Anthropic PBC, EC, ECB, Sapienza Università di Roma, Princeton IAS, UN

A successful first Global Dialogue on AI Governance should move beyond high-level consensus and deliver operational convergence across jurisdictions. Three outcomes are particularly critical: First, the establishment of shared governance baselines translating principles (e.g. safety, human rights, accountability) into auditable controls, metrics, and lifecycle processes. This includes common taxonomies for AI risk, evaluation methodologies, and post-deployment monitoring. Second, the creation of interoperability mechanisms between existing frameworks (e.g. EU AI Act, NIST AI RMF, OECD principles), enabling organizations to map compliance efforts across regimes. Without interoperability, governance fragmentation will increase systemic risk and regulatory inefficiency. Third, concrete commitments to capacity-building, particularly for developing countries. This should include access to technical infrastructure, evaluation tools, and training programs to reduce asymmetries in AI governance capabilities. Additionally, the Dialogue should initiate structured collaboration channels between policymakers, technical experts, and industry practitioners—ensuring that governance evolves alongside real-world system behavior, including frontier risks identified through red-teaming and adversarial evaluation. Ultimately, success will be measured by whether the Dialogue produces actionable governance artifacts (frameworks, toolkits, benchmarks) that can be directly adopted by institutions, rather than remaining a purely deliberative forum.

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

These priorities reflect a systems-level view of AI governance, where effectiveness depends on both technical and institutional alignment. Interoperability of governance approaches is foundational. AI systems operate across jurisdictions, while governance frameworks remain fragmented. Enabling mappings between regulatory regimes and standards is essential to avoid duplication, reduce compliance costs, and ensure consistent safety guarantees. Open-source software, open data, and open models are critical for transparency, auditability, and scientific progress. Open ecosystems enable independent evaluation, reproducibility, and broader participation in safety research-provided that appropriate safeguards and risk-tiering mechanisms are in place. AI capacity-building is necessary to prevent the emergence of structural asymmetries between "AI producers" and "AI consumers." Without investment in skills, infrastructure, and governance capabilities, many countries will be unable to effectively deploy or regulate AI systems. Finally, addressing the social, economic, ethical, cultural, linguistic, and technical implications of AI ensures that governance frameworks remain human-centric and globally relevant. This includes multilingual fairness, labor market transitions, and culturally aware system design. Together, these priorities support a transition from principle-based governance to operational, inclusive, and scalable implementation.

A key emerging gap is the need for operational AI safety and assurance frameworks, particularly for advanced and agentic systems. While existing themes address principles and impacts, there is limited explicit focus on: 1. Continuous model monitoring and lifecycle governance AI systems are dynamic. Governance must include post-deployment monitoring, drift detection, incident reporting, and model change control-aligned with real-world system behavior. 2. Adversarial evaluation and red-teaming at scale Frontier models increasingly exhibit capabilities that may pose systemic risks. Structured, repeatable red-teaming methodologies are required to identify and mitigate these risks before and after deployment. 3. Governance of agentic and autonomous systems The rise of multi-agent systems introduces new challenges in accountability, traceability, and control. Governance frameworks must evolve to address decision chains, tool use, and emergent behaviors. 4. Evaluation standards and metrics harmonization There is currently no globally agreed set of benchmarks for safety, robustness, and alignment. Without shared evaluation standards, comparisons across systems and jurisdictions remain inconsistent. 5. Secure information-sharing on sensitive risks Certain risk domains (e.g. biosecurity, cyber capabilities) require controlled collaboration between trusted actors, as open disclosure may itself create harm vectors. Addressing these cross-cutting issues would significantly strengthen the Dialogue's ability to translate high-level commitments into practical, enforceable governance mechanisms.

