YSU

A successful first Global Dialogue on AI Governance would be defined by practical alignment, not lofty consensus: Shared baseline: Minimal agreement on key definitions and safety principles among the European Union, United States, and China. Interoperability: Steps to make frameworks like the EU AI Act compatible with others. Technical coordination: A standing expert mechanism linked to bodies like the OECD. Risk cooperation: An incident-sharing system for AI failures or misuse. Global inclusion: Meaningful participation from countries like India and Brazil. Concrete roadmap: Clear next steps, timelines, and accountability.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• AI capacity-building
• Interoperability of governance approaches

A coherent choice _especially from a European policy perspective _ would prioritize the following four areas, as they combine urgency, strategic relevance, and global impact: 1. Safe, secure and trustworthy AI This is foundational. Without credible safety, robustness, and risk mitigation, trust in AI systems-and in governance itself-erodes. It aligns directly with the risk-based approach of the EU AI Act and positions your engagement within a framework already shaping global norms. 2. Interoperability of governance approaches Fragmentation is the main systemic risk. Ensuring that approaches taken by the European Union, United States, and China can function together-even without full alignment-is essential to avoid regulatory conflict and enable global innovation. This is where diplomatic and technical leadership is most needed. 3. AI capacity-building Global legitimacy depends on inclusion. Supporting capacity-building-especially in emerging economies-helps prevent a two-tier AI world dominated by a few actors. It also reinforces partnerships with countries such as India and Brazil, strengthening both development and geopolitical balance. 4. Social, economic, ethical, cultural, linguistic and technical implications of AI AI is not only a technological issue; it reshapes labor markets, democratic processes, and cultural production. Addressing these broader impacts ensures governance remains politically sustainable and socially grounded, rather than purely technical. In essence: these four priorities balance risk control (safety), system coherence (interoperability), global equity (capacity), and societal legitimacy (impact) _ a combination necessary for credible and durable AI governance.

Yes - several cross-cutting issues remain insufficiently captured, yet will decisively shape AI governance outcomes. First, compute and infrastructure governance. Access to advanced chips, cloud capacity, and large-scale training resources is becoming the primary source of power. Without addressing these structural asymmetries, governance risks reinforcing dominance by actors in the European Union, United States, and China. Second, the role of private actors. Frontier AI is largely developed by companies rather than states. Governance frameworks that do not systematically integrate actors such as OpenAI or Google DeepMind risk remaining aspirational rather than operational. Third, security convergence. AI is increasingly intertwined with cybersecurity, disinformation, and hybrid threats. Governance discussions often remain siloed, while real-world risks cut across these domains, requiring closer alignment with security and defense frameworks. Fourth, data asymmetries. Beyond privacy, unequal access to high-quality, diverse datasets affects who can build competitive systems. This raises concerns about data concentration, dependency, and fair participation in the global AI ecosystem. Fifth, enforcement and verification. Many initiatives emphasize principles but lack credible compliance mechanisms. Without auditability, monitoring tools, and enforcement capacity, even advanced regulatory models-such as the EU AI Act-may have limited global impact. Finally, environmental sustainability. The growing energy and water demands of large-scale AI systems are emerging as a governance issue, intersecting with climate objectives and resource constraints. Overall, the next phase of AI governance will depend less on norms alone and more on how effectively it addresses infrastructure, power asymmetries, and enforceability.

Governance gaps across the selected priorities are already shaping both the constraints and the leverage of the European Union. Challenges Fragmentation and external dependence. While the EU AI Act positions the EU as a regulatory leader, gaps in interoperability with the United States and China risk isolating European firms. Divergent standards increase compliance costs and may reduce the global scalability of EU-based innovation. Limited technological sovereignty. Governance ambition is not yet matched by capacity in compute, data, and frontier model development. This creates structural dependence on non-EU providers, weakening the EU's ability to enforce its own standards globally. Enforcement complexity. Translating principles into practice _especially across 27 Member States _ remains difficult. Uneven implementation could undermine credibility and create internal market fragmentation. Societal pressure and legitimacy risks. AI's impact on jobs, public services, and democratic processes is accelerating faster than policy adaptation, exposing gaps between regulatory intent and citizens' expectations. Opportunities The "Brussels effect" in AI. If effectively implemented, the EU AI Act can globalize EU standards, particularly in high-risk sectors, shaping corporate behavior beyond Europe. Leadership in trusted AI. By prioritizing safety, transparency, and rights-based governance, the EU can differentiate itself as the global benchmark for "trustworthy AI," influencing international norms and partnerships. Coalition-building through interoperability. The EU is well placed to act as a bridge-builder, promoting convergence with like-minded partners while maintaining dialogue with major powers. Strategic capacity-building. Investments in skills, infrastructure, and partnerships _ especially with emerging economies _ can expand the EU's influence and reduce long-term dependencies. In sum, the EU faces a structural tension: regulatory leadership without full technological control—but this also creates space for strategic norm-setting and alliance-building.

