Microsoft

Success for the first Global Dialogue on AI Governance should be measured less by grand declarations and more by whether it creates durable mechanisms for cooperation. A successful outcome would begin with a shared baseline understanding of AI risks and opportunities. Countries differ in priorities, but agreement on core principles safety, human rights, transparency, accountability, inclusion, and innovation would provide a foundation for future work. Second, the Dialogue should produce practical commitments rather than vague statements. Examples include channels for rapid information-sharing on frontier model incidents, voluntary reporting norms for major developers, and cooperation on technical standards such as model evaluation, cybersecurity, provenance, and interoperability. Even nonbinding commitments can shape behavior if they are specific and measurable. Third, success requires meaningful global inclusion. AI governance cannot be designed only by a few advanced economies or large technology firms. Developing countries need a real voice on issues such as infrastructure access, talent development, language representation, and equitable distribution of AI benefits. Civil society, academia, and industry should also have structured participation. Fourth, the meeting should reduce regulatory fragmentation. Full harmonization is unrealistic, but greater compatibility among national approaches would lower compliance burdens, support innovation, and prevent gaps that bad actors exploit. A roadmap for ongoing dialogue between regulators would be valuable. Fifth, the Dialogue should establish continuity. If participants leave with no follow-up process, momentum will fade. Success would include a timetable for future meetings, working groups on priority topics, and a mechanism to track progress. Finally, the strongest sign of success would be trust. If governments with different political systems and strategic interests can still cooperate on AI safety and shared prosperity, that would signal that governance is possible even amid competition. In that sense, the first Dialogue succeeds not by solving every issue immediately, but by proving that sustained international coordination on AI is achievable.

• Interoperability of governance approaches
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Safe, secure and trustworthy AI
• Open-source software, open data and open AI models

My selected priorities reflect the need to ensure that AI develops in ways that are safe, broadly beneficial, and internationally coordinated. First, safe, secure and trustworthy AI is essential because public confidence depends on systems that are reliable, resilient, and aligned with human values. This includes robust testing, cybersecurity safeguards, risk management, and mechanisms to prevent misuse or harmful unintended consequences. Second, the social, economic, ethical, cultural, linguistic and technical implications of AI require urgent attention because AI is already reshaping labor markets, education, healthcare, media, and public services. Governance frameworks should address fairness, bias, inclusion, cultural diversity, and equitable access, while ensuring that smaller economies and underrepresented languages are not left behind. Third, interoperability of governance approaches is a practical priority in a world where AI systems operate across borders. Divergent national rules may create fragmentation, increase compliance costs, and leave regulatory gaps. Greater compatibility among standards, terminology, risk frameworks, and assurance mechanisms would support innovation while strengthening accountability and international cooperation. Fourth, open-source software, open data and open AI models can accelerate innovation, research, education, and participation in the digital economy. Open ecosystems can lower barriers to entry for startups, researchers, and developing countries. At the same time, openness should be accompanied by proportionate safeguards, responsible release practices, and clear guidance for higher-risk capabilities. Taken together, these four priorities balance innovation with responsibility. They recognize that AI governance should not only mitigate risks, but also expand opportunity, foster inclusion, and promote collaboration across countries and sectors. Effective global dialogue should therefore focus on building trust, enabling shared standards, and ensuring that the benefits of AI are widely distributed.

Yes. While the listed themes cover many core areas, several cross-cutting and emerging issues deserve explicit attention. First, frontier AI governance and compute concentration should be highlighted. The development of the most advanced systems depends on large-scale computing infrastructure, specialized chips, cloud capacity, and proprietary data. Concentration of these resources in a small number of firms and countries may create strategic dependencies, unequal access, and barriers to competition. Governance discussions should therefore consider fair access, resilience, and responsible oversight of frontier capabilities. Second, environmental sustainability is increasingly important. Training and deploying large AI models can require significant energy, water, and hardware resources. Policymakers should encourage transparency around environmental impacts, efficiency standards, and the use of cleaner energy sources. Third, labor market transition and workforce adaptation merit separate focus. Beyond broad socioeconomic impacts, AI may rapidly change job tasks, demand new skills, and alter bargaining power in many sectors. Governments, industry, and educational institutions should coordinate on reskilling, lifelong learning, and transition support. Fourth, information integrity and democratic resilience need sustained attention. Generative AI can accelerate disinformation, fraud, impersonation, and manipulation at scale. This requires stronger provenance tools, media literacy, platform responsibility, and cross-border cooperation. Fifth, measurement and implementation capacity are often overlooked. Many countries may agree on principles but lack the institutions, technical expertise, testing infrastructure, or regulatory capacity to operationalize them. Practical support for implementation is therefore as important as norm-setting. Finally, governance agility itself is an emerging issue. AI evolves faster than traditional policymaking cycles, so institutions need adaptive mechanisms such as periodic review, regulatory sandboxes, horizon scanning, and rapid expert consultation. These issues cut across existing themes and would strengthen a future-oriented global AI governance agenda.

