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A successful first Global Dialogue on AI Governance should not be judged by the volume of discussion, but by the quality of convergence, commitment, and actionable outcomes it produces. First, success would mean normative alignment across global frameworks. Institutions such as UNESCO and the International Telecommunication Union have already established ethical and technical foundations, yet fragmentation persists. A meaningful outcome would be a shared baseline of principles, interoperable across jurisdictions—covering transparency, accountability, safety, and human rights. This does not require uniform regulation, but coherence that reduces regulatory arbitrage and uncertainty for innovators. Second, the Dialogue should deliver a clear roadmap for implementation, not just intent. This includes agreement on risk-tiered governance models, capacity-building mechanisms, and measurable adoption milestones, particularly aligned with emerging regulatory paradigms such as the EU AI Act. Without implementation pathways, even the most sophisticated principles risk remaining symbolic. Third, success must be defined by inclusion and equity. The Dialogue should institutionalize mechanisms that ensure meaningful participation from the Global South—moving beyond consultation to co-creation of governance models. This includes commitments to digital infrastructure investment, local AI ecosystems, and knowledge transfer. If Africa, Latin America, and parts of Asia are not structurally embedded in governance processes, global AI will remain asymmetrical and ethically incomplete.

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

The selected priorities reflect a balanced, implementation-oriented approach to AI governance that integrates technical robustness, human capability, ethical accountability, and global coordination. Safe, secure and trustworthy AI is foundational because trust determines adoption. Without robust, resilient, and secure systems, AI introduces systemic risks-particularly in critical sectors such as healthcare, finance, and telecommunications. Ensuring safety and reliability is therefore a prerequisite for both innovation and public confidence. AI capacity-building is essential to address global inequalities in AI development and deployment. Many regions-particularly in the Global South-face structural constraints in skills, infrastructure, and institutional readiness. Prioritizing capacity-building ensures that AI governance is not only inclusive but also enables local innovation, reduces dependency, and strengthens digital sovereignty. Transparency, accountability, and human oversight form the ethical and operational core of governance. As AI systems become more autonomous and complex, mechanisms such as explainability, auditability, and human-in-the-loop decision-making are critical to mitigating risks such as bias, opacity, and automation overreach. These safeguards ensure that responsibility remains clearly defined and aligned with societal values. Interoperability of governance approaches addresses the growing fragmentation of regulatory frameworks. AI operates across borders, yet governance remains largely national or regional. Interoperability enables coherence between different regulatory regimes, reduces compliance complexity, and supports scalable innovation while maintaining safeguards. Collectively, these priorities create a coherent governance architecture: safety builds trust, capacity enables participation, accountability ensures responsibility, and interoperability drives global alignment. This integrated approach is particularly critical for ensuring that AI governance is both globally inclusive and practically implementable.

Yes, while the listed themes are comprehensive, several cross-cutting and emerging issues require more explicit attention to ensure future-ready AI governance. First, governance of agentic and autonomous AI systems is an emerging priority. Unlike traditional models, these systems can plan, act, and interact with other systems with limited human intervention. This raises new questions about delegated authority, liability, and real-time oversight that existing frameworks do not fully address. Second, AI supply chain governance is increasingly critical. AI systems depend on complex, globalized layers-data sources, foundation models, APIs, cloud infrastructure, and hardware. Risks such as data provenance, model integrity, and dependency on a small number of providers create vulnerabilities that extend beyond individual systems to entire ecosystems. Third, compute and infrastructure concentration is a strategic concern. Advanced AI development is increasingly controlled by a small number of organizations with access to large-scale compute resources. This concentration risks reinforcing global inequalities and limiting participation from emerging economies, making equitable access to compute a governance issue in its own right. Fourth, the environmental sustainability of AI remains underemphasized. Training and deploying large-scale models require significant energy and water resources. Governance frameworks must incorporate sustainability metrics and green AI standards to align AI development with global climate commitments. Finally, cultural and linguistic inclusivity requires deeper focus. Many AI systems underrepresent non-Western languages and knowledge systems, leading to systemic bias and exclusion. Governance must ensure context-sensitive AI development that reflects diverse epistemologies and societal values. These issues cut across existing themes but highlight a critical shift: AI governance must evolve from regulating systems to governing complex, adaptive ecosystems-balancing innovation, equity, and long-term societal impact.

