UNDP-SFDA

A successful first Global Dialogue on AI Governance would, in my view, deliver more than a strong discussion. It should create a credible foundation for ongoing cooperation and become a trusted source of practical guidance for stakeholders globally. Success would include a concise Co-Chairs' Summary that identifies a small number of practical priorities, a shared understanding of the main thematic clusters and how they relate to one another, and concrete examples of solutions, use cases, and good practices that can be carried forward. It would also be valuable if the Dialogue clarifies how inclusivity and stakeholder engagement will work in practice, including how governments, technical experts, civil society, private sector actors, major technology firms, and stakeholders from developing countries can participate meaningfully. Another important outcome would be greater clarity on operational questions that still seem underdefined, including system architecture and operating requirements, expected value-add, how UN entities and other stakeholders plug in, data and cloud stewardship, model lifecycle governance, and decisions on APIs, access rights, standards, licensing, privacy, training rights, and interoperability. It would also be useful if the Dialogue points toward continuity mechanisms beyond the inaugural meeting, such as lighter thematic follow-up tracks, an ongoing exchange space, or a searchable implementation-oriented resource hub. This could reduce fragmentation, improve discoverability, strengthen implementation uptake, and connect guidance to practice through use cases, tools, roadmaps, finance options, monitoring and evaluation approaches, model cards, templates, and lessons learned. In that sense, success should be judged not only by the quality of debate, but by whether the Dialogue creates enough clarity, trust, and practical direction to support useful action between the 2026 and 2027 editions.

Interoperability of governance approaches;Open-source software, open data and open AI models;AI capacity-building;

I selected AI capacity-building, interoperability of governance approaches, and open-source software, open data, and open AI models because these appear to be the most foundational and the most difficult for many countries and institutions to address independently. Capacity-building is urgent because without institutional readiness, skills, infrastructure, and implementation support, many stakeholders will remain observers rather than active participants in AI governance and deployment. Interoperability is equally important because fragmented governance approaches will make it harder for countries, sectors, and institutions to align standards, exchange data safely, and apply guidance consistently across borders and contexts. I also prioritized open-source software, open data, and open AI models because they can help reduce dependency on a small number of providers, improve access for lower-resource settings, and support more equitable innovation and adaptation. These areas are especially important for developing countries and smaller institutions that may otherwise face structural barriers in compute, tools, and technical capacity. The other themes, such as safety, trustworthiness, transparency, accountability, human oversight, and human rights, remain critically important. However, in my view, they are more likely to be supported through existing principles and governance frameworks, whereas capacity-building, interoperability, and open ecosystems require more active coordination, investment, and practical implementation pathways.

Yes. The four proposed themes are broadly sound, but several cross-cutting operational issues appear under-specified rather than fully captured. The current themes cover opportunities and implications, bridging AI divides, safe and trustworthy AI, and human rights, transparency, accountability, and oversight. 1. Implementation architecture: how governance guidance will translate into a practical, living exchange platform rather than remain a static set of principles. This includes resource repositories, implementation roadmaps, financing options, monitoring and evaluation tools, and structured pathways for adaptation across entities and countries. 2. Cloud, data, and stewardship governance: not only open data and open models, but also clear rules on what data may enter AI systems, under what legal basis, for which type of processing, in which cloud or compute environment, and with what retention, monitoring, and accountability arrangements. 3. Model lifecycle governance for continuously evolving systems, especially large multi-modal and generative models. This includes model registries, version tracking, revalidation triggers, risk-tiered use-case approvals, incident reporting and recall, output evaluation logs, and retirement or archiving rules. 4. Interoperability in practice, including APIs, standards, licensing, and integration of local and national data with UN reporting systems. 5. It would be useful to make more explicit the questions of stakeholder discoverability, role-based notifications, and coordinated plug-in roles across UN entities and private-sector actors, so that implementation capacity is not fragmented over time.

