Cyber Institute

Success for the first Global Dialogue on AI Governance would move beyond general principles and produce actionable alignment across diverse governance approaches, while preserving national sovereignty and cultural context. First, success would include the establishment of a shared baseline of understanding on AI risks, opportunities, and terminology, informed by the Independent International Scientific Panel on AI. This is critical to reducing fragmentation and enabling meaningful participation by all Member States, particularly those with limited technical capacity. Second, the Dialogue should deliver practical pathways for interoperability between national and regional governance frameworks, rather than attempting uniformity. This includes identifying areas of convergence across existing approaches and enabling mutual recognition of standards where appropriate. Third, a successful outcome would prioritize closing the global AI capacity gap, ensuring that developing countries are not only participants but co-shapers of governance norms. This includes access to data, infrastructure, and policy expertise to avoid a widening AI divide. Fourth, the Dialogue should advance trust-building mechanisms, including transparency, accountability, and human oversight, while addressing emerging risks such as algorithmic bias, data governance challenges, and dual-use concerns. Finally, success would be measured by the creation of ongoing multistakeholder implementation pathways, linking dialogue outcomes to real-world policy development, pilot initiatives, and measurable progress toward the Sustainable Development Goals. Deliberately, the Dialogue should serve not only as a forum for discussion, but as a catalyst for coordinated, evidence-based, and globally inclusive AI governance that balances innovation with risk mitigation and respects diverse national contexts.

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

The selected priorities reflect areas where coordinated international action is both urgent and achievable. Safe, secure, and trustworthy AI is foundational, as trust underpins adoption, cross-border cooperation, and long-term societal benefit. Without baseline assurances around safety and reliability, other governance efforts risk fragmentation or loss of public confidence. Interoperability of governance approaches is critical in a landscape where multiple national and regional frameworks are emerging simultaneously. Rather than pursuing uniformity, enabling interoperability allows diverse governance models to coexist while still supporting coordination, reducing regulatory friction, and facilitating responsible innovation across jurisdictions. Transparency, accountability, and human oversight are essential to maintaining legitimacy in AI-enabled decision-making. As AI systems become embedded in public and private sector workflows, ensuring that their outputs can be understood, challenged, and governed by humans is central to both democratic accountability and effective risk management. Finally, AI capacity-building is necessary to ensure that all Member States can meaningfully participate in shaping and implementing AI governance. Without targeted efforts to expand technical, institutional, and policy capacity, there is a risk of deepening global inequalities and limiting the inclusiveness of governance outcomes. Together, these priorities support a balanced approach that advances innovation while addressing systemic risks, promotes global participation, and enables practical coordination across diverse governance environments.

A key cross-cutting issue not fully captured in the listed themes is the question of interpretive sovereignty in AI governance. As AI systems, particularly large language models, are increasingly used to support policy development, regulatory analysis, and institutional decision-making, they do more than process information. They shape how concepts such as risk, fairness, compliance, and legitimacy are interpreted and applied. When these systems are trained predominantly on data, norms, and frameworks originating from a limited set of regions, they may implicitly project those perspectives into other governance contexts. Preserving interpretive sovereignty ensures that countries retain the ability to apply AI in ways that align with their own legal systems, cultural values, and policy priorities. This is not only a matter of national control, but of maintaining the integrity and legitimacy of governance outcomes. This issue becomes particularly important in periods of geopolitical tension. In such contexts, collaboration is more likely to emerge where there is confidence that participation does not require ceding interpretive authority. When interpretive sovereignty is respected, it can strengthen trust, enabling states to engage in cooperative frameworks while maintaining alignment with domestic priorities. Transparency and accountability mechanisms further reinforce this by making AI systems more understandable and contestable across jurisdictions. A related emerging issue is the dual-use nature of advanced AI capabilities, particularly as they intersect with cybersecurity and, increasingly, quantum technologies. Governance approaches must anticipate not only current risks but also how rapidly evolving capabilities may shift threat landscapes. Together, these issues highlight the need for governance frameworks that are not only technically robust, but also sensitive to context, sovereignty, and long-term systemic effects.