In the African telecommunications and digital services sector, governance gaps across the selected priorities—trustworthy AI, capacity-building, accountability, and interoperability—are already shaping both risk exposure and opportunity creation. A primary challenge is the capacity deficit. Limited access to advanced skills, computing infrastructure, and high-quality local datasets constrains the development and governance of AI systems. This often results in dependence on externally developed models, raising concerns around data sovereignty, contextual bias, and regulatory misalignment with local realities. Second, fragmented regulatory environments across African markets create barriers to scale. The absence of interoperable governance frameworks complicates cross-border AI deployment, particularly for telecom operators delivering regional digital services (e.g., AI-enabled customer support, fraud detection, and network optimization). This fragmentation increases compliance costs and slows innovation. Third, transparency and accountability gaps are evident in the adoption of AI-driven decision systems. In sectors such as credit scoring, digital identity, and automated customer interactions, limited explainability can erode trust and amplify risks of bias—particularly in underserved communities. However, these challenges present significant opportunities. Africa has the potential to become a global leader in inclusive and context-aware AI governance. By embedding ethical principles early—such as human oversight, fairness, and cultural relevance—there is an opportunity to "leapfrog" legacy regulatory constraints seen in more mature markets. The telecommunications sector, in particular, can enable trusted AI ecosystems by leveraging its infrastructure, data reach, and customer interfaces to deploy AI responsibly at scale. Additionally, regional collaboration initiatives can drive harmonized governance frameworks, unlocking cross-border innovation and digital trade. Ultimately, addressing these governance gaps can transform the region from a passive consumer of AI into an active co-creator of globally relevant governance models.

The AI Dialogue can serve as a catalytic platform for coordinated global action, moving AI governance from fragmented national efforts toward coherent, interoperable international systems. First, it can enable norm convergence by aligning existing principles and standards developed by institutions such as UNESCO and the International Telecommunication Union. Rather than duplicating frameworks, the Dialogue can harmonize them into a shared global baseline, reducing regulatory fragmentation and uncertainty for governments and industry. Second, the Dialogue can accelerate practical cooperation through implementation pathways. This includes facilitating agreement on risk-based governance models, cross-border testing environments (e.g., regulatory sandboxes), and shared metrics for safety, accountability, and performance. Such mechanisms translate high-level principles into operational standards. Third, it can act as a bridge between Global North and Global South priorities, ensuring that international governance is not asymmetrical. By institutionalizing co-creation mechanisms, the Dialogue can support capacity-building, knowledge transfer, and infrastructure investment—enabling more equitable participation in AI development and oversight. Fourth, the Dialogue can foster multi-stakeholder collaboration ecosystems. Effective AI governance requires coordination between governments, industry, academia, and civil society. The Dialogue can formalize these interactions, enabling shared responsibility for both innovation and risk mitigation. Finally, it can establish a continuous global coordination mechanism—a living platform for monitoring emerging risks, sharing best practices, and adapting governance approaches as AI technologies evolve, particularly with the rise of generative and autonomous systems. In essence, the AI Dialogue's role is to transform international cooperation from episodic engagement into sustained, structured collaboration, ensuring that AI governance remains adaptive, inclusive, and globally aligned.