Governance gaps and uneven advances in the selected thematic areas are creating both risks and opportunities across countries, regions, and sectors. The most significant challenge in safe, secure and trustworthy AI is the uneven maturity of risk management practices. Many organizations are adopting AI faster than they can evaluate reliability, cybersecurity vulnerabilities, privacy risks, or downstream harms. Smaller firms and public institutions often lack the technical capacity to conduct audits, red-team testing, or continuous monitoring. This can slow adoption in sensitive sectors such as healthcare, finance, education, and public administration. In relation to the social, economic, ethical, cultural, linguistic and technical implications of AI, benefits are not being distributed evenly. Regions with stronger digital infrastructure, capital, and talent capture disproportionate gains, while others risk widening productivity gaps. Underrepresented languages and local cultural contexts remain underserved in many AI systems, limiting usability and inclusion. At the same time, AI creates major opportunities to expand access to education, healthcare, translation, and public services when adapted to local needs. For interoperability of governance approaches, fragmented regulatory models create uncertainty for companies operating across borders. Different rules on data use, liability, standards, and compliance increase costs, especially for startups and small enterprises. However, convergence around common frameworks, technical standards, and mutual recognition could unlock innovation, trade, and safer cross-border deployment. Regarding open-source software, open data, and open AI models, the opportunity is broader participation in innovation. Researchers, startups, and developing economies can build solutions without relying entirely on a few dominant providers. Yet gaps remain around quality control, security, misuse prevention, and sustainable funding for open ecosystems. Overall, the central challenge is ensuring that governance keeps pace with technological change without suppressing innovation. The central opportunity is to use AI to accelerate inclusive growth, improve public services, and strengthen competitiveness if trust, capability, and coordination are built in parallel.

The AI Dialogue can play a valuable role as a neutral, inclusive platform for building practical international cooperation on AI governance. Because AI development and deployment are inherently cross-border, no single country can effectively address all opportunities and risks alone. The Dialogue can help align diverse national approaches while respecting different legal systems, development levels, and policy priorities. First, it can foster trust and shared understanding among governments, industry, academia, and civil society. Many disagreements stem from differing assumptions about risk, innovation, security, or human rights. Regular dialogue can narrow these gaps and create a common vocabulary for discussing frontier models, safety, accountability, and economic impacts. Second, the Dialogue can support interoperability of governance frameworks. Full harmonization may be unrealistic, but countries can still cooperate on baseline principles, technical standards, evaluation methods, incident reporting practices, and assurance mechanisms. Greater compatibility would reduce fragmentation, lower compliance costs, and strengthen accountability. Third, it can elevate the voices of developing countries and underrepresented regions. International AI governance will be more legitimate and effective if all countries can shape norms on access, infrastructure, language inclusion, talent development, and equitable benefit-sharing. The Dialogue can help prevent a widening global AI divide. Fourth, it can catalyze capacity-building and knowledge exchange. Many governments need support on regulatory design, technical expertise, procurement standards, and institutional readiness. Sharing best practices and lessons learned can accelerate responsible adoption. Fifth, the Dialogue can provide an early-warning and coordination function for emerging risks such as misuse, cybersecurity threats, market concentration, and disinformation. Rapid information-sharing channels and expert working groups would be valuable. Ultimately, the AI Dialogue should not be only a discussion forum. Its greatest value would come from translating conversation into sustained cooperation through roadmaps, voluntary commitments, technical working groups, and measurable follow-up actions. If it helps countries compete responsibly while cooperating where interests overlap, it will be a meaningful contribution to global AI governance.

The AI Dialogue should build upon existing international, regional, multistakeholder, and technical initiatives rather than duplicate them. A strong foundation already exists. Key examples include the United Nations system's work on digital cooperation and sustainable development; UNESCO Recommendation on the Ethics of Artificial Intelligence; the OECD AI Principles and policy observatory; the G7 Hiroshima AI Process; the Global Partnership on AI; standards bodies such as ISO, IEC, and IEEE; as well as regional frameworks such as the European Union AI Act and African Union digital and AI strategies. Industry-led safety initiatives, research networks, and open-source communities should also be connected. The added value of the AI Dialogue would be its ability to convene these efforts in one inclusive global forum with universal legitimacy. Many current initiatives are geographically limited, sector-specific, or focused on like-minded participants. The Dialogue can bridge gaps between advanced and developing economies, regulators and innovators, and public and private actors. It can also improve coherence by mapping overlaps, identifying governance gaps, and encouraging interoperability across standards, terminology, risk classifications, and assurance practices. This would reduce fragmentation and help countries with limited capacity navigate a crowded governance landscape. A further contribution would be implementation support. The Dialogue could connect countries to technical assistance, policy toolkits, training, and peer learning. Finally, it can provide continuity through regular review of emerging risks such as frontier models, compute concentration, cybersecurity, labor disruption, and disinformation. In short, the AI Dialogue should function as a coordinator, amplifier, and bridge-builder that turns existing efforts into a more coherent global governance ecosystem.

