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A successful first Global Dialogue should produce outcomes that are practical, internationally relevant, and useful beyond the meeting itself. In my view, success would mean three things: First, the Dialogue should clarify where international cooperation is most urgently needed. The concept note correctly frames the Dialogue as a platform for cooperation, exchange of best practices, and inclusive discussion. A strong first outcome would therefore be a concise mapping of priority governance gaps where fragmented national or regional approaches are insufficient. Second, it should identify a small number of actionable areas for ongoing work. These could include interoperability of governance approaches, shared approaches to transparency and accountability, and practical cooperation on evaluation, safety, and capacity-building. The Dialogue will be most useful if it helps translate broad principles into workstreams that different actors can actually advance. Third, it should establish a credible bridge between the Scientific Panel's report and policy discussion. The concept note expressly asks how the Dialogue can ensure that scientific findings inform actionable policy discussions. A successful first meeting should therefore produce a forward-looking work programme, endorsed priorities for further consultation, and a basis for continued multistakeholder engagement.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Open-source software, open data and open AI models
• Transparency, accountability, and human oversight

I selected these four priorities because they are, in my view, the areas where international dialogue can add the most immediate value. Safe, secure and trustworthy AI is essential because security and reliability are foundational to any legitimate AI governance framework. Transparency, accountability, and human oversight are equally important, because governance depends not only on technical performance, but also on the ability to assign responsibility, scrutinize decisions, and preserve meaningful human control. These themes are particularly important where AI systems are deployed in sensitive or high-impact contexts. I also selected interoperability of governance approaches because the current landscape is increasingly fragmented. Different legal, regulatory, and technical approaches are emerging across jurisdictions. The Dialogue can play a useful role in identifying where compatibility is possible, even where full harmonization is not. Finally, I selected open-source software, open data and open AI models because openness creates both major opportunities and distinctive governance challenges. In particular, when advanced models are openly released, responsibility may become blurred across the AI value chain. This makes it especially important to discuss release practices, downstream modification, accountability, and security safeguards in an international and multistakeholder setting. Taken together, these four priorities support a governance approach that is both practical and globally relevant.

Yes. One important cross-cutting issue is governance across the full AI value chain. Several listed themes touch parts of this problem, but none explicitly addresses how responsibilities should be allocated among upstream developers, model releasers, downstream modifiers, deployers, and integrators. This question is especially important for open and widely distributed AI models, where control over the system can shift significantly after release. A second emerging issue is the governance of substantial modification. In practice, the risk profile of a system may change materially through fine-tuning, capability extension, removal of safeguards, or integration into other systems. International dialogue would benefit from more explicit discussion of when responsibility should remain with the original provider and when it should shift to downstream actors. A third cross-cutting issue is AI-enabled cyber capability proliferation. AI governance discussions often treat security in general terms, but the cyber domain raises specific challenges, including the lowering of barriers to malicious activity, the rapid diffusion of capabilities, and the need for cooperation on incident information-sharing, evaluation, and response. These issues cut across safety, accountability, interoperability, human rights, and open models, and would strengthen the Dialogue if addressed explicitly.

From the perspective of the European region and the cybersecurity and AI governance sector, the main challenge is that AI capabilities are advancing faster than governance can adapt across the full value chain. This is particularly visible in relation to advanced general-purpose and open-weight models. Once model weights are released, upstream providers may lose meaningful control, while downstream actors can fine-tune, repurpose, or deploy those systems in ways that significantly change their risk profile. This creates uncertainty around responsibility, accountability, and enforcement. A second challenge is fragmentation. Different jurisdictions are developing different legal, technical, and policy responses. For organizations operating across borders, this can create compliance complexity, legal uncertainty, and uneven security practices. In cybersecurity, it also increases the difficulty of coordinating responses to AI-enabled malicious use, including phishing, malware support, and other forms of offensive capability amplification. A third challenge is capacity asymmetry. Not all public institutions, regulators, or smaller organizations have equal access to evaluation tools, technical expertise, compute resources, or incident intelligence. This can widen existing gaps between well-resourced actors and those with fewer means to assess or govern AI systems effectively. At the same time, there are important opportunities. Current developments can support more interoperable governance, common documentation and evaluation practices, and clearer allocation of responsibility across the AI lifecycle. They also create an opportunity to strengthen cooperation on security testing, transparency, and cyber threat information-sharing. If approached well, these developments can help build governance frameworks that improve safety and accountability while still preserving innovation, openness, and international collaboration.

