Law Society of African AI Professionals

The first Global Dialogue on AI Governance would be successful if it moves beyond high-level principles and creates meaningful pathways for inclusive and practical global cooperation. In particular, success would require ensuring that perspectives from the Global South are not treated as secondary contributions, but as central to shaping international AI governance frameworks. Many countries, especially in Africa and other developing regions, are navigating AI adoption within contexts marked by infrastructural gaps, limited technical capacity, unequal access to data and compute resources, and dependence on technologies developed elsewhere. A successful dialogue should therefore acknowledge these asymmetries and support governance approaches that are responsive to different social, economic, and political realities. The dialogue should also produce actionable outcomes. These could include commitments to capacity building, knowledge sharing, technical cooperation, and support for locally grounded AI innovation and regulatory development. Equally important is creating mechanisms for continued engagement between governments, civil society, academia, youth, and private sector actors beyond a single event. Finally, success would mean advancing AI governance frameworks that promote safety, transparency, accountability, and human oversight while also protecting development priorities, human rights, and public trust. The dialogue should help ensure that AI governance is not shaped only by technologically dominant states and corporations, but through genuinely global participation.

AI capacity-building;Interoperability of governance approaches;Transparency, accountability, and human oversight;Safe, secure and trustworthy AI

I selected these thematic areas because they reflect some of the most urgent governance challenges facing developing countries, particularly in Africa, as AI systems become more integrated into public and private life. Safe, secure, and trustworthy AI is essential because many countries are adopting AI technologies without sufficient safeguards, regulatory readiness, or public awareness of associated risks. Governance discussions must therefore prioritize human rights, public trust, and protection against harmful or discriminatory outcomes. AI capacity building is especially important in the African context. Many countries continue to rely heavily on technologies developed in the Global North while facing limited access to funding, infrastructure, technical expertise, compute resources, and research opportunities. Capacity building should therefore go beyond technical training and include support for local innovation ecosystems, policymaking capacity, and context-sensitive governance development. I also selected interoperability of governance approaches because fragmented regulatory systems may create barriers for cooperation, innovation, and cross-border accountability. While governance models should reflect local realities, there is still value in developing shared principles and coordinated approaches internationally. Finally, transparency, accountability, and human oversight are critical to ensuring that AI systems remain subject to democratic governance and public scrutiny. This is particularly important in contexts where institutional safeguards may still be developing and where the use of AI in governance or public services could significantly affect rights and freedoms.

One important cross-cutting issue is the growing concentration of AI development, compute infrastructure, data resources, and governance influence within a small number of countries and corporations. This creates risks of unequal participation in shaping global AI systems and standards, particularly for developing countries. Discussions on AI governance should therefore pay greater attention to questions of equity, digital dependency, access to infrastructure, and meaningful participation in AI development. Without addressing these structural imbalances, there is a risk that global governance frameworks may reinforce existing inequalities rather than reduce them. Another related issue is linguistic and cultural representation in AI systems. Many communities, languages, and local contexts remain underrepresented in datasets and AI development processes, which can affect both the quality and fairness of AI systems globally.

In Kenya, governance gaps in AI are becoming more visible as digital technologies are adopted more rapidly across both public and private sectors. While Kenya has a growing innovation ecosystem and strong digital uptake, governance, regulatory, and institutional frameworks are still developing in ways that can effectively respond to the pace and complexity of AI deployment. One significant challenge is limited regulatory and technical capacity. Many institutions may lack the expertise, infrastructure, or resources necessary to assess AI systems, monitor risks, or implement effective oversight mechanisms. This creates concerns around transparency, accountability, data governance, and the protection of fundamental rights, particularly where AI systems may affect access to public services, employment, finance, or security. Another important issue is dependence on technologies, models, and digital infrastructure developed outside the region. This can limit local participation in AI development and reduce opportunities for context-sensitive innovation that reflects local languages, social realities, and development priorities. At the same time, there are important advances. Kenya has an active technology and innovation sector, growing public awareness around digital rights, and increasing policy discussions on AI governance and data protection. These developments create opportunities to build governance approaches that are both innovation-friendly and rights-based. However, the most significant challenge remains ensuring that AI adoption is accompanied by sufficient safeguards, institutional readiness, and inclusive capacity building so that technological advancement does not outpace accountability and public trust.