The AI Dialogue can serve as a pragmatic coordination platform _ not to resolve geopolitical differences, but to make them manageable. First, as a bridge between regulatory systems. It can facilitate convergence _ where possible _ and compatibility _ where necessary _ between major approaches, notably those of the European Union, United States, and China. Even limited alignment on definitions, risk classifications, or testing protocols would reduce fragmentation and compliance burdens. Second, as a hub for technical standard-setting. By connecting policymakers with experts and standardization bodies such as the OECD, the Dialogue can accelerate the development of shared benchmarks for safety, evaluation, and auditing _ turning abstract principles into operational tools. Third, as a confidence-building mechanism. In a context of strategic rivalry, structured exchanges on AI risks _ such as incident reporting or model safety practices _ can build predictability and reduce escalation risks, particularly in sensitive domains like cybersecurity and information integrity. Fourth, as an inclusion platform. The Dialogue can ensure that emerging and developing economies are not passive recipients of rules but active contributors. Supporting participation from countries such as India and Brazil strengthens legitimacy and promotes more balanced global governance. Fifth, as a catalyst for coalitions. Rather than seeking universal consensus, the Dialogue can enable flexible coalitions of willing actors to advance specific initiatives _ on safety, interoperability, or capacity-building _ while remaining open to broader participation over time. In essence, the AI Dialogue's value lies in shifting from fragmented national approaches toward structured interdependence—creating the minimal conditions for cooperation in a competitive geopolitical environment.

The AI Dialogue should not start from scratch, but rather connect and amplify existing initiatives across regulatory, technical, and multilateral domains. Key initiatives to build upon Norm-setting and policy coordination: The OECD AI Principles and the UNESCO Recommendation on AI provide widely endorsed global baselines. Standardization efforts: Technical work by ISO and IEEE is essential for operationalizing safety, risk, and interoperability. Multistakeholder governance platforms: The Global Partnership on AI (GPAI) and the G7 Hiroshima AI Process offer models for bridging governments, industry, and academia. Regulatory leadership: The EU AI Act and emerging U.S. and Chinese frameworks provide concrete governance experiments. Frontier AI safety initiatives: The AI Safety Institute and related national efforts are beginning to shape evaluation and risk-testing practices. Added value of the AI Dialogue First, coherence across silos. Existing initiatives are fragmented _ normative, technical, regional, or sectoral. The Dialogue can act as a meta-platform, aligning these efforts and avoiding duplication. Second, political anchoring of technical work. Many standards lack high-level political backing. The Dialogue can connect technical outputs (e.g., ISO standards) with political commitments, increasing uptake and legitimacy. Third, global inclusivity. Current initiatives are often dominated by advanced economies. The Dialogue can broaden participation, ensuring that developing countries shape _ not just adopt _ AI governance norms. Fourth, continuity and accountability. By establishing follow-up mechanisms, timelines, and reporting, the Dialogue can transform voluntary principles into iterative governance processes. In sum, its added value lies in turning a scattered ecosystem into a more coordinated, legitimate, and action-oriented architecture for global AI governance.

Effective AI governance requires structured contributions from all stakeholder groups, combined with a format that balances political direction and technical depth. Stakeholder contributions Governments (e.g., European Union, United States, China): provide political commitment, align regulatory approaches, and ensure implementation capacity. Private sector (e.g., OpenAI, Google DeepMind): contribute technical expertise, share safety practices, and support standard development. International organizations (e.g., OECD, UNESCO): offer normative frameworks, data, and coordination platforms. Academia and research institutes: provide independent evaluation, foresight, and evidence-based policy input. Civil society: ensure accountability, inclusion, and protection of fundamental rights, particularly for vulnerable groups. Recommendations on format and structure 1. Multi-layered architecture. Combine: A high-level political track (ministerial or leadership level) to set priorities A technical track with experts and standards bodies to develop operational tools A multistakeholder forum to ensure inclusiveness and legitimacy 2. Thematic working groups. Organize around key priorities (safety, interoperability, capacity-building), with clear mandates and deliverables. 3. Iterative process with timelines. Move beyond one-off dialogue: establish regular cycles, reporting mechanisms, and measurable outputs. 4. Flexible coalitions. Allow "coalitions of the willing" to advance specific initiatives while keeping the process open and scalable. 5. Integration with existing frameworks. Ensure structured links with initiatives led by bodies like the OECD to avoid duplication. In essence, the Dialogue should function as a hybrid governance platform—politically anchored, technically grounded, and inclusively structured—to translate discussion into coordinated global action.