In many LMICs and across parts of the Southern Hemisphere, governance gaps in AI capacity-building, interoperability, and open-source / open-data ecosystems are already creating uneven outcomes. Smaller organizations, public-private partnerships, local innovators, and disadvantaged populations often lag in access to knowledge, technical infrastructure, skilled talent, financing, and trusted implementation support. As a result, many actors remain dependent on external tools and models without having the governance, procurement, data, or oversight capacity to adapt them responsibly to local needs. A major challenge is that fragmented governance approaches make it difficult to align standards, share data safely, and integrate local systems with regional or global reporting frameworks. This increases duplication, slows adoption, and can widen existing digital and development divides. Limited access to compute, cloud infrastructure, quality data, and implementation finance further constrains local participation, especially for smaller institutions and locally led initiatives. At the same time, there is a significant opportunity. If capacity-building, interoperability, and open ecosystems are prioritized, AI can become more accessible, locally relevant, and development-oriented. Open-source tools, open models, and open data can help reduce dependency, lower entry barriers, and support innovation in settings that cannot build everything independently. Better interoperability can also enable cross-border learning, more consistent governance approaches, and stronger collaboration between governments, UN entities, academia, civil society, and private sector actors. For my context, the most important opportunity is to ensure that AI governance does not remain concentrated in higher-capacity settings, but becomes usable and actionable for smaller institutions, disadvantaged populations, and locally grounded partnerships as well.

The AI Dialogue can play a valuable role as a neutral multilateral bridge between fragmented governance efforts, helping connect principles, practice, and cooperation across regions, sectors, and stakeholder groups. First, it can support regulatory harmonization by creating a common space for governments, UN entities, technical experts, civil society, academia, and private-sector actors to compare approaches, identify gaps, and build greater coherence across governance models. This is especially important where countries are moving at different speeds and with different levels of capacity. Second, the Dialogue can help shift international cooperation from broad principles alone toward more practical forms of support. This includes sharing use cases, lessons learned, implementation roadmaps, model governance practices, and approaches to data stewardship, interoperability, and capacity-building. In this way, harmonization becomes more actionable and less abstract. Third, it can help ensure that lower-capacity stakeholders, including LMICs, smaller institutions, and disadvantaged populations, are not left behind. By foregrounding access, openness, and implementation support, the Dialogue can reduce fragmentation and support more equitable participation in AI governance. Fourth, it can strengthen continuity. International cooperation on AI governance will need more than annual meetings. The Dialogue could support lighter follow-up tracks, knowledge exchange, and practical resource-sharing between editions, so that momentum is sustained and governance convergence can develop over time. Overall, the Dialogue can add value by serving as a trusted mechanism for coordination, regulatory harmonization, comparability, and practical follow-through, helping international AI governance become more inclusive, coherent, and implementation-oriented.

The AI Dialogue should build on existing initiatives and processes rather than duplicate them. Based on the documents, important foundations include the Global Digital Compact and WSIS+20 as broader governance reference points; UNESCO's Recommendation on the Ethics of AI and related Member State processes; AI for Good as a convening space already linked to the inaugural Dialogue; RightsCon and civil-society consultations on human rights and participation; regional processes such as the ECA–ESCWA regional dialogue; and thematic initiatives such as AI-POL on public trust and responsible AI in sensitive sectors. The added value of the AI Dialogue would be to connect these efforts into a more coherent multilateral process. It could help translate scattered principles, consultations, and sector-specific lessons into a more practical architecture for cooperation across governments, UN entities, civil society, academia, and private sector actors. In particular, it could help make existing resources more discoverable and usable, reduce fragmentation, and clarify where ongoing work is already happening versus where genuine gaps remain. It would also add value if it became a source of practical follow-through, not only dialogue, for example through lighter thematic tracks, a searchable implementation-oriented resource base, or other continuity mechanisms that support use cases, lessons learned, and implementation guidance between the 2026 and 2027 editions. That would make the Dialogue useful not only as a forum for exchange, but as a platform for coherence, continuity, and practical uptake across regions and sectors.

Different stakeholders can contribute in complementary ways. Governments can share policy priorities, implementation challenges, and national experiences. UN entities can contribute normative frameworks, sector expertise, and lessons from implementation. Civil society can bring rights-based, community-level, and accountability perspectives. Academia and technical experts can contribute evidence, standards, evaluation methods, and emerging risks. Private-sector actors can share operational insights, technical constraints, and practical implementation lessons. It would also be valuable to allow multiple contribution channels. These could include short written inputs, regional consultations, curated use cases, case-study submissions, and post-event follow-up. A searchable implementation-oriented resource space linked to the Dialogue would help ensure that contributions do not disappear after the event. Overall, the Dialogue would benefit from being designed not only as a discussion forum, but as a practical coordination mechanism that connects principles, examples, and implementation lessons across stakeholders.