The AI Dialogue can play an important role in creating a more inclusive and coordinated approach to international AI governance. At present, AI governance discussions are taking place across multiple institutions, regions, and sectors, often with differing priorities, levels of capacity, and regulatory approaches. The Dialogue can help create a shared platform for cooperation, mutual learning, and sustained engagement among states and other stakeholders. One of its most important functions should be ensuring that developing countries, particularly those from the Global South, are able to participate meaningfully in shaping global AI governance norms and priorities. International cooperation should not be limited to the countries and corporations leading AI development, but should also reflect the perspectives of countries that are primarily navigating AI adoption, implementation, and governance challenges. The Dialogue can also support cooperation through knowledge sharing, technical assistance, and capacity building. Many countries are still in the early stages of developing AI policies, regulatory frameworks, and institutional expertise. Facilitating exchange between governments, academia, civil society, and industry could help strengthen governance readiness globally. In addition, the Dialogue can help reduce fragmentation by encouraging interoperability between governance approaches while still respecting different national and regional contexts. This could contribute to greater accountability, trust, and consistency in how AI systems are developed and deployed internationally.

The AI Dialogue should build upon existing international and regional initiatives that have already contributed to responsible AI governance discussions, including the UNESCO Recommendation on the Ethics of Artificial Intelligence, the OECD AI Principles, the Global Digital Compact process, and emerging regional initiatives within the African Union and other Global South institutions. These initiatives have established important principles around human rights, transparency, accountability, inclusion, and trustworthy AI. However, there are still significant gaps in representation, implementation capacity, and coordination, particularly for developing countries. Many Global South countries continue to face challenges related to limited technical infrastructure, funding, research capacity, access to compute resources, and meaningful participation in global standard-setting processes. The added value of the AI Dialogue could therefore lie in creating a more inclusive and implementation-oriented platform for international cooperation. In particular, the Dialogue can help ensure that AI governance is not shaped only by technologically dominant states and corporations, but also reflects the priorities and realities of countries that are still building their AI ecosystems and governance frameworks. The Dialogue could also strengthen cooperation through capacity building, knowledge sharing, and support for locally grounded innovation and policymaking. For Africa especially, this is important to reduce growing technological dependency and to promote governance approaches that are responsive to local social, economic, cultural, and linguistic contexts. Finally, the AI Dialogue can help connect existing governance efforts, reduce fragmentation between frameworks, and encourage more equitable global participation in shaping the future of AI governance.

Different stakeholders can contribute to the AI Dialogue by bringing diverse forms of expertise, experience, and accountability into governance discussions. Governments play an important role in developing regulatory frameworks and public policy, while the private sector contributes technical expertise and insight into AI development and deployment. Civil society organizations can help ensure that human rights, public interest concerns, and community perspectives remain central to governance discussions. Academia and researchers contribute evidence-based analysis, while youth voices are important because younger generations will be significantly affected by long-term AI governance decisions. To ensure meaningful participation, the AI Dialogue should adopt a genuinely multistakeholder structure that goes beyond symbolic representation. Participation from developing countries, particularly from Africa and other Global South regions, should be actively supported through accessible participation mechanisms, funding support, and regional engagement processes. The Dialogue should also combine high-level discussions with more practical and interactive working sessions focused on implementation challenges, regional priorities, and emerging risks. In addition to plenary discussions, thematic breakout sessions, regional consultations, and collaborative policy workshops could help create more substantive engagement. Finally, the Dialogue should be designed as an ongoing process rather than a one-time event. Mechanisms for continued cooperation, follow-up consultations, and knowledge sharing would help ensure that discussions translate into long-term collaboration and practical governance outcomes.

