Access Bank Plc

In my opinion, the first Global Dialogue on AI Governance would be successful if it achieves three core outcomes: clarity of direction, inclusive participation, and momentum toward practical cooperation. First, success would mean clarity on shared priorities and governance gaps. The Dialogue should result in a clear Co‑Chairs' summary that identifies areas of convergence, priority risks, and unresolved questions, without forcing premature consensus. This would help establish the Dialogue as a trusted space for coordination rather than negotiation, and provide a common reference point for governments and stakeholders navigating an increasingly fragmented AI governance landscape. Second, success would be reflected in meaningful inclusiveness, particularly for developing countries and underrepresented communities. This goes beyond participation to ensuring that capacity gaps, linguistic and cultural diversity, and different levels of AI readiness are explicitly recognized. A strong outcome would be agreement on how existing United Nations and multi‑stakeholder mechanisms can be leveraged to support capacity‑building, including skills development, institutional readiness, and access to computing infrastructure. Without this, global AI governance risks reinforcing existing inequalities. Third, the Dialogue should create practical momentum toward implementation. This could include a shared mapping of capacity needs and available support mechanisms, identification of priority areas where governance interoperability is most urgent, and clear signals on how transparency, accountability, and human oversight can be operationalized in practice. Equally important is agreement on follow‑up themes or workstreams to ensure continuity beyond the first meeting. Finally, success would be measured by how effectively the Dialogue embeds human rights, social and economic impacts, and responsible open innovation as foundational elements of AI governance, rather than as side issues. Ultimately, the first Global Dialogue would be successful if participants leave with greater clarity on how to cooperate, build capacity, and align governance approaches, laying a credible foundation for ongoing, inclusive, and action‑oriented global AI governance.

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

These four thematic areas reflect my priorities because they are foundational enablers of effective, inclusive, and globally applicable AI governance. Safe, secure and trustworthy AI is a prerequisite for public trust and responsible adoption. Without shared expectations around safety, risk management, and system reliability, AI deployment risks causing harm and eroding confidence across societies and markets. AI capacity-building is essential to ensuring that AI governance is not limited to a small number of technologically advanced actors. Many countries face gaps in skills, institutional readiness, access to data, and computing infrastructure. Addressing these gaps is critical for meaningful participation in AI governance, effective oversight, and equitable access to AI's benefits. Protection and promotion of human rights must be central to AI governance. AI systems increasingly affect access to services, economic opportunities, and civic participation. Embedding human rights across the AI lifecycle, from design and deployment to monitoring and redress, ensures that governance frameworks align with international law and remain people-centred. Transparency, accountability, and human oversight are necessary to operationalize both safety and human rights commitments. Clear expectations around explainability, accountability mechanisms, and meaningful human control help translate principles into practice and support responsible use of AI in both public and private sectors. Together, these priorities address the most urgent risks while enabling inclusive participation, effective oversight, and sustainable innovation across different national and development contexts.

One important cross-cutting issue is the concentration of AI infrastructure and capability, particularly access to large-scale computing resources and foundational models. This concentration has implications for competition, sovereignty, security, and the ability of countries to meaningfully govern and benefit from AI systems. Another emerging issue is the environmental and sustainability impact of AI, including the energy and resource demands of large-scale model training and deployment. Integrating environmental considerations into AI governance discussions is increasingly important for alignment with broader sustainable development goals. Additionally, governance of AI in the public sector deserves focused attention. Governments are rapidly adopting AI for public services, decision-making, and administration, raising unique questions around accountability, procurement, transparency, and public trust. Finally, the pace of technological change itself is a cross-cutting challenge. Governance mechanisms must remain adaptive and forward-looking to keep pace with evolving AI capabilities, while still grounded in scientific evidence and inclusive dialogue. Addressing these cross-cutting issues alongside the core thematic areas would strengthen the relevance, resilience, and long-term impact of the Global Dialogue on AI Governance.

From a Nigerian perspective, governance gaps and recent advances in safe and trustworthy AI, AI capacity‑building, human rights protection, and transparency and human oversight are already shaping how AI is adopted across the economy and public sector. Nigeria has made important policy advances, notably through the launch of the National Artificial Intelligence Strategy (NAIS) and the strengthening of data protection under the Nigeria Data Protection Act (NDPA) 2023. These frameworks signal strong political commitment to ethical, inclusive, and development‑oriented AI. However, a key challenge remains the translation of these strategies into consistent, enforceable practice across sectors. AI governance responsibilities are currently spread across multiple regulators, leading to fragmentation and uneven oversight, particularly in high‑impact areas such as fintech, digital identity, telecommunications, and public service delivery. Capacity gaps are another significant constraint. While Nigeria has a vibrant AI innovation ecosystem and a growing pool of talent, regulatory institutions and public sector bodies often lack the technical expertise, data access, and computing resources needed to effectively evaluate, monitor, and audit AI systems. This limits the practical application of transparency, accountability, and human oversight requirements, even where legal obligations exist. From a human rights perspective, the expanded use of AI in areas such as credit scoring, surveillance, and automated decision‑making raises concerns around privacy, bias, and redress. Although the NDPA provides safeguards, enforcement capacity and public awareness remain uneven, creating risks of rights‑infringing deployments without adequate accountability. At the same time, these gaps present clear opportunities. Nigeria's AI strategy, public‑sector capacity‑building initiatives, and engagement with international partners position the country to pilot rights‑based, transparent AI governance models that can scale nationally and inform regional practice. Strengthening institutional capacity and coordination could enable Nigeria to emerge as a leader in responsible AI governance in Africa.