Across sectors, current AI governance gaps are creating both policy uncertainty and uneven implementation, particularly where regulatory approaches are evolving at different speeds across jurisdictions. One of the most significant challenges is fragmentation of governance frameworks, which complicates cross-border collaboration and implementation. Organizations and institutions operating internationally must navigate differing standards for safety, accountability, and data governance, increasing complexity and slowing the responsible deployment of AI systems. A related challenge is the lack of practical interoperability mechanisms between emerging frameworks. While many principles are aligned at a high level, there is limited guidance on how they can be applied coherently in practice, particularly for institutions working across regulatory boundaries. Capacity disparities also remain a major issue. Many regions face constraints in technical expertise, infrastructure, and policy development capabilities, limiting their ability to fully participate in AI governance processes and benefit from AI-enabled systems. This risks reinforcing existing inequalities and creating asymmetries in both governance influence and technological adoption. At the same time, these gaps present important opportunities. There is growing momentum to develop shared tools, methodologies, and best practices that can support more consistent evaluation and implementation of AI governance across contexts. Advancements in transparency mechanisms and evaluation approaches also offer the potential to strengthen trust and accountability. Finally, there is an opportunity to design governance approaches that are context-aware and sovereignty-respecting, enabling collaboration without requiring uniformity. By focusing on interoperability, capacity-building, and practical implementation pathways, the current moment presents a unique opportunity to shape a more inclusive and coordinated global AI governance ecosystem.

The AI Dialogue can play a critical role as a neutral, multistakeholder platform that facilitates trust-building, policy coherence, and practical coordination across diverse governance approaches. First, it can support the development of a shared understanding of AI risks, opportunities, and terminology, helping to reduce fragmentation and enable more meaningful engagement among Member States with varying levels of capacity and technical maturity. Second, the Dialogue can serve as a forum to advance interoperability between governance frameworks, identifying areas of convergence while respecting different legal, cultural, and institutional contexts. This is particularly important to enable cooperation without requiring uniform regulatory models. Third, the Dialogue can strengthen inclusive participation, ensuring that developing countries are not only represented but actively engaged in shaping governance approaches. This includes elevating perspectives that may otherwise be underrepresented in global AI policy discussions. Fourth, it can help translate high-level principles into practical implementation pathways, including the exchange of best practices, development of shared methodologies, and support for pilot initiatives that demonstrate how governance approaches can be applied in real-world contexts. Finally, the Dialogue can reinforce trust and transparency as foundations for cooperation, particularly in a complex geopolitical environment. By providing a consistent and open forum for engagement, it can reduce uncertainty, build confidence, and support sustained collaboration across regions. In this way, the AI Dialogue can act as a bridge between principle-setting and implementation, helping to advance coordinated, inclusive, and context-aware global AI governance.

The AI Dialogue should build upon and connect with a range of existing international initiatives and processes, including the Global Digital Compact, UNESCO's Recommendation on the Ethics of AI, OECD AI Principles, and ongoing work within forums such as the G7, G20, and regional governance efforts. It should also engage with technical and scientific initiatives, including the Independent International Scientific Panel on AI, as well as multistakeholder platforms such as ITU's AI for Good. These initiatives have made important progress in establishing high-level principles and advancing dialogue. However, there remains a gap in practical coordination, interoperability, and implementation across frameworks. The added value of the AI Dialogue lies in its ability to act as a connecting mechanism across these efforts, helping to align priorities, reduce duplication, and identify pathways for coherent application across jurisdictions. It can provide a structured space to translate principles into shared methodologies, evaluation approaches, and implementation guidance. In addition, the Dialogue can support greater inclusivity and balance, ensuring that perspectives from developing countries and diverse governance contexts are meaningfully integrated into global discussions. Finally, it can help advance evidence-based and context-aware governance approaches, including mechanisms to assess alignment across frameworks while respecting national sovereignty. By focusing on coordination, practical outcomes, and inclusivity, the AI Dialogue can strengthen the overall effectiveness and coherence of the global AI governance ecosystem.

Different stakeholders can contribute to the AI Dialogue by bringing complementary perspectives that reflect the full AI ecosystem. Member States can provide policy leadership and regulatory insight, while international organizations can support coordination and knowledge-sharing. The private sector can contribute technical expertise and implementation experience, and academia can provide independent research and evaluation methodologies. Civil society plays a critical role in ensuring that human rights, equity, and societal impacts remain central to discussions. To support meaningful participation, the Dialogue should adopt a structured, multi-layered format. This could include high-level plenary sessions to align on priorities, supported by smaller thematic working groups focused on specific issues such as interoperability, transparency, and capacity-building. In addition, the Dialogue would benefit from incorporating regional consultations and virtual participation mechanisms to ensure broad geographic inclusion. Structured opportunities for written input and iterative feedback can help capture perspectives beyond those able to participate in person. Finally, the Dialogue should emphasize continuity beyond single events, with clear pathways linking discussions to ongoing working groups, pilot initiatives, and follow-up processes. This would enable stakeholders to contribute not only ideas, but also to support implementation and sustained collaboration over time.

