Ejada

A successful first Global Dialogue on AI Governance should move beyond general principles toward actionable, inclusive, and context-aware outcomes. First, it should establish a shared understanding that AI governance cannot rely solely on the direct adoption of existing regulatory models. Many current frameworks, while robust, do not fully account for the institutional, economic, and data realities of emerging and developing contexts. Recognizing this gap is essential for meaningful global cooperation. Second, the Dialogue should produce a practical mechanism for translating global AI principles into locally applicable governance practices. This could take the form of a flexible "governance translation layer" that enables countries to adapt risk classification, compliance requirements, and oversight structures to their specific environments while maintaining alignment with international standards. Third, success would require the creation of sustained multi-stakeholder collaboration channels—bringing together governments, academia, technical experts, and the private sector—not only for discussion, but for continuous co-development, testing, and refinement of governance approaches. Pilot initiatives and regulatory sandboxes should be encouraged as part of this effort. Finally, the Dialogue should prioritize capacity-building as a core pillar of AI governance. This includes investing in local expertise, institutional readiness, and governance literacy, ensuring that countries are not only adopting AI systems, but are also equipped to govern them responsibly. In essence, the success of the Dialogue lies in its ability to shift from static, one-size-fits-all frameworks toward adaptive, evidence-informed, and context-aware governance architectures that are both globally aligned and locally effective.

• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• AI capacity-building

My selection reflects a priority on bridging the gap between global AI governance frameworks and their effective implementation in diverse, real-world contexts-particularly in emerging and developing economies. AI capacity-building is foundational, as many countries face structural challenges related to institutional readiness, technical expertise, and governance literacy. Without targeted investment in these areas, even well-designed regulatory frameworks risk remaining aspirational rather than operational. Interoperability of governance approaches is equally critical. The current global landscape is characterized by fragmented and often non-aligned frameworks. There is a pressing need for mechanisms that enable adaptation and alignment across jurisdictions while respecting local contexts. This includes translating high-level principles into actionable, context-sensitive governance practices. Transparency, accountability, and human oversight form the operational backbone of trustworthy AI systems. These elements are essential for enabling auditability, managing risk, and ensuring that AI systems remain aligned with human values and regulatory expectations throughout their lifecycle. Finally, the broader social, economic, ethical, cultural, linguistic, and technical implications of AI must be central to governance discussions. AI systems do not operate in a vacuum; their impacts are deeply shaped by local conditions, societal norms, and economic structures. Effective governance must therefore be context-aware and inclusive, ensuring that AI adoption contributes to equitable and sustainable outcomes. Together, these priorities support a shift from static, one-size-fits-all governance models toward adaptive, interoperable, and context-aware frameworks that are both globally aligned and locally effective.

Yes-one critical cross-cutting issue that remains underrepresented is the challenge of translating AI governance from principle to practice across diverse institutional contexts. While existing themes address important dimensions such as safety, transparency, and capacity-building, there is a persistent gap between high-level governance frameworks and their effective implementation, particularly in emerging and developing economies. This gap is not only technical, but also institutional and contextual. A key emerging priority is what can be described as a "governance translation challenge"-the need to systematically adapt global AI principles into locally actionable, context-aware governance mechanisms. This includes aligning risk classifications with local data ecosystems, tailoring compliance models to varying regulatory maturity levels, and embedding feedback loops that allow governance systems to evolve over time. Closely related to this is the issue of "governance readiness," which goes beyond capacity-building to assess whether institutions, processes, and accountability structures are sufficiently prepared to operationalize AI governance. Without such readiness, even well-designed policies may fail to achieve their intended outcomes. Additionally, the interaction between formal governance frameworks and informal or hybrid systems-common in many regions-requires greater attention. AI systems often operate within complex socio-economic environments where informal practices significantly influence outcomes, yet these dynamics are rarely reflected in governance models. Addressing these cross-cutting challenges would enable a shift from static, principle-based governance toward adaptive, implementation-oriented architectures that are responsive to real-world complexity and capable of delivering measurable impact.