The AI Dialogue can play a critical role in advancing international cooperation on AI governance by serving as an inclusive, neutral, and UN‑anchored platform that bridges fragmented governance efforts and enables coordinated action across countries and stakeholder groups. First, the Dialogue can help build shared understanding and trust among countries at different stages of AI readiness. By creating space for open exchange between governments, the private sector, civil society, academia, and the technical community, the Dialogue can reduce misalignment, promote transparency, and support confidence‑building in how AI systems are governed and deployed across borders. Second, the Dialogue can promote governance interoperability rather than uniformity. As national and regional AI frameworks continue to evolve, international cooperation will depend on compatibility between approaches. The Dialogue can support this by encouraging shared vocabularies for AI risk, accountability, and oversight, and by identifying areas where alignment is most urgently needed to facilitate cross‑border cooperation, trade, and innovation. Third, the AI Dialogue can act as a connector between science, policy, and capacity‑building. By drawing on evidence from the Independent International Scientific Panel on AI, the Dialogue can ground policy discussions in shared facts and emerging risks, while helping translate scientific insights into practical governance cooperation. It can also help coordinate existing UN and multi‑stakeholder mechanisms to address capacity gaps, particularly in developing countries. Finally, the Dialogue can provide continuity and momentum by identifying priority areas for follow‑up, partnership, and learning beyond a single meeting. This includes supporting rights‑based approaches, transparency and accountability practices, and responsible innovation, including open‑source and public‑interest AI initiatives. Overall, the AI Dialogue's most important contribution to international cooperation is its ability to bridge principles and practice, ensuring that AI governance evolves through collaboration, inclusiveness, and shared responsibility rather than fragmentation or unilateral action.

The AI Dialogue should build upon and connect with several existing international, regional, and multistakeholder initiatives to avoid duplication and strengthen coherence in global AI governance. At the global level, the Global Digital Compact provides a foundational framework for digital cooperation, including commitments on safe, inclusive, and rights‑based AI governance. The AI Dialogue can serve as a dedicated space to translate these high‑level commitments into practical cooperation on AI‑specific governance challenges, including capacity‑building, interoperability, and oversight. The work of the Independent International Scientific Panel on AI is also central, offering an evidence‑based foundation that can inform policy discussions and help ground cooperation in shared facts and risk assessments. The Dialogue should also connect with established multistakeholder and standard‑setting initiatives, such as the ITU's AI for Good platform, which focuses on practical AI applications for sustainable development, and the OECD AI Principles, which have informed many national and regional governance approaches. These initiatives offer valuable technical expertise, policy tools, and implementation experience that can complement the Dialogue's convening role. At the regional level, frameworks such as the African Union's Continental AI Strategy demonstrate how regional cooperation can align AI governance with development priorities, human rights, and capacity‑building needs. Connecting regional efforts with global dialogue can help ensure diverse perspectives are reflected and that global cooperation supports, rather than overrides, regional strategies. The added value of the AI Dialogue lies in its UN‑anchored, universal, and inclusive mandate. Unlike sector‑specific or regional initiatives, the Dialogue can act as a bridge, linking science and policy, global and regional efforts, and countries at different stages of AI readiness. By fostering shared understanding, promoting interoperability rather than uniformity, and coordinating capacity‑building efforts, the AI Dialogue can help transform a fragmented governance landscape into a more coherent and cooperative global approach to AI governance.