AI governance gaps are increasingly visible at the intersection of technical language, policy frameworks, and real-world implementation. A key challenge lies in the misalignment between formal, mathematical concepts and the available governance vocabulary. Many advanced AI notions—particularly those emerging from areas such as alignment, interpretability, and agentic behavior—do not yet have precise, standardized linguistic representations. This creates a structural barrier: policymakers, regulators, and practitioners often operate with ambiguous or overloaded terms, leading to inconsistent interpretation and ineffective implementation. The absence of a shared lexicon is already recognized as a core governance issue, contributing to misaligned expectations, fragmented regulation, and weak compliance practices . More broadly, language itself shapes governance: imprecise terminology can obscure responsibility, distort risk perception, and limit the ability to design enforceable controls. This challenge is amplified at the global level by the multilingual divide, where AI capabilities and safety assurances vary significantly across languages, creating uneven governance outcomes and systemic inequities. At the same time, this gap presents a major opportunity. AI itself can support the development of a new, more precise governance vocabulary, grounded in formal representations and computational semantics. By aligning linguistic frameworks with underlying technical realities, institutions can improve: clarity in regulation and policy drafting interoperability across jurisdictions effectiveness of auditing and compliance mechanisms In this sense, AI governance is not only about regulating systems, but also about evolving the language through which we understand and control them. Addressing this linguistic realignment is essential to fully leverage AI capabilities while ensuring coherent, enforceable, and globally inclusive governance.

The Global Dialogue on AI Governance can play a pivotal role by acting as a coordination layer between existing governance efforts, rather than a parallel initiative. First, it can function as a translation mechanism between high-level principles and operational implementation. While many frameworks promote trustworthy AI, there remains a gap in translating these into shared technical standards, evaluation methodologies, and governance processes. For example, existing frameworks often provide guidance but lack enforceable or measurable controls, limiting their practical impact. Second, the Dialogue can facilitate interoperability across jurisdictions, enabling mutual recognition of governance approaches. Given that AI systems are inherently transnational, alignment between regulatory models (e.g. risk-based approaches and voluntary frameworks) is essential to avoid fragmentation and duplication. Third, it can serve as a platform for trusted information-sharing, particularly on emerging risks and safety practices. This includes structured exchanges on adversarial testing, model evaluation, and incident reporting-areas where cooperation is necessary but currently limited. Finally, the Dialogue can contribute to reducing global asymmetries by connecting capacity-building initiatives with technical governance practices, ensuring that all countries can meaningfully participate in AI governance. In this sense, the AI Dialogue should be positioned not as a normative body, but as an operational bridge-aligning principles, standards, and practices into a coherent and collaborative global governance ecosystem.

The AI Dialogue should build upon and connect existing global governance frameworks that already provide complementary foundations. Key initiatives include: The OECD AI Principles, which establish globally recognized values for trustworthy, human-centric AI and serve as a baseline for international cooperation. The NIST AI Risk Management Framework (AI RMF), which offers a structured, lifecycle-based approach to identifying and managing AI risks through governance, measurement, and monitoring processes. The EU AI Act, which operationalizes a risk-based regulatory model and is increasingly influencing global governance approaches. The UNESCO Recommendation on AI Ethics, which provides a comprehensive normative framework grounded in human rights and social impact. These frameworks are largely complementary but fragmented, and are often implemented in parallel rather than in an integrated manner. The added value of the AI Dialogue lies in its ability to: Create crosswalks and mappings between frameworks, enabling interoperability and reducing duplication Promote shared evaluation standards and governance toolchains, moving from principles to measurable implementation Enable multistakeholder coordination, bridging policymakers, technical experts, and practitioners Support global capacity alignment, including shared resources, training, and governance infrastructures In particular, the Dialogue can contribute to initiatives already proposed at the UN level, such as a global AI standards exchange and capacity development network, by providing a structured and continuous coordination platform. Ultimately, its value is in transforming a fragmented landscape into a connected, operational, and globally inclusive governance system.