In my region (Middle East and North Africa) and across similar emerging economies, AI governance gaps are creating a growing disconnect between rapid AI adoption and the institutional capacity required to manage its risks effectively. One of the most significant challenges is the mismatch between imported governance frameworks and local implementation realities. Many organizations are adopting international standards and regulatory models; however, these often assume levels of data maturity, regulatory enforcement, and technical expertise that are not yet fully developed. As a result, governance efforts risk becoming compliance-oriented on paper, rather than operational in practice. Another challenge lies in fragmented ownership and limited interoperability across institutions and sectors. AI governance responsibilities are often distributed across multiple entities (e.g., data, cybersecurity, risk, and business units) without clear coordination mechanisms, leading to duplication, gaps in accountability, and inconsistent oversight. At the same time, there is a significant opportunity to build context-aware governance models from the ground up. Emerging economies are not constrained by legacy regulatory systems to the same extent as more mature markets, allowing for more adaptive, integrated, and innovation-friendly governance approaches. This includes the use of regulatory sandboxes, iterative policy design, and the integration of governance into AI development lifecycles. Additionally, increasing investment in national AI strategies and digital transformation programs—particularly in countries such as Saudi Arabia and the UAE—creates a strong foundation for advancing governance capabilities alongside technical deployment. Overall, addressing governance gaps presents an opportunity to move beyond replication of global models toward the development of flexible, interoperable, and context-sensitive AI governance frameworks that can serve as scalable references for other emerging regions.

The AI Dialogue can play a pivotal role in advancing international cooperation by shifting from a forum for exchange to a platform for coordinated action and practical alignment. First, it can serve as a bridge between diverse governance approaches, enabling countries to move beyond fragmented and sometimes competing frameworks. By fostering interoperability, the Dialogue can help align global principles with locally adaptable implementation models, ensuring that cooperation does not come at the expense of contextual relevance. Second, the Dialogue can facilitate the co-development of shared governance tools and methodologies. This includes common risk assessment approaches, audit and accountability mechanisms, and policy translation frameworks that can be adapted across different regulatory environments. Such outputs would move international cooperation from abstract commitments to operational capabilities. Third, it can act as a catalyst for sustained multi-stakeholder collaboration. Bringing together governments, academia, industry, and technical communities in a structured and continuous engagement model would enable iterative learning, joint experimentation, and the scaling of successful governance practices. Regulatory sandboxes and pilot initiatives could be coordinated across countries to test and refine governance models in real-world settings. Finally, the Dialogue can elevate capacity-building as a central pillar of international cooperation. Supporting knowledge exchange, institutional development, and governance readiness—particularly in emerging economies—will be critical to ensuring inclusive participation in the global AI ecosystem. In essence, the Dialogue's role is to transform international cooperation from principle-based alignment into a dynamic, implementation-driven ecosystem—one that is interoperable, inclusive, and responsive to the realities of different regions while maintaining a shared global vision for responsible AI governance.

The AI Dialogue should build upon and connect with a range of existing global and regional initiatives that have already established important foundations for AI governance. At the international level, frameworks such as those developed by the Organisation for Economic Co-operation and Development, UNESCO, and the International Telecommunication Union have provided guiding principles, ethical standards, and technical coordination mechanisms. Similarly, regional regulatory efforts—such as the European Union AI Act—have advanced risk-based governance approaches, while multi-stakeholder platforms like the Global Partnership on Artificial Intelligence have fostered collaboration between governments, industry, and academia. However, these initiatives often operate in parallel, with limited mechanisms for alignment, contextual adaptation, and implementation support—particularly in emerging and developing economies. The added value of the AI Dialogue lies in its potential to act as an integrative layer across these efforts. Rather than introducing new principles, it can focus on connecting, translating, and operationalizing existing ones. This includes enabling interoperability between governance frameworks, facilitating the adaptation of global standards to local contexts, and supporting the co-development of practical tools such as policy translation models, shared risk assessment methodologies, and governance readiness frameworks. In addition, the Dialogue can provide a neutral, inclusive platform for continuous engagement—ensuring that perspectives from underrepresented regions are incorporated into global governance discussions and that implementation challenges are addressed collectively. By building bridges across existing initiatives and focusing on practical alignment, the AI Dialogue can transform a fragmented landscape into a more coherent, collaborative, and implementation-oriented global governance ecosystem.

Different stakeholders can contribute to the AI Dialogue by aligning their roles with their comparative strengths while engaging within a structured, implementation-oriented framework. Governments should focus on policy design, regulatory alignment, and enabling cross-border cooperation. Academia can contribute evidence-based research, evaluation methodologies, and critical insights into societal impacts. The private sector brings practical implementation experience, innovation capacity, and real-world use cases, while the technical community can support standards development, system design, and risk mitigation mechanisms. Civil society plays a key role in representing public interests, ensuring inclusivity, and highlighting ethical and human rights considerations. To maximize impact, the AI Dialogue should move beyond a traditional discussion format toward a multi-layered and continuous engagement model. First, it should be structured around thematic working groups that produce concrete outputs—such as policy toolkits, governance frameworks, and implementation guidelines—rather than general recommendations. Second, it should incorporate pilot-driven collaboration, including regulatory sandboxes and cross-country case studies, allowing stakeholders to test and refine governance approaches in real-world settings. Third, a "translation layer" mechanism should be embedded within the Dialogue to systematically adapt global principles into context-specific practices, ensuring relevance across different regions and levels of institutional maturity. Finally, the Dialogue should adopt a cyclical structure—combining periodic high-level convenings with ongoing virtual collaboration—to ensure continuity, iterative learning, and measurable progress over time. Such a format would transform the AI Dialogue into a dynamic, co-creation platform that not only facilitates exchange, but also delivers actionable, context-aware governance solutions.

