Kurdistan Regional Government

To establish shared basic principles like safety, transparency, and accountability, while also creating ongoing coordination mechanisms rather than remaining a one-time event. It would make tangible progress on specific high-risk issues such as military use of AI and deepfakes, and ensure broad global participation beyond just major powers. Success would also include meaningful commitments to transparency from both governments and companies, along with a clear roadmap outlining next steps. Just as importantly, it would align expectations between industry and policymakers and lead to visible follow-through within a year. In essence, success would mean building real momentum and practical cooperation, not just producing statements.

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

I prioritized safe, secure, and trustworthy AI because establishing strong safety, reliability, and accountability standards is foundational to building public trust and preventing harm, especially as AI systems become more powerful and widely deployed. Without this baseline, progress in other areas risks being undermined by misuse or unintended consequences. AI capacity building is equally critical to ensure that all countries not just technologically advanced ones, can participate meaningfully in AI development and governance. Strengthening skills, infrastructure, and institutional knowledge helps reduce global inequalities and supports more inclusive decision-making. The socioeconomic and technical implications of AI are a priority because AI is already reshaping labor markets, economic structures, and access to opportunities. Addressing these impacts proactively is essential to maximize benefits while mitigating risks such as job displacement, inequality, and digital divides. Finally, interoperability of governance approaches is important to avoid fragmented or conflicting regulatory systems across countries. Greater alignment and compatibility between frameworks can facilitate international cooperation, reduce compliance burdens, and support the safe cross-border development and deployment of AI technologies.

Challenges and opportunities for AI in Education

Governance gaps in safe, secure, and trustworthy AI are creating uncertainty around standards, accountability, and risk management, which can slow adoption while also exposing systems to misuse or low-quality deployments. In contexts where regulatory frameworks are still evolving, this can lead to uneven levels of protection for users and reduced trust in AI systems. Gaps in AI capacity building are contributing to disparities in access to infrastructure, expertise, and data. This limits the ability of some countries or sectors to fully benefit from AI, increases reliance on external technologies, and can widen existing economic and digital inequalities. The lack of clear approaches to managing the socioeconomic and technical implications of AI is affecting labor markets and institutions, which may not yet be fully prepared for automation, reskilling needs, or shifts in productivity. This creates challenges in ensuring that the benefits of AI are broadly shared while minimizing negative impacts such as job displacement or unequal access to opportunities. Also, limited interoperability between governance approaches across countries leads to fragmentation, making it harder to collaborate internationally, share data responsibly, and deploy AI systems across borders. This can slow innovation, increase compliance complexity, and reduce the overall effectiveness of governance efforts.

The AI Dialogue can advance international cooperation by providing a neutral platform for countries to align on priorities, share knowledge, and build trust. It can promote common approaches to AI governance, support capacity building, and improve interoperability between different regulatory frameworks. Overall, its role is to turn discussion into ongoing, practical cooperation with concrete outcomes.

The AI Dialogue should build on existing efforts by the United Nations system, UNESCO, OECD, Global Partnership on AI, International Telecommunication Union, and standards bodies like ISO and IEEE, as well as regional frameworks such as the EU AI Act. Its added value would be to connect these fragmented initiatives, promote coherence and interoperability, include underrepresented countries, and translate existing principles into more coordinated and practical global action.

Different stakeholders can contribute to the AI Dialogue in ways that leverage their unique expertise and perspectives. Governments can provide policy guidance, share regulatory experiences, and commit to adopting coordinated standards. Industry can offer technical expertise, best practices, and insight into real-world AI deployment and risks. Academia and research institutions can contribute evidence-based analyses, safety frameworks, and impact assessments. Civil society and non-governmental organizations can ensure that societal, ethical, and human rights considerations are included, representing the voices of affected communities. Multilateral organizations can facilitate alignment across regions and help provide neutral platforms for discussion. For the format and structure, the Dialogue should be inclusive, multi-layered, and action-oriented. A combination of plenary sessions for high-level commitments and thematic working groups for technical and sector-specific discussions would be effective. Regular follow-ups, progress tracking, and reporting mechanisms can ensure continuity and accountability. Hybrid formats including virtual participation can broaden accessibility, especially for stakeholders in developing regions. Structured opportunities for collaboration, such as joint initiatives, pilot projects, or shared technical assessments, can translate dialogue into practical outcomes while maintaining transparency and trust.

Underrepresented voices in global AI governance include stakeholders from developing countries, Indigenous and marginalized communities, small and medium-sized enterprises, and civil society groups focused on social, labor, or environmental issues. They could be included through capacity-building support, dedicated seats or advisory roles in the Dialogue, accessible materials and hybrid participation, regional hubs to amplify local voices, and structured consultations like workshops and public feedback mechanisms.

Effective engagement formats for the AI Dialogue include interactive workshops and scenario exercises, multi-stakeholder hackathons or policy labs, hybrid and virtual participation with real-time interaction, panels with diverse moderators, and deep-dive sessions on specific themes. Ongoing online forums and shared repositories can maintain momentum and translate discussions into practical outcomes.

Effective AI governance combines policies, standards, and collaborative platforms. Examples include the EU AI Act, which sets risk-based requirements for AI systems; the OECD AI Principles guiding responsible development; and the Global Partnership on AI, which fosters multi-stakeholder collaboration. Technical standards from ISO and IEEE, along with transparency tools, auditing frameworks, and capacity-building programs, provide practical guidance and support inclusive, accountable AI deployment.