Several voices and perspectives remain underrepresented in global AI governance discussions, particularly those from developing countries, local communities, youth, indigenous communities, smaller civil society organizations, and researchers from the Global South. In many cases, AI governance conversations continue to be dominated by technologically advanced states, large technology companies, and institutions with greater financial and technical resources. This imbalance can result in governance frameworks that do not fully reflect the realities of countries that are primarily navigating AI adoption rather than leading AI development. For many African countries, discussions around infrastructure limitations, linguistic inclusion, data governance, digital dependency, and capacity building are especially important but may receive less attention in global forums. Greater inclusion requires more than simply inviting participation. It also involves addressing structural barriers that limit meaningful engagement, including funding constraints, unequal access to technical expertise, language barriers, and limited representation in global standard-setting spaces. To improve inclusion, the AI Dialogue could support regional consultations, multilingual engagement, youth participation initiatives, partnerships with local institutions, and accessible virtual participation mechanisms. It is also important to ensure that underrepresented groups are included not only as participants, but as contributors to agenda-setting, decision-making, and policy development processes.

Meaningful engagement during the AI Dialogue would benefit from formats that encourage collaboration, practical problem-solving, and sustained interaction rather than relying only on traditional panel discussions. One effective approach could be the use of thematic and regional working groups where participants collaboratively examine specific governance challenges, such as AI in public services, cross-border data governance, safety standards, or capacity building needs within developing countries. These smaller and more interactive sessions may allow for deeper engagement and more practical outcomes. Scenario-based policy simulations and case-study workshops could also help participants explore how governance frameworks operate in real-world contexts. This may be particularly useful for understanding how AI risks and governance needs differ across regions and sectors. To encourage broader participation, the Dialogue could include dedicated youth forums, civil society roundtables, and regional consultation sessions that feed directly into high-level discussions. Hybrid and multilingual participation mechanisms would also help improve accessibility for participants who may face geographic, financial, or language barriers. Finally, collaborative drafting sessions, open consultation platforms, and ongoing digital engagement spaces could help make the Dialogue more participatory and action-oriented. These formats would allow stakeholders not only to exchange views, but also to contribute directly to recommendations, governance principles, and future cooperation mechanisms.

Several existing policies, practices, platforms, and approaches already provide concrete examples of effective AI governance and offer practical solutions to current governance challenges. At the regional level, the African Union Continental AI Strategy provides a coordinated framework for African states to strengthen AI governance through regional cooperation, ethical alignment, and capacity building. At the national level, Kenya's National AI Strategy (2025-2030) sets out a roadmap for developing AI infrastructure, strengthening research and innovation ecosystems, and promoting responsible deployment, supported by legal safeguards such as the Data Protection Act, 2019, which enhances accountability in the use of personal data. At the global level, instruments such as the UNESCO Recommendation on the Ethics of Artificial Intelligence and the OECD AI Principles establish widely recognised standards for trustworthy, human rights-based AI governance. These frameworks offer practical guidance that can be translated into national policy and regulatory design. Beyond formal policies, several practical governance approaches provide concrete solutions to emerging challenges. Algorithmic impact assessments help identify and mitigate risks before AI systems are deployed, particularly in high-stakes areas such as public services and financial systems. Risk-based regulatory approaches allow governments to tailor oversight according to the level of potential harm posed by different AI applications. In addition, transparency and explainability requirements for public sector AI systems strengthen accountability and public trust by ensuring that automated decisions can be scrutinised and understood. Multi-stakeholder platforms involving governments, civil society, academia, and the private sector further support coordinated responses through knowledge sharing, technical assistance, and capacity building. Together, these approaches demonstrate that effective AI governance is achieved not only through policy development, but through practical implementation tools that enhance oversight, accountability, and inclusive participation.