Several voices remain structurally underrepresented in global AI governance, limiting both legitimacy and effectiveness. 1. The Global South Countries beyond major powers—particularly in Africa, Latin America, and parts of Asia—are often rule-takers rather than rule-shapers. While actors like India or Brazil are increasingly visible, many others lack access to technical expertise and negotiating capacity. Inclusion: dedicated funding for participation, regional hubs, and capacity-building partnerships embedded in processes led by the United Nations. 2. Smaller economies within advanced regions Even within the European Union, smaller Member States and less digitally advanced economies have limited influence compared to larger players. Inclusion: structured coordination mechanisms and expert pools to amplify collective representation. 3. Civil society and affected communities Voices representing workers, minorities, and vulnerable groups are often consulted late, if at all. This creates gaps between governance frameworks and societal realities. Inclusion: formal consultation rights, citizen assemblies, and integration into decision-shaping—not just feedback—phases. 4. Technical communities beyond major firms AI discourse is dominated by large companies such as OpenAI, while independent researchers, open-source communities, and smaller labs remain marginal. Inclusion: support for open research ecosystems and structured engagement channels in standard-setting. 5. Interdisciplinary expertise Governance is still heavily tech- and policy-driven, underrepresenting social scientists, ethicists, and cultural experts. Inclusion: mandatory interdisciplinary panels within initiatives coordinated by bodies like OECD or UNESCO. In sum, inclusion requires moving from ad hoc consultation to institutionalized participation, backed by resources, representation, and real influence over outcomes.

To ensure meaningful and dynamic engagement, the AI Dialogue should move beyond traditional conference formats and adopt innovative, interactive, and multi-layered approaches. 1. Multi-stakeholder workshops and labs Thematic "labs" can bring together governments, industry, academia, and civil society to collaboratively tackle specific challenges _e.g., AI safety, interoperability, or data access. Using design-thinking and scenario-based exercises encourages concrete problem-solving rather than abstract debate. 2. Simulation and tabletop exercises Replicating potential AI crises _ such as model failures, misuse, or cross-border security incidents—can help participants understand systemic risks, coordination gaps, and the consequences of delayed action. This builds shared situational awareness among technical, regulatory, and political actors. 3. Iterative, hybrid dialogue tracks A combination of high-level plenaries, deep-dive technical sessions, and virtual consultations allows continuous engagement. Digital platforms can host asynchronous contributions, enabling smaller actors _ including those from the Global South or independent research communities—to participate meaningfully. 4. Citizen and civil society engagement modules Structured citizen assemblies, online deliberation platforms, or "AI impact labs" give affected communities a voice, particularly on societal, ethical, and cultural dimensions. Insights can feed directly into policy recommendations. 5. Hackathons and challenge-driven formats Short, focused competitions on problems like model transparency, bias detection, or interoperability testing can mobilize technical talent, generate tangible outputs, and foster collaboration across borders. 6. Coalition-building and peer learning forums Encouraging "coalitions of the willing" to work on specific initiatives _ such as AI audit standards or incident reporting—can create momentum and demonstrate practical progress, while remaining open for others to join. In essence, the most effective engagement formats combine interactivity, cross-disciplinary collaboration, inclusivity, and output-orientation. By blending political deliberation with hands-on technical and societal exercises, the Dialogue can produce both shared understanding and actionable solutions.

Several policies, practices, and platforms already provide concrete approaches to AI governance, offering lessons for global dialogue. 1. Regulatory frameworks: The EU AI Act is a leading example of risk-based regulation, categorizing AI applications by potential harm and imposing proportional obligations on developers and deployers. Its emphasis on transparency, human oversight, and accountability creates clear expectations while allowing innovation in lower-risk areas. 2. Multistakeholder platforms: The Global Partnership on AI (GPAI) and the OECD AI Principles offer structured forums where governments, industry, and academia co-develop guidelines and best practices. GPAI, for instance, enables research collaboration on trustworthy AI, ensuring standards reflect practical realities. 3. Technical standardization: ISO/IEC standards for AI, as well as initiatives from the IEEE, provide concrete methods to assess robustness, transparency, and safety. These standards help operationalize abstract principles into verifiable practices for model development, auditing, and deployment. 4. Risk management and incident reporting: Some national AI safety initiatives, including those in the UK and Canada, are experimenting with confidential AI incident reporting systems. These mimic aviation or cybersecurity practices, allowing stakeholders to share near-misses or model failures without immediate punitive consequences, fostering learning and trust. 5. Capacity-building and inclusion programs: The UNESCO and regional AI hubs support skill development, regulatory training, and infrastructure access in underrepresented regions. Such programs promote equitable participation and reduce global AI asymmetries. 6. Corporate governance practices: Companies like OpenAI and DeepMind have internal AI safety boards, red-teaming exercises, and external audits to identify risks proactively, demonstrating practical accountability mechanisms. In summary, these examples show that effective AI governance combines regulation, standards, multistakeholder collaboration, transparency, risk management, and inclusive capacity-building, forming an ecosystem that balances innovation with safety and societal trust.