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successful inaugural Global Dialogue on AI Governance should yield outcomes that are both value-driven and practically applicable. To begin with, it should define a set of shared international principles for trustworthy AI. These principles should extend beyond commonly discussed themes like fairness and transparency, and also address evolving challenges such as automation bias and excessive dependence on AI technologies. In addition, success would involve outlining a structured roadmap for global collaboration. This would support the exchange of knowledge, alignment of regulatory approaches, and inclusive engagement—particularly for regions that currently lack sufficient AI capacity. Furthermore, the Dialogue should generate actionable guidance for implementation. This includes promoting human-in-the-loop system architectures, ensuring clear communication about AI limitations, and adopting evaluation methods that assess trustworthiness beyond simple accuracy measures. Another key indicator of success would be the establishment of sustained, multi-stakeholder collaboration. Continuous involvement from governments, academic institutions, industry leaders, and civil society organizations is essential. Finally, the Dialogue should lead to tangible commitments from participants to implement responsible AI practices in their respective domains. Overall, the Dialogue can be considered successful if it effectively translates discussion into meaningful action, advancing the development of AI systems that are safe, accountable, and deserving of public trust.

• Safe, secure and trustworthy AI
• AI capacity-building
• Transparency, accountability, and human oversight
• Social, economic, ethical, cultural, linguistic and technical implications of AI

These priorities reflect a balanced approach between managing risks and enabling inclusive progress in AI development. Ensuring safe, secure, and trustworthy AI is fundamental, as the rapid deployment of AI systems introduces risks related to reliability, misuse, and unintended consequences. Strengthening safeguards is essential to build public confidence and ensure responsible adoption. AI capacity-building is equally critical to address global disparities in access to technology, expertise, and infrastructure. Without targeted efforts, many regions risk being excluded from both the benefits of AI and participation in shaping its governance. Supporting skills development, knowledge transfer, and institutional readiness is therefore a key priority. Transparency, accountability, and human oversight are necessary to ensure that AI systems remain aligned with human values and can be properly scrutinized. Emphasizing human-in-the-loop approaches and clear disclosure of system limitations helps mitigate over-reliance and strengthens accountability mechanisms. Finally, addressing the broader social, economic, ethical, cultural, linguistic, and technical implications of AI is essential for long-term sustainability. AI systems do not operate in isolation; they shape and are shaped by societal contexts. Considering these dimensions helps ensure that AI development is inclusive, context-sensitive, and responsive to diverse needs. Together, these priorities support a comprehensive approach that combines risk management, equitable access, and responsible innovation.

Yes, several cross-cutting and emerging issues deserve greater attention beyond the listed themes. One important area is automation bias and human over-reliance on AI systems. As AI becomes more embedded in decision-making, there is a growing risk that users defer excessively to automated outputs, even when they are flawed. Addressing this requires not only technical safeguards but also user training and better system design that encourages critical oversight. Another emerging issue is evaluation and measurement of AI trustworthiness beyond accuracy. Current assessment practices often prioritize performance metrics, while overlooking factors such as robustness, explainability, reliability in real-world conditions, and societal impact. Developing standardized, multidimensional evaluation frameworks is essential. Environmental sustainability of AI systems is also an increasingly urgent concern. The energy consumption associated with training and deploying large-scale models has significant environmental implications, which should be incorporated into governance discussions and accountability mechanisms. In addition, data governance and data quality remain foundational challenges. Issues such as data bias, representativeness, consent, and data stewardship cut across all AI applications and directly influence outcomes. Finally, the concentration of AI capabilities and resources in a small number of actors raises concerns about equitable access, market competition, and geopolitical imbalance. Addressing this requires policies that promote inclusivity, shared infrastructure, and broader participation in AI development. These cross-cutting issues highlight the need for a holistic and forward-looking approach to AI governance.

Governance gaps in the selected thematic areas are creating both pressing challenges and meaningful opportunities across the sector. One of the most significant challenges relates to uneven AI capacity and readiness. Limited access to skilled talent, infrastructure, and localized datasets constrains the ability to develop and deploy AI systems effectively. This can widen existing digital divides and reduce competitiveness in the global AI landscape. Another key issue is the lack of clear and harmonized regulatory frameworks. Fragmented or evolving policies create uncertainty for organizations seeking to adopt AI responsibly, slowing innovation while also increasing the risk of inconsistent safeguards. In particular, gaps in standards for transparency, accountability, and human oversight make it difficult to operationalize trustworthy AI in practice. There are also concerns around over-reliance on AI systems and insufficient understanding of their limitations, which can lead to poor decision-making and reduced human accountability. This is compounded by limited mechanisms for evaluating AI systems beyond technical performance metrics. At the same time, these gaps present important opportunities. Strengthening AI governance frameworks can position the sector as a leader in responsible and trustworthy AI adoption, enhancing public trust and attracting investment. Expanding capacity-building initiatives can empower local talent, foster innovation ecosystems, and enable more inclusive participation in AI development. Furthermore, addressing broader social and ethical implications creates opportunities to design AI systems that are more context-aware, equitable, and aligned with societal values. Overall, bridging these governance gaps can unlock sustainable growth while ensuring that AI deployment remains safe, accountable, and beneficial.