A credible AI Dialogue should build on, not duplicate, existing global efforts, connecting ethical norms, technical standards, and regulatory practice into a coherent ecosystem. Key initiatives include: the UNESCO Recommendation on the Ethics of AI, which anchors human-rights–based principles; the International Telecommunication Union AI for Good platform, which convenes standards bodies and practitioners; the OECD AI Principles and policy observatory, which provide widely adopted guidance; the Global Partnership on AI (GPAI), which advances applied research and policy pilots; and the National Institute of Standards and Technology AI Risk Management Framework, which operationalizes risk-based governance. Regulatory advances such as the EU AI Act also offer concrete implementation models. In Africa, emerging regional efforts (e.g., AU data policy frameworks and national AI strategies) add essential context on inclusion and development. The added value of the AI Dialogue lies in integration and execution: Interoperability layer: Translate principles into cross-walks between frameworks (e.g., mapping UNESCO ethics to NIST controls and EU risk tiers), reducing duplication and compliance friction. Implementation accelerators: Establish global regulatory sandboxes and reference architectures for high-risk use cases (health, finance, telecom), enabling safe, cross-border experimentation. Inclusive co-creation: Institutionalize Global South participation via funded fellowships, shared compute access, and regional hubs—moving from consultation to joint design. Assurance and metrics: Define common assurance metrics (safety, robustness, fairness, environmental impact) and promote independent auditing and certification pathways. Rapid response mechanism: Create a living coordination platform to share incident data, best practices, and updates for emerging modalities (e.g., agentic systems). In sum, the Dialogue can convert a landscape of strong but fragmented initiatives into a connected, action-oriented governance system.

Effective AI governance requires coordinated, role-specific contributions from diverse stakeholders, combined with a Dialogue structure that converts discussion into measurable outcomes. Stakeholder contributions Governments and regulators: Set policy direction, align national frameworks with global principles, and commit to risk-based regulatory pilots and cross-border cooperation. Industry (technology firms, telecoms, startups): Provide technical expertise, real-world use cases, and transparency commitments (e.g., model documentation, safety testing, auditability). Co-develop interoperable standards. Academia and research institutions: Generate evidence-based insights, advance methods for explainability, safety, and evaluation, and support independent validation of AI systems. Civil society and NGOs: Represent public interest, ensure human rights, inclusion, and ethical accountability, and surface societal risks often overlooked in technical design. International organizations such as UNESCO and the International Telecommunication Union: Facilitate global coordination, standard-setting alignment, and capacity-building across regions. Recommended format and structure Thematic working groups aligned to priority areas (e.g., safety, capacity-building, accountability, interoperability), each co-led by multi-stakeholder representatives. Action-oriented deliverables: Each group produces policy briefs, technical standards mappings, and implementation roadmaps with defined timelines and success metrics. Regulatory sandbox network: Establish cross-border pilot environments to test governance approaches in real-world contexts (e.g., healthcare AI, fintech, telecom operations). Global South inclusion mechanism: Dedicated funding, fellowships, and regional hubs to ensure co-creation, not just participation. Annual summit + continuous platform: Combine a high-level convening with a persistent digital collaboration platform for ongoing knowledge exchange, monitoring, and iteration. Accountability framework: Public reporting on commitments, progress tracking dashboards, and independent review mechanisms. This structure ensures the Dialogue evolves from a forum of ideas into a sustained, execution-driven governance ecosystem.

Global AI governance discussions remain structurally imbalanced, with several critical voices underrepresented. First, Global South stakeholders—including African, Latin American, and small-island states—are often consulted but not empowered as co-designers. This limits context-sensitive governance and risks importing misaligned regulatory models. Inclusion requires co-creation mechanisms: funded regional hubs, voting representation in standard-setting, and shared access to compute, data, and research partnerships. Second, local communities and grassroots users, especially those most affected by AI systems (e.g., informal workers, rural populations)—are rarely heard. Their lived experiences are essential for identifying real-world harms. Inclusion can be strengthened through participatory governance models, citizen panels, and community impact assessments embedded in policy processes. Third, linguistic and cultural minorities are underrepresented in both datasets and governance debates, leading to systemic bias. Addressing this requires multilingual policy processes, support for local-language AI development, and inclusion of indigenous knowledge systems in ethical frameworks. Fourth, small and medium enterprises (SMEs) and startups often lack a voice compared to large technology firms, despite being key drivers of innovation. Inclusion mechanisms should include SME advisory councils, simplified compliance pathways, and access to shared regulatory sandboxes. Fifth, interdisciplinary perspectives, particularly from social sciences, humanities, and ethics, remain overshadowed by technical and commercial voices. Governance must integrate philosophical, legal, and sociological expertise to address broader societal implications. To address these gaps, institutions such as UNESCO and the International Telecommunication Union can institutionalize inclusive governance architectures: equitable funding models, transparent selection processes, and accountability for representation. Ultimately, inclusion must shift from symbolic participation to structural influence, ensuring AI governance reflects the diversity of the societies it seeks to serve.