Several voices remain underrepresented in global discussions on AI governance, particularly those from developing countries, small and medium-sized states, and regions with limited technical and policy capacity. These stakeholders often face barriers to participation, including resource constraints, limited access to technical expertise, and fewer opportunities to engage in international forums. In addition, there is a need to more fully include local communities, practitioners, and domain experts who experience the real-world impacts of AI systems, as well as perspectives that reflect diverse cultural, linguistic, and governance contexts. Technical discussions are also often dominated by a narrow set of perspectives, which can limit the consideration of context-specific risks, values, and priorities. This can contribute to governance approaches that are less applicable or effective across different regions. To address these gaps, the AI Dialogue should prioritize inclusive participation mechanisms, including financial and logistical support for participation, multilingual engagement, and regionally distributed consultations. Partnerships with local institutions and networks can help extend outreach and ensure that engagement is grounded in local contexts. In addition, structured mechanisms for ongoing input, such as open consultations and contributions from diverse stakeholder groups, can help ensure that inclusion is sustained beyond individual events.

To foster meaningful and dynamic engagement, the AI Dialogue should incorporate formats that move beyond traditional panel discussions and enable more interactive, outcome-oriented participation. One approach is the use of thematic working labs, where small, diverse groups collaborate on specific challenges such as interoperability, transparency, or capacity-building, producing concrete outputs such as recommendations or draft frameworks. The Dialogue could also incorporate scenario-based exercises, allowing participants to explore how governance approaches perform under different conditions, including cross-border use cases or emerging risk scenarios. This can help translate abstract principles into practical insights. Another effective format is structured multistakeholder roundtables, designed to ensure balanced participation across sectors and regions, with facilitated discussions that lead to clearly documented outcomes. Digital participation tools can further expand engagement by enabling real-time input, polling, and collaborative drafting, allowing contributions from a broader global audience. Finally, the Dialogue could include pilot or demonstration initiatives, where stakeholders test governance approaches or evaluation methods in practice and share lessons learned. This would help bridge the gap between dialogue and implementation. Together, these formats can support a more interactive, inclusive, and results-oriented process that strengthens both participation and impact.

Several existing policies and practices provide valuable foundations for effective AI governance. At the normative level, frameworks such as UNESCO's Recommendation on the Ethics of AI and the OECD AI Principles have established widely recognized baselines for responsible AI, emphasizing human rights, transparency, and accountability. These have been further complemented by regional approaches, such as the EU AI Act, which introduces a risk-based regulatory model that links governance requirements to levels of potential harm. In parallel, technical and institutional practices are emerging that support implementation. These include algorithmic impact assessments, transparency and reporting mechanisms, and structured approaches to human oversight. Such practices help translate high-level principles into actionable governance processes within both public and private sector contexts. There is also growing emphasis on multistakeholder and capacity-building initiatives, including platforms such as ITU's AI for Good, which facilitate knowledge-sharing, technical collaboration, and broader participation in AI governance discussions. An important emerging area is the development of approaches that help reduce fragmentation across governance frameworks, identify gaps, and support more coherent implementation. For example, civil society and research organizations such as the Cyber Institute are working with academic partners across regions to advance methodologies that provide practical implementation guidance and support responsible AI adoption, including through engagement with international initiatives such as ITU's Cyber for Good. These efforts contribute to strengthening multilateral cooperation and advancing shared objectives related to inclusive innovation, institutional capacity, and effective governance, in alignment with the Sustainable Development Goals, particularly SDGs 9, 16, and 17. Finally, effective governance increasingly depends on approaches that are interoperable and context-aware, allowing different frameworks to work together while respecting national legal systems, cultural values, and policy priorities. Taken together, these examples highlight a shift from principle-setting toward practical implementation, coordination, and trust-building, which will be essential to advancing inclusive and globally coherent AI governance.