Underrepresented voices often include stakeholders from LMICs and the Global South, smaller public institutions, grassroots civil society, local innovators, youth, women and girls, marginalized communities, persons with disabilities, linguistic minorities, practitioners working in fragile, humanitarian, or low-resource settings, and digitally illiterate or digitally challenged communities. Smaller organizations and public-private initiatives are also often underrepresented because they may lack funding, technical bandwidth, or access to global policy spaces. These perspectives are important because they experience the consequences of governance gaps most directly, but often have the least influence on shaping standards, tools, and implementation pathways. Many disadvantaged populations also face diverse workflow challenges and develop context-specific solutions shaped by unique social, environmental, and resource realities. These experiences can be highly valuable across borders and should be included, not only as examples of vulnerability, but as sources of practical knowledge and adaptation. Inclusion could be improved through regional and sector-specific consultations, low-bandwidth and multilingual participation options, travel and participation support where possible, asynchronous written inputs, and more visible pathways for local case studies and implementation lessons. It would also help to actively seek input from affected communities and frontline institutions, rather than relying only on established global actors. Meaningful inclusion should go beyond symbolic representation. These voices should be reflected in agenda-setting, practical outputs, and follow-up mechanisms, so they shape not only the discussion, but also the resources and guidance that emerge from the Dialogue, especially in ways that remain relevant to ongoing diversity across settings and populations.

A mix of formats would likely work better than plenary statements alone. Short moderated interventions can help surface diverse views efficiently, but they should be complemented by more interactive formats that generate practical outputs. Useful options could include thematic breakout groups, problem-solving roundtables, regional reflection sessions, and implementation-focused clinics built around concrete use cases. Scenario-based discussions could also be valuable, for example exploring how different stakeholders would respond to cross-border data-sharing, model updates, or governance failures in practice. This would make the Dialogue more applied and less abstract. It could also be useful to create a short pre-event exercise or countdown process in the weeks leading up to the Dialogue. This might include brief written prompts, mini-consultations, or curated questions that help build momentum, surface practical issues in advance, and prepare participants for more substantive engagement during the event itself. Another strong format would be curated practice exchanges, where governments, UN entities, civil society, technical actors, and private-sector participants each present one concrete lesson, challenge, or tool. This could be paired with a searchable repository so the value continues beyond the event itself. The Dialogue could also establish special interest groups around different themes or implementation challenges, allowing participants to continue working together between annual sessions. To encourage sustained engagement, it might also consider an annual award or recognition mechanism for meaningful contributions, innovative collaboration, or practical implementation progress. Post-event engagement is just as important. Continued participation could be supported through lighter thematic follow-up tracks, periodic update sessions, or longer-term collaborative initiatives that allow stakeholders to keep contributing lessons, tools, and implementation experiences over time. Overall, the most effective format would combine high-level political framing with smaller, structured, implementation-oriented sessions and pre- and post-event mechanisms that sustain momentum beyond the Dialogue itself.

The strongest examples are those that combine governance principles, readiness, implementation support, and usable tools or data infrastructure. These include the EU AI Act and the EMA reflection paper on AI in the medicinal product lifecycle; the NIST AI Risk Management Framework and FDA guidance and discussion papers on AI for drug development and manufacturing; Microsoft's Responsible AI Standard; UNDP's Artificial Intelligence Landscape Assessment, AILA, which helps countries assess institutional preparedness and define actionable pathways for responsible AI adoption; and WHO's Global Initiative on Digital Health, GIDH, which is relevant as a model for coordinated implementation support because it helps countries plan, build, finance, and govern digital ecosystems around country-led transformation. What makes these examples useful is that they do not stop at high-level principles. They connect policy to readiness, implementation, and practical tools. In my view, effective AI governance works best when it links policy, institutional readiness, implementation support, and discoverable shared resources, including tools and open-model initiatives.