Different stakeholders can contribute to the AI Dialogue in complementary and mutually reinforcing ways if the Dialogue is structured to value diverse forms of expertise and experience. Governments can contribute policy perspectives, regulatory experiences, and lessons learned from national and regional AI governance efforts. Their participation is essential for identifying areas of convergence, interoperability challenges, and practical implementation constraints. International organizations and UN entities can support coordination, ensure coherence with existing frameworks, and link AI governance discussions to broader agendas such as sustainable development, human rights, and capacity‑building. The private sector can contribute technical expertise, operational insights, and experience from deploying AI systems at scale, including on risk management, transparency practices, and responsible innovation. Academia and the scientific community can provide independent, evidence‑based analysis of AI's impacts, risks, and opportunities, helping ground the Dialogue in shared facts rather than speculation. Civil society and affected communities play a critical role in highlighting real‑world impacts, human rights concerns, and social implications, ensuring that governance discussions remain people‑centred. To support these contributions, the AI Dialogue should adopt a modular and inclusive structure, combining: 1) High‑level plenary sessions for shared framing and political visibility; 2) Thematic breakout discussions aligned with the Dialogue's mandate; 3) Structured opportunities for stakeholder interventions, including written inputs and remote participation; 4) Clear synthesis outputs, such as Co‑Chairs' summaries, that capture areas of convergence and open questions. This structure would enable broad participation while maintaining focus, continuity, and practical relevance.

Several voices and perspectives remain underrepresented in global discussions on AI governance. These include developing countries and least developed countries, particularly those with limited AI infrastructure and regulatory capacity; local innovators and small enterprises from the Global South; communities affected by AI deployment, such as workers, marginalized groups, and language minorities; and public sector practitioners responsible for implementing AI in government services. Women, youth, persons with disabilities, and indigenous communities are also often underrepresented, despite being disproportionately affected by AI‑driven decisions and systems. In addition, non‑technical perspectives, including social scientists, ethicists, and practitioners from sectors such as education, health, and social protection, are not always adequately reflected. These voices could be better included through: 1) Dedicated speaking slots and thematic sessions focused on lived experience and implementation challenges; 2) Targeted outreach and support for participation from underrepresented regions and groups; 3) Hybrid and remote participation formats to reduce cost and access barriers; 4) Use of regional consultations and partnerships to channel local perspectives into the global Dialogue; 5) Clear mechanisms for written submissions that are reflected in official outcomes. Ensuring meaningful inclusion would strengthen the legitimacy, relevance, and equity of global AI governance discussions.

Innovative engagement formats can help ensure the AI Dialogue is interactive, inclusive, and action‑oriented rather than purely declarative. One effective format is issue‑focused roundtables that bring together diverse stakeholders to discuss a concrete governance challenge, such as AI oversight in public services or cross‑border data governance, and identify shared insights and gaps. Scenario‑based discussions can also be valuable, using realistic AI use cases to explore governance trade‑offs, risk mitigation approaches, and responsibilities across actors. This helps translate abstract principles into practical considerations. Regional or cross‑regional dialogues, held alongside the global Dialogue, could allow participants to surface context‑specific challenges and feed structured inputs into the plenary discussions. The Dialogue could also include interactive policy labs or clinics, where governments and stakeholders present real governance questions and receive peer feedback, fostering learning and cooperation rather than negotiation. To enhance inclusiveness, hybrid participation tools, live polling, and moderated digital platforms could be used to capture inputs from remote participants in real time. Finally, allocating time for reflection and synthesis sessions, where moderators summarize emerging themes and areas of convergence, can help ensure discussions translate into clear takeaways and inform future work. Together, these formats would promote deeper engagement, mutual learning, and practical outcomes aligned with the Dialogue's mandate.

Several existing policies, practices, and platforms offer concrete and practical approaches to addressing AI governance challenges. A widely cited policy example is the OECD AI Principles, which provide a values-based framework for trustworthy AI grounded in human rights, transparency, robustness, and accountability. These principles have been adopted or referenced by dozens of countries and inform national AI strategies, risk frameworks, and oversight mechanisms, contributing to greater international interoperability in AI governance. The European Union's AI Act offers a regulatory model based on a risk-based approach, distinguishing between unacceptable, high-risk, limited-risk, and minimal-risk AI systems. By linking governance obligations to the level of risk, the Act provides a structured way to protect fundamental rights while allowing innovation in lower-risk applications. At the regional level, the African Union's Continental AI Strategy presents an Africa-centric, development-oriented approach to AI governance. It integrates capacity-building, data governance, human rights protection, and regional cooperation, offering a model for aligning AI governance with socioeconomic development priorities. In terms of platforms and practical implementation, the ITU's AI for Good platform demonstrates how multistakeholder collaboration can translate governance principles into actionable solutions. Through technical standards development, policy dialogue, and sector-specific initiatives in areas such as health, agriculture, and multimedia authenticity, the platform bridges policy, technology, and real-world deployment. At the national level, Nigeria's Data Protection Act (NDPA) 2023 illustrates how existing data protection frameworks can address AI risks in the absence of AI-specific legislation. Provisions on automated decision-making, data protection impact assessments, and human intervention provide practical safeguards relevant to AI governance. Together, these examples show that effective AI governance can be advanced through risk-based regulation, rights-based principles, regional cooperation, technical standards, and adaptive use of existing legal frameworks.