ALSE Data

A successful Global Dialogue on AI Governance should deliver outcomes that go beyond high-level principles and translate into actionable, inclusive, and globally coordinated frameworks. First, it should establish a shared understanding of minimum governance standards for safe, trustworthy, and human-centric AI, while respecting regional differences and development levels. This includes practical guidance on risk classification, accountability mechanisms, and human oversight. Second, the dialogue should result in concrete collaboration mechanisms between governments, private sector, academia, and civil society. AI governance cannot be shaped by a single group; it requires multi-stakeholder engagement and continuous dialogue. Third, capacity-building must be a central outcome. Many countries and institutions still lack the technical and institutional capabilities to implement AI governance effectively. Providing access to tools, knowledge, and best practices is essential for global inclusion. Fourth, the dialogue should promote interoperability between governance approaches. Fragmented regulatory environments risk slowing innovation and creating global inequalities. Alignment efforts, even at a baseline level, would significantly enhance cross-border cooperation. Finally, success would mean creating a living platform or follow-up mechanism that ensures continuity. AI governance is not a one-time discussion but an evolving process that requires ongoing monitoring, feedback, and adaptation. In this sense, the dialogue should act as a bridge between policy and practice, enabling responsible AI adoption while supporting innovation and societal benefit.

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

My selection reflects a focus on translating AI governance from high-level principles into practical, implementable systems, particularly across diverse institutional and regional contexts. Safe, secure, and trustworthy AI is foundational, as trust remains the primary barrier to adoption for both governments and organizations. However, trust cannot be achieved without strong transparency, accountability, and human oversight mechanisms. These are essential to ensure that AI systems remain aligned with societal values and can be effectively monitored and governed. At the same time, AI capacity-building is critical to avoid deepening global inequalities. Many countries and institutions lack not only technical expertise, but also the regulatory and operational capabilities required to implement governance frameworks. Supporting capacity-building through tools, training, and knowledge-sharing is therefore a priority for enabling inclusive participation in the AI ecosystem. Interoperability of governance approaches is equally important. As AI systems operate across borders, fragmented regulatory frameworks risk creating inefficiencies and limiting collaboration. Greater alignment and interoperability can support both innovation and responsible deployment at a global scale. Together, these priorities reflect the need to bridge policy and practice, ensuring that AI governance is not only defined, but effectively implemented across different contexts.

One key cross-cutting issue is the gap between AI governance frameworks and their real-world implementation. While many discussions focus on principles, there is still limited emphasis on practical tools and infrastructures that enable organizations to apply these frameworks effectively. Another important issue is the growing concentration of AI capabilities within a small number of actors, raising concerns about the distribution of influence over standards and governance practices.In addition, the usability of AI governance is often overlooked. For governance to be effective, it must be understandable and actionable for non-technical stakeholders. Finally, stronger cross-border coordination is needed. AI systems operate globally, yet governance approaches remain fragmented across countries. Enhancing alignment and cooperation between nations, while adapting to sector-specific needs, is essential for effective and inclusive AI governance.

In my country and region, AI governance gaps are most visible in the disconnect between rapid technological adoption and the institutional capacity to govern it effectively. There is strong momentum in adopting AI across sectors such as public services, finance, and retail. In strategic areas like defense, notable progress demonstrates the region's capability to develop and deploy advanced AI systems, creating broader opportunities for efficiency and innovation. However, the lack of clear governance frameworks and practical implementation tools limits responsible and scalable adoption. At the ministry level, different public institutions are actively working on AI-related initiatives. Yet, these efforts are often carried out independently, with limited coordination or visibility across institutions. This highlights the need for a more centralized and harmonized national approach to AI governance. A key challenge is limited capacity at both institutional and operational levels, particularly in managing risks such as bias, accountability, and transparency. At the regional level, fragmentation between countries further complicates cross-border collaboration. At the same time, this presents an opportunity to build more adaptive governance models, strengthen capacity-building, and enhance cross-border alignment, positioning the region as an active contributor to global AI governance.

The AI Dialogue can play a critical role as a bridge between fragmented national approaches and the need for coherent global AI governance. First, it can provide a neutral and trusted platform for multi-stakeholder engagement, bringing together governments, private sector, academia, and civil society to exchange perspectives and align on shared priorities. This is essential in a landscape where AI development and governance are advancing at different speeds across countries . Second, the Dialogue can support greater interoperability between governance frameworks. By identifying common principles and practical areas of alignment, it can reduce regulatory fragmentation and enable more effective cross-border collaboration. Third, it can accelerate capacity-building by facilitating knowledge-sharing, best practices, and practical tools, particularly for countries and institutions with limited resources. This would help ensure more inclusive participation in shaping and implementing AI governance. Finally, the Dialogue can serve as a mechanism for continuity, enabling ongoing exchange, monitoring, and adaptation as AI technologies evolve. In this sense, the AI Dialogue can act not only as a discussion forum, but as a catalyst for coordinated, actionable, and globally inclusive AI governance.