The AI Dialogue can serve as a neutral, inclusive platform for aligning global approaches to AI governance while respecting regional differences. It can facilitate structured exchange between governments, industry, academia, and civil society, helping to bridge policy gaps and reduce fragmentation. A key role is to promote convergence around core principles for trustworthy AI, while also translating these principles into practical, implementable guidance. The Dialogue can support coordination on standards, risk management approaches, and evaluation methods, enabling greater interoperability across jurisdictions. It can also strengthen global inclusion by amplifying the voices of underrepresented regions and supporting capacity-building efforts. By fostering knowledge sharing and technical cooperation, the Dialogue can help ensure that all countries can meaningfully participate in AI governance. Ultimately, the Dialogue can act as a catalyst for moving from high-level commitments to coordinated action, accelerating the development of safe, accountable, and globally aligned AI systems.

The AI Dialogue should build on existing efforts such as international principles on trustworthy AI, regional regulatory frameworks, multi-stakeholder partnerships, and technical standard-setting initiatives. These include work by intergovernmental organizations, standards bodies, and collaborative research networks. Its added value lies in connecting these fragmented efforts into a more coherent global ecosystem. The Dialogue can provide a platform for coordination, reduce duplication, and identify gaps that existing initiatives do not address. Additionally, it can bridge the gap between policy and practice by translating principles into actionable guidance and fostering collaboration across sectors. By ensuring broader inclusion—particularly from developing regions—it can also enhance legitimacy and global relevance. Overall, the AI Dialogue can complement existing initiatives by acting as a unifying and action-oriented forum for international AI governance.

A multi-stakeholder approach is essential for a meaningful AI Dialogue. Governments can provide policy direction and regulatory perspectives; industry can share practical insights on deployment and innovation; academia can contribute research and evaluation methods; and civil society can ensure that societal impacts and human rights considerations are fully represented. To be effective, the Dialogue should combine plenary sessions for high-level alignment with smaller thematic working groups focused on specific issues such as safety, capacity-building, and accountability. These groups should be tasked with producing concrete outputs, such as recommendations or toolkits. The structure should also include regional consultations to capture diverse perspectives and ensure geographic inclusivity. Hybrid participation (in-person and virtual) can further broaden access. Clear timelines, defined deliverables, and follow-up mechanisms are critical to ensure continuity and accountability beyond the initial Dialogue. Establishing a standing coordination mechanism or periodic review process would help sustain progress and track implementation.

Several important perspectives remain underrepresented in global AI governance discussions. These include stakeholders from developing countries, small and medium-sized enterprises (SMEs), marginalized and vulnerable communities, indigenous groups, and non-technical disciplines such as the humanities and social sciences. Their limited participation is often due to resource constraints, lack of access to technical expertise, and insufficient representation in global forums. This can result in governance frameworks that do not fully reflect diverse social, cultural, and economic contexts. To address this, the AI Dialogue should prioritize inclusive participation mechanisms, such as targeted funding support, travel grants, and capacity-building initiatives. Regional and local consultations can help surface context-specific challenges and solutions. Additionally, simplifying technical language, providing multilingual access, and creating accessible engagement formats can lower barriers to participation. Partnering with local organizations and community networks can also help ensure that a broader range of voices is meaningfully included in shaping AI governance.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and adopt more interactive formats. One effective approach is scenario-based workshops, where participants collaboratively explore real-world use cases and governance challenges. This encourages practical problem-solving and cross-sector understanding. Policy labs or co-creation sessions can also be valuable, enabling stakeholders to jointly develop recommendations, frameworks, or prototypes in a structured and outcome-oriented setting. Incorporating simulation exercises—such as crisis response scenarios involving AI failures or misuse—can help participants better understand risks and test governance approaches. Digital engagement tools, including collaborative platforms, live polling, and breakout discussions, can enhance participation, especially in hybrid settings. Finally, establishing ongoing communities of practice or thematic networks can extend engagement beyond the Dialogue itself, ensuring sustained collaboration and knowledge exchange. These formats can make the Dialogue more inclusive, practical, and action-oriented.

Several existing policies, frameworks, and practices provide strong foundations for effective AI governance and offer practical solutions to current challenges. Risk-based regulatory models, such as the EU AI Act, demonstrate how oversight can be tailored to the level of risk posed by AI systems, balancing innovation with safety. Similarly, the OECD AI Principles and the UNESCO Recommendation on the Ethics of AI provide globally recognized benchmarks for trustworthy and human-centered AI. At the operational level, tools like Algorithmic Impact Assessments (AIAs)-used in countries such as Canada-help organizations identify and mitigate risks before deployment. The National Institute of Standards and Technology AI Risk Management Framework is another practical resource that guides organizations in assessing and managing AI-related risks. Transparency is further supported by technical practices such as model cards and datasheets for datasets, which document system capabilities, limitations, and intended uses. In parallel, independent auditing mechanisms are emerging to strengthen accountability and external oversight. Collaborative initiatives also play an important role. The Global Partnership on Artificial Intelligence and open research ecosystems promote knowledge sharing, capacity-building, and alignment across jurisdictions. Together, these examples highlight that effective AI governance depends on combining regulatory approaches, technical tools, and international cooperation to translate high-level principles into actionable and measurable outcomes.