Different stakeholders should contribute through clearly defined, complementary roles that combine expertise, legitimacy, and practical experience. Governments should provide policy leadership, identify national priorities, share regulatory lessons, and negotiate common principles or cooperative mechanisms. International organizations can supply convening power, comparative research, technical assistance, and links to broader development goals. Industry should contribute technical knowledge, deployment experience, safety practices, and resources for implementation, while being subject to balanced transparency expectations. Academia and independent researchers can provide evidence-based analysis, model evaluations, foresight, and policy design ideas. Civil society should represent affected communities, human rights concerns, labor interests, consumer protection, and inclusion perspectives. Technical standards bodies can help translate policy goals into measurable and interoperable standards. Developing countries and smaller economies should have dedicated channels to raise capacity, infrastructure, language, and access priorities. For format and structure, the AI Dialogue should be multistakeholder, action-oriented, and continuous rather than a one-time conference. First, establish an annual ministerial-level plenary to set priorities, review progress, and endorse recommendations. Second, create permanent thematic working groups on areas such as AI safety, interoperability, open ecosystems, inclusion, labor transitions, and capacity-building. These groups should include balanced representation from governments, industry, academia, and civil society. Third, include regional consultations before each plenary so local priorities inform the global agenda. Fourth, develop a structured consultation mechanism allowing written submissions, expert roundtables, youth participation, and public comment periods. Fifth, create a voluntary implementation track where countries and organizations can share commitments, pilot projects, and measurable progress. Sixth, maintain a small secretariat to coordinate agendas, publish reports, track outcomes, and support participation from lower-capacity states. Finally, decision-making should prioritize consensus where possible, with nonbinding recommendations when consensus is not feasible. Transparency, published outputs, and clear follow-up timelines are essential. A successful AI Dialogue should therefore combine high-level political attention with technical depth, inclusive participation, and practical delivery.

Several important voices remain underrepresented in global discussions on AI governance, which can weaken both legitimacy and policy effectiveness. First, developing countries and smaller economies are often underrepresented despite being significantly affected by AI adoption, data governance choices, and shifting global competitiveness. Many face constraints in technical expertise, negotiating capacity, and participation resources. Second, communities from the Global South and speakers of low-resource languages are insufficiently represented. AI systems are frequently optimized for dominant languages and markets, which can marginalize local knowledge, cultural contexts, and linguistic diversity. Third, workers and labor representatives need stronger inclusion, especially from sectors likely to be transformed by automation and augmentation. Their perspectives are essential for designing fair transition policies, skills strategies, and worker protections. Fourth, small and medium-sized enterprises, startups, and open-source communities are often overshadowed by large technology firms. Yet they are critical sources of innovation and can provide practical insight into how regulation affects competition and market entry. Fifth, civil society organizations representing consumers, children, persons with disabilities, migrants, indigenous communities, and other affected groups are not consistently engaged. Their lived experience is essential for identifying harms that technical or state actors may overlook. Sixth, independent researchers and institutions from outside major technology hubs need more visibility, particularly those working on public-interest technology, ethics, and development applications. To include these voices, participation must be designed intentionally. This includes funded travel support, remote participation, multilingual interpretation, open calls for submissions, transparent stakeholder selection, and reserved seats in working groups. Regional consultations should feed directly into global agendas, rather than being symbolic side events. Capacity-building support can help lower-resourced countries engage substantively. The Dialogue should also publish consultation summaries showing how external input shaped outcomes. Inclusion should be measured not only by attendance, but by whether underrepresented groups can influence priorities, language, and final recommendations. Effective AI governance requires those most affected to help shape the rules.

Meaningful engagement is more likely when the AI Dialogue moves beyond traditional speeches and panel discussions toward interactive, problem-solving formats. First, moderated policy labs could bring governments, industry, academia, and civil society together to work on specific challenges such as frontier model safety, interoperability standards, or workforce transition strategies. Small-group drafting sessions often produce more concrete outcomes than plenaries. Second, scenario simulation exercises would be valuable. Participants could respond to realistic cross-border incidents such as AI-enabled cyberattacks, large-scale disinformation campaigns, model failures in critical infrastructure, or sudden labor market disruption. Simulations help reveal governance gaps, coordination needs, and decision bottlenecks. Third, multistakeholder roundtables using the Chatham House Rule can encourage candid discussion on sensitive issues like national security, market concentration, or regulatory trade-offs, while reducing performative diplomacy. Fourth, innovation showcases and "policy demo days" could allow startups, researchers, public agencies, and open-source communities to present practical tools for safety testing, auditing, language inclusion, accessibility, and public-service delivery. This keeps the Dialogue connected to implementation. Fifth, structured public consultations should run alongside the main event through digital platforms where stakeholders can submit proposals, vote on priorities, and comment on draft recommendations. This broadens participation beyond those physically present. Sixth, youth assemblies and citizen panels can surface societal expectations that experts may miss, especially regarding education, employment, and trust. Seventh, matchmaking sessions could connect countries needing support with institutions offering technical assistance, funding, or training. For structure, the Dialogue could combine a high-level plenary, parallel working sessions, regional caucuses, and closing sessions that adopt clear next steps. Outputs should be captured in live dashboards tracking proposals, consensus areas, and commitments. The most effective format is one that balances diplomacy with practical collaboration: fewer speeches, more co-creation; fewer abstract principles, more tested solutions; and broader participation linked to measurable follow-up.