The AI Dialogue can add the most value by serving as an inclusive UN platform that connects fragmented governance efforts and turns broad principles into practical cooperation. The concept note already frames the Dialogue as a space to discuss international cooperation, share best practices, facilitate open and inclusive discussion, and ensure that the Scientific Panel's findings inform actionable policy conversations. In that sense, its role should not be to duplicate existing initiatives, but to help different communities understand where approaches are converging, where gaps remain, and where cooperation is most urgently needed. More specifically, the Dialogue can advance cooperation in three ways. First, it can support interoperability by identifying common governance building blocks across jurisdictions, even where full harmonization is unrealistic. Second, it can elevate issues that require genuinely international discussion, such as governance across the AI value chain, cross-border deployment, capacity asymmetries, and the governance implications of open and widely distributed models. Third, it can create a practical bridge between technical evidence and policy design by linking the Scientific Panel's work to concrete cooperation agendas on safety, accountability, evaluation, incident information-sharing, and capacity-building. Its comparative advantage is legitimacy and inclusiveness. Under UN auspices, the Dialogue can bring together Member States, industry, technical experts, academia, and civil society, including actors from developing countries that are often underrepresented in smaller governance forums. A successful Dialogue should therefore function as a coordination layer: not replacing other processes, but helping align them around shared priorities, practical cooperation, and a more globally representative governance conversation.

The Dialogue should build on existing initiatives that already provide substantive foundations for AI governance. These include the UNESCO Recommendation on the Ethics of Artificial Intelligence, which applies to all UNESCO Member States; the OECD AI Principles and the integrated OECD.AI/GPAI ecosystem, which provide policy principles, data, and expert networks; the G7 Hiroshima AI Process, including its code of conduct for organizations developing advanced AI systems; and the Council of Europe Framework Convention on AI, which is the first international legally binding treaty in this field. Since the inaugural Dialogue will take place alongside the AI for Good Global Summit, it should also connect with ITU-led work on standards, skills, and practical multistakeholder engagement. The added value of the AI Dialogue would be different from each of these initiatives. It can provide a broader and more inclusive umbrella under UN auspices, especially by bringing together countries and stakeholders that are not always central in club-based, regional, or sector-specific processes. It can also help connect normative frameworks, technical standardization efforts, policy experimentation, and capacity-building initiatives that are currently developing in parallel. In practical terms, the Dialogue should add value by mapping points of convergence, highlighting unresolved governance gaps, and identifying areas for cooperative follow-up. This is particularly important for cross-cutting issues that no single initiative fully resolves, such as interoperability across governance regimes, responsibility across the AI value chain, and the governance of open-source and open-weight models. The Dialogue can therefore act as a coordination and gap-identification mechanism that strengthens, rather than duplicates, the broader AI governance ecosystem.

Different stakeholders should contribute in differentiated but complementary ways. Member States should identify policy priorities, governance gaps, and areas where international cooperation is feasible. International organizations should help connect existing processes, standards, and capacity-building efforts. The private sector should contribute operational experience on development, deployment, safety practices, and incident response. Civil society should bring perspectives on rights, equity, accountability, and affected communities. Academia and the technical community should provide evidence on capabilities, limitations, evaluation methods, and emerging risks. This would align well with the Dialogue's intended multistakeholder and inclusive character. In terms of structure, the Dialogue should combine a high-level segment with smaller, problem-focused sessions. A useful format would be: first, a plenary framing the main governance challenges; second, thematic working sessions organized around a limited number of concrete clusters; and third, a closing segment focused on practical outputs and follow-up. Each thematic session should be guided by a short issues paper and a limited set of questions, to avoid broad but inconclusive discussion. To make the Dialogue more useful, each session should aim to identify: areas of convergence, areas of divergence, issues requiring further evidence, and opportunities for cooperation. The Scientific Panel's findings should be integrated into the thematic discussions rather than treated as a separate track. A concise Co-Chairs' summary and a forward-looking work programme would help ensure continuity after the first meeting.