Global discussions on AI governance continue to underrepresent voices from emerging and developing economies, particularly practitioners operating within real-world implementation environments. While these regions are often included at a policy level, their practical constraints—such as institutional capacity, data infrastructure limitations, and informal economic dynamics—are not sufficiently reflected in governance frameworks. In addition, frontline technical practitioners, public sector implementers, and cross-functional professionals (e.g., those working at the intersection of AI, risk, compliance, and operations) remain underrepresented. These actors possess critical insights into how governance mechanisms function in practice, yet their perspectives are often overshadowed by high-level policy or purely technical narratives. Local communities affected by AI systems—especially in sectors such as finance, healthcare, and public services—are also insufficiently engaged. This includes populations whose data is used or whose livelihoods are impacted by automated decision-making, but who lack formal channels to influence governance design. To address these gaps, the AI Dialogue should move toward more inclusive and structured participation models. This includes establishing regional representation mechanisms, supporting practitioner-led contributions, and integrating implementation case studies as a core component of discussions. Dedicated channels should be created for public sector operators and local stakeholders to share grounded experiences. Furthermore, capacity-building initiatives should be linked with participation, enabling underrepresented groups not only to attend, but to meaningfully contribute. Hybrid engagement formats—combining global forums with localized dialogues—can help ensure that diverse perspectives are systematically captured and reflected in governance outcomes. By broadening participation beyond traditional policy and technical elites, the AI Dialogue can foster more realistic, inclusive, and implementable AI governance approaches.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions toward more interactive, outcome-driven formats that prioritize co-creation, experimentation, and practical problem-solving. First, "policy labs" or co-creation workshops can bring together diverse stakeholders to collaboratively design governance solutions around specific use cases (e.g., AI in finance, healthcare, or public services). These sessions should produce tangible outputs such as draft policy tools, risk frameworks, or implementation roadmaps. Second, simulation-based formats—such as "governance stress tests"—can be introduced, where participants evaluate how different regulatory approaches perform under realistic scenarios. This allows stakeholders to explore trade-offs, identify gaps, and refine governance mechanisms in a controlled environment. Third, cross-regional "paired dialogues" can connect stakeholders from different regions (e.g., emerging and advanced economies) to exchange perspectives and co-develop context-aware adaptations of existing frameworks. This would help bridge the gap between global standards and local realities. Fourth, practitioner-led case clinics can provide a platform for real-world implementers to present governance challenges they are facing, followed by structured, multi-stakeholder feedback and solution design. This ensures that discussions remain grounded in operational realities. Finally, a continuous digital collaboration layer should complement in-person sessions, enabling stakeholders to contribute asynchronously, share resources, and track progress over time. This would transform the Dialogue from a one-time event into an ongoing, iterative engagement process. Together, these formats can create a more dynamic, inclusive, and implementation-oriented dialogue—one that not only facilitates exchange, but actively generates actionable and context-relevant governance solutions.

Several policies and practices have made important progress in advancing effective AI governance, particularly where they combine principled frameworks with practical implementation mechanisms. The European Union AI Act stands out for its risk-based classification model, which links regulatory obligations to the level of risk posed by AI systems. This approach provides a structured and scalable foundation for governance. Similarly, the Organisation for Economic Co-operation and Development AI Principles and the UNESCO Recommendation on the Ethics of AI have established widely recognized normative baselines for responsible AI. Beyond policy frameworks, practical implementation initiatives are equally critical. Regulatory sandboxes-adopted in sectors such as financial services-offer a controlled environment to test AI systems under supervision, enabling iterative learning and evidence-based regulation. In parallel, the National Institute of Standards and Technology AI Risk Management Framework provides actionable guidance for organizations to assess, manage, and mitigate AI risks throughout the system lifecycle. However, a key lesson across these examples is that effectiveness depends not only on the strength of the framework, but on its adaptability to context. In emerging economies, successful approaches increasingly focus on embedding governance within AI development processes, integrating risk assessment, accountability, and oversight into system design rather than treating governance as a separate compliance layer. Building on these practices, there is an opportunity to develop "governance translation" approaches that connect global principles with local implementation realities-ensuring that governance models remain both interoperable and context-sensitive, and capable of delivering measurable impact across diverse institutional environments.