Effective participation in the AI Dialogue should reflect a true multistakeholder model, where governments, industry, academia, and civil society contribute complementary expertise. This approach is widely recognized as essential, as no single actor can adequately govern AI systems alone. Each stakeholder group should contribute in structured ways: Governments: define regulatory priorities and public-interest constraints Industry: provide implementation insights and operational constraints Academia and technical experts: contribute evaluation methodologies, formal models, and safety techniques Civil society: ensure representation of societal impacts, rights, and inclusion To operationalize this, the Dialogue should adopt a layered structure: Plenary sessions for strategic alignment and political direction Technical working groups focused on specific domains (e.g. evaluation, interoperability, safety) Implementation labs where stakeholders collaboratively test governance approaches on real-world use cases Feedback loops to integrate outcomes into policy and standards Importantly, participation should not be episodic. Continuous engagement mechanisms-such as working groups, consultation cycles, and shared repositories-are necessary to maintain momentum and institutional memory. The Dialogue should also emphasize clear role definition, transparency, and documented outputs, ensuring that stakeholder contributions translate into actionable outcomes rather than informal discussion.

The Global Dialogue offers a unique opportunity to broaden participation and enrich AI governance through diverse perspectives and experiences. Several voices can be further empowered: Technical practitioners and implementers, whose experience can strengthen the connection between policy design and real-world systems Institutions from emerging and developing economies, contributing context-specific innovation and governance approaches Linguistic and cultural communities, ensuring that AI systems and governance frameworks reflect global diversity Interdisciplinary fields, such as social sciences and humanities, which provide valuable insights into human-centered AI The inclusion of these perspectives is essential to achieving the Dialogue's goal of global and inclusive governance, where all countries and communities can actively contribute To enable this, the Dialogue should: Promote capacity-building initiatives and knowledge-sharing platforms Support multilingual participation and localized engagement formats Encourage regional representation and rotating participation models Facilitate collaboration between technical and policy communities Inclusion should be approached as an opportunity to expand collective capability, enabling governance frameworks that are more robust, adaptable, and globally relevant.

The AI Dialogue can distinguish itself by adopting innovative, interactive formats that transform participation into meaningful collaboration. Promising approaches include: Governance co-design labs, where stakeholders jointly develop frameworks, standards, and evaluation methods Scenario-based simulations, allowing participants to explore how governance approaches perform in realistic situations Collaborative experimentation environments, where governance concepts are tested on real or representative AI systems Peer-learning sessions, enabling countries and organizations to share successful practices and implementation strategies Digital collaboration platforms, supporting continuous and global participation beyond physical meetings These formats align with the Dialogue's role as a space to share knowledge, build common understanding, and accelerate cooperation By emphasizing experimentation, co-creation, and continuous interaction, the Dialogue can foster a dynamic ecosystem of collaboration, where stakeholders collectively advance AI governance in a practical and forward-looking manner.

A number of existing policies and practices demonstrate how AI governance can be translated into practical, scalable, and innovation-enabling approaches. A first strong example is the combination of principle-based and operational frameworks. The OECD AI Principles provide a widely adopted foundation for trustworthy and human-centric AI, promoting innovation, interoperability, and inclusive growth across jurisdictions . These principles are complemented by more operational frameworks such as the NIST AI Risk Management Framework, which introduces lifecycle-based processes (govern, map, measure, manage) to support practical implementation . A second important practice is the risk-based regulatory approach, exemplified by the EU AI Act. By classifying AI systems into categories and associating proportional obligations, it enables innovation while providing clarity for deployment and compliance . Third, ethics-to-implementation translation mechanisms - such as the UNESCO Recommendation on the Ethics of Artificial Intelligence-demonstrate how values like inclusion, transparency, and accountability can be embedded into institutional processes and system design . At the operational level, several practices stand out: Governance-by-design approaches, integrating policies, controls, and monitoring directly into AI system lifecycles Regulatory sandboxes, allowing institutions to test AI systems and governance models in real-world conditions while refining approaches iteratively Evaluation and observability platforms, enabling continuous measurement of system performance and alignment with governance objectives Cross-framework mappings, aligning multiple standards (e.g. OECD, NIST, EU) to support interoperability and reduce fragmentation Overall, effective AI governance emerges from integrating principles, technical methods, and institutional processes into a coherent system-transforming governance from a static requirement into an enabler of innovation, trust, and global collaboration.