The AI Dialogue should build upon existing global frameworks such as the OECD AI Principles, the UNESCO Recommendation on the Ethics of AI, the EU AI Act, and recent efforts like the UN AI Advisory Body, which together provide a strong foundation for trustworthy and human-centric AI. At the same time, it should connect with leading academic AI forums and research centers, as well as private sector initiatives that are actively shaping the development and deployment of AI systems. However, these efforts often remain fragmented across sectors. The key added value of the AI Dialogue would be to bring together public institutions, private sector actors, academia, and civil society; including think tanks, into a more structured and inclusive platform. Beyond alignment, the Dialogue can act as an operational bridge between principles and implementation, translating governance frameworks into practical tools and approaches that institutions can apply. By enabling coordination across stakeholders, amplifying diverse voices, and supporting cross-border collaboration, the AI Dialogue can help ensure that global AI governance is not only defined by a limited group of actors, but shaped through a more inclusive and representative process.

Different stakeholders should contribute to the AI Dialogue through clearly defined and complementary roles. Governments can provide policy direction, the private sector can share implementation experience, academia can offer research-based insights, and civil society and think tanks can represent societal and ethical perspectives. To ensure effectiveness, the AI Dialogue should be structured as a multi-tiered and continuous platform rather than a one-time event. First, an annual high-level summit can bring together senior decision-makers to set strategic priorities and align on key governance challenges. Second, thematic working groups (AI safety, capacity-building, interoperability) should operate on an ongoing basis, enabling deeper collaboration and the co-creation of policy recommendations. Third, AI Governance Sandboxes can be established to test regulatory approaches, risk frameworks, and oversight mechanisms in controlled, real-world environments, involving both public and private sector actors. Fourth, dedicated implementation labs can support the development of practical tools and governance infrastructures. Finally, a digital collaboration platform should enable continuous engagement, knowledge-sharing, and cross-border coordination. Each cycle should produce concrete outputs such as policy briefs, implementation guidelines, and pilot outcomes. This structured approach can bridge the gap between dialogue and implementation while ensuring inclusive and action-oriented participation.

Several important voices remain underrepresented in global AI governance discussions. First, practitioners and implementers within organizations such as public officials, business teams, and technical operators, are often excluded, despite being directly responsible for applying AI systems in real-world contexts. Second, emerging economies, including actors within informal economies, are frequently underrepresented due to limited resources and structural constraints. This risks concentrating influence over AI governance in a small number of countries and large technology actors. Third, sector-specific stakeholders(particularly in areas such as healthcare, public services, and finance)are not sufficiently included, despite facing distinct risks and operational realities. In addition, there is a growing need for new bridging roles within governments, such as AI policy advisors who can connect public institutions with private sector actors, global initiatives, and foreign affairs units. These roles can help ensure more coherent, coordinated, and internationally aligned AI governance approaches. To address these gaps, the AI Dialogue should adopt inclusive participation mechanisms such as targeted regional outreach, financial and technical support, hybrid participation formats, and dedicated tracks for practitioners and sector experts, ensuring that diverse perspectives are meaningfully integrated into decision-making processes.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panels and adopt more interactive and outcome-oriented formats. A central format could be AI Policy Roundtables focused on drafting policy recommendations. Each roundtable would bring together representatives from government, private sector, academia, and civil society to ensure balanced and multi-stakeholder input. These roundtables should be organized around specific sectors such as healthcare, agriculture, finance, and public technologies, allowing discussions to be grounded in real-world challenges and context-specific risks. Complementary formats such as scenario-based simulations and design sprints can further support collaborative problem-solving and deepen engagement. A continuous digital platform can extend participation beyond the event, enabling knowledge-sharing and cross-border coordination. Each cycle of the Dialogue should produce concrete outputs, including research reports and policy recommendations, providing timely and actionable input to guide the work of UN bodies and global governance processes. Such an approach can transform the Dialogue into a participatory and impact-driven process, where stakeholders actively co-create sector-specific and actionable AI governance solutions.

Effective AI governance increasingly depends on participatory and practice-oriented approaches that go beyond static frameworks. A relevant example is the OECD's current Global Call for "Governing with AI," which invites governments and stakeholders worldwide to share real-world use cases, policy initiatives, and implementation tools. This type of open consultation helps build a collective evidence base and ensures that governance frameworks are informed by diverse country experiences and practical insights. In parallel, policy simulations and negotiation-based exercises such as those conducted in academic and policy settings like the Harvard Kennedy School AI Negotiation Forum, enable stakeholders to test governance responses in complex, real-world scenarios and better understand cross-border dynamics. Together, these approaches highlight the importance of combining global consultation mechanisms with interactive, experience-based learning supported by continuity and long-term coordination. This combination supports more inclusive, adaptive, and actionable AI governance, grounded not only in principles but also in practice.