Several perspectives remain underrepresented in global AI governance discussions. First, stakeholders from developing countries, especially those with limited access to compute, technical infrastructure, or specialized policy capacity, are often not represented on equal terms. Second, smaller civil society organizations, independent technical researchers, and public-interest experts from outside major transatlantic and large-industry ecosystems are frequently less visible. Third, practitioners working at the intersection of AI and cybersecurity, digital forensics, incident response, and infrastructure security are still not consistently present in broader governance conversations. Fourth, communities most directly affected by language exclusion, accessibility barriers, or weak digital infrastructure are often discussed but not adequately heard. The Dialogue's stated objective of openness, inclusiveness, and attention to digital divides makes this especially important. Inclusion should therefore be designed into the process, not treated as an afterthought. This means funding or facilitating remote participation, ensuring geographical and linguistic balance in speaker selection, publishing concise background materials in accessible form, and reserving speaking slots for underrepresented stakeholder groups. It would also be helpful to invite structured written submissions from organizations that cannot attend in person and to summarize those contributions transparently in the official outputs. Meaningful inclusion also requires thematic inclusion. Issues such as capacity asymmetries, local infrastructure constraints, multilingual AI, and downstream deployment impacts should be treated as central governance questions, not peripheral ones.

To foster meaningful engagement, the Dialogue should move beyond a sequence of formal statements and include formats that produce interaction, comparison, and concrete outputs. One useful format would be moderated roundtables organized around a single practical question, such as where responsibility should lie across the AI value chain or which areas of governance interoperability are most achievable in the near term. Another would be short "evidence-to-policy" sessions in which technical experts present a concrete finding and policymakers or practitioners respond with governance implications. This would help connect the Scientific Panel's work to actionable discussion, which the concept note explicitly identifies as a priority. A second promising format would be structured multistakeholder breakout groups, each tasked with producing a brief set of takeaways: points of convergence, unresolved questions, and suggested follow-up actions. These outcomes could feed directly into the Co-Chairs' summary. A third option would be scenario-based discussions built around realistic cross-border cases, such as misuse of open models, failures of transparency, or challenges in capacity-building and access. Case-based discussion is often more productive than abstract debate because it reveals where governance approaches actually work or break down. Finally, digital participation tools should be used to widen engagement: advance written inputs, live synthesis of stakeholder views, and post-session comment windows. This would support the Dialogue's open and inclusive mandate while making the process more dynamic and policy-relevant.

Examples that are especially useful for effective AI governance combine normative principles, operational tools, and mechanisms for international coordination. First, the UNESCO Recommendation on the Ethics of Artificial Intelligence provides a global normative baseline centred on human rights, human dignity, transparency, accountability, and human oversight, and applies across UNESCO's Member States. Second, the OECD AI Principles are a strong example of interoperable governance because they offer a widely used intergovernmental framework for trustworthy, human-centred AI, and were updated in 2024 to reflect developments such as general-purpose and generative AI. Third, the NIST AI Risk Management Framework is a practical model for implementation. Its "Govern, Map, Measure, Manage" structure helps organizations move from abstract principles to concrete risk-management practices across the AI lifecycle. Fourth, the Council of Europe Framework Convention on AI and human rights, democracy and the rule of law is important because it is the first international legally binding treaty in this field and takes a lifecycle-based approach grounded in rights and the rule of law. Fifth, the Hiroshima AI Process Reporting Framework, supported by the OECD, is a promising example of a practical transparency mechanism. It provides a common voluntary structure for organizations developing advanced AI systems to disclose governance and risk-management practices. Finally, platforms such as the OECD AI Incidents and Hazards Monitor (AIM) are valuable because evidence on incidents and harms is essential for better policy, shared learning, and international cooperation.