To move beyond passive dialogue and foster meaningful, outcome-driven engagement, the AI Dialogue should adopt innovative, participatory formats that blend policy, technical, and societal perspectives. 1. Policy-to-Prototype Labs Multi-stakeholder teams (policy makers, engineers, ethicists) co-design and rapidly prototype governance solutions (e.g., AI audit frameworks, explainability dashboards). This bridges the gap between abstract principles and operational tools. 2. Scenario-Based Simulation Exercises Participants engage in real-time simulations of AI crises (e.g., algorithmic bias in credit scoring, autonomous system failure, misinformation campaigns). This format strengthens decision-making under uncertainty and reveals governance gaps in practice. 3. Cross-Regional "Governance Hackathons" Short, intensive collaborations where diverse teams develop interoperable policy models or standards mappings across jurisdictions. This encourages innovation while addressing fragmentation in global governance. 4. Living Case Study Clinics Organizations (e.g., telecom operators, healthcare providers) present active AI deployments, followed by structured peer review. This enables shared learning grounded in real-world implementation challenges. 5. AI-Assisted Deliberation Platforms Leverage AI tools to synthesize inputs, map consensus, and surface dissent in real time—enhancing transparency and inclusivity, particularly in large, multilingual settings. 6. Global South Innovation Forums Dedicated spaces for emerging markets to present locally developed AI solutions and governance models, ensuring co-creation rather than peripheral participation. 7. Commitment Roundtables with Public Dashboards Stakeholders make time-bound commitments (e.g., adopting risk frameworks, funding capacity-building), tracked via transparent dashboards to ensure accountability. Supported by convening bodies such as UNESCO and the International Telecommunication Union, these formats transform engagement from discussion into experiential, collaborative, and action-oriented governance practice.

Several mature, practice-oriented approaches already demonstrate how to translate AI principles into effective governance. Risk-based regulation. The EU AI Act operationalizes governance by classifying systems by risk (unacceptable, high, limited, minimal) and attaching proportionate obligations (e.g., conformity assessments, post-market monitoring). This balances innovation with safeguards and provides legal clarity for deployment at scale. Operational risk management frameworks. The National Institute of Standards and Technology AI Risk Management Framework (AI RMF) offers a lifecycle approach-Map, Measure, Manage, Govern-with practical controls (documentation, testing, incident response) that organizations can embed into engineering and MLOps pipelines. Human-rights-based ethics. The UNESCO Recommendation on the Ethics of AI anchors governance in rights, inclusion, and environmental sustainability, and encourages national implementation tools such as impact assessments and public oversight mechanisms. Technical standardization. The International Telecommunication Union and partners (e.g., ISO/IEC JTC 1/SC 42) advance interoperable standards for data quality, model evaluation, and system robustness, enabling cross-border compatibility and certification. Model documentation and transparency. Industry practices such as Model Cards and Datasheets for Datasets (Mitchell et al., 2019; Gebru et al., 2021) improve explainability, disclose limitations, and support auditability across the AI lifecycle. Independent auditing and assurance. Third-party audits, bias testing toolkits (e.g., IBM AIF360), and explainability methods (e.g., SHAP) provide verifiable evidence of fairness and performance, strengthening accountability and trust. Regulatory sandboxes. Cross-sector sandboxes (e.g., in fintech and health) allow controlled experimentation with AI under supervisory oversight, accelerating innovation while managing risk. Added value for the AI Dialogue: connect these elements through framework cross-walks (UNESCO ↔ NIST ↔ EU), shared assurance metrics, and cross-border sandboxes, turning fragmented good practice into a coherent, interoperable governance system.