorithmic impact assessments (AIAs) used in countries such as Canada provide structured methods to evaluate risks before deployment. Likewise, AI ethics review boards within organisations help ensure ongoing oversight and accountability. Platform-based approaches also play a key role. The Global Partnership on AI (GPAI) facilitates international collaboration on responsible AI, while tools such as model cards and data sheets for datasets promote transparency by documenting system design, limitations, and intended use. From an educational and capacity-building perspective, platforms like Jisc's digital capability framework support institutions in developing AI literacy and governance readiness. Collectively, these examples demonstrate that effective AI governance requires a combination of regulation, institutional practice, technical transparency, and capacity-building, integrated within a coherent and context-sensitive framework.