Ministry of Foreign Affairs of the Republic of Türkiye

The success of the first Global Dialogue on AI Governance will depend on its ability to deliver concrete, inclusive, and actionable outcomes that strengthen coherence across an increasingly fragmented international landscape. A key priority should be mapping existing global and regional AI initiatives to identify gaps, overlaps, and areas requiring enhanced cooperation. By clearly defining its added value and avoiding duplication, the Dialogue can position itself as a complementary and efficiency-enhancing platform within the broader AI governance ecosystem. Building on this foundation, the Dialogue should establish a shared framework that fosters mutual understanding of different national regulatory approaches. This framework should aim to identify common minimum principles on safety, transparency, and the ethical use of AI systems, while remaining flexible enough to accommodate diverse national priorities, capacities, and development levels. In this regard, providing targeted technical and institutional support, particularly for countries with limited capacities, will be essential to ensure inclusivity and meaningful participation. Equally important is the development of a robust cooperation architecture that strengthens intergovernmental engagement, facilitates knowledge exchange, and promotes policy coordination. Such a structure can help translate dialogue into sustained collaboration and more coherent global responses. To move beyond high-level discussions, the Dialogue should prioritize the operationalization of AI governance principles through practical tools, regulatory guidance, and actionable recommendations. This includes, for example, advancing discussions on the physical security and resilience of AI computational infrastructure. Finally, success will depend on the Dialogue's ability to avoid institutional bottlenecks and generate outcomes that influence real-world practices. Ensuring a degree of authority, continuity, and stakeholder buy-in will be critical for translating commitments into measurable actions among key actors, and to encourage alignment of practices.

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

The selection of "Transparency, Accountability, and Human Oversight" reflects the view that explainability of AI systems, clear allocation of responsibility in decision-making processes, and the retention of ultimate human control are fundamental to ensuring legitimate, trustworthy, and human-centered governance ensuring respect for fundamental rights and freedoms. These elements are essential not only for public trust, but also for effective risk management and accountability. "Safe, Secure and Trustworthy AI" is likewise a key priority, as it directly contributes to minimizing risks, preventing harm, and ensuring that AI systems are developed and deployed in a responsible and public-interest-oriented manner. Together with transparency and accountability, this forms the core of a robust governance framework aligned with our national AI strategy and supportive of international standard-setting efforts. At the same time, the "Social, economic, ethical, cultural, linguistic, and technical implications of AI" must be addressed holistically. A socio-technical perspective highlights that AI is not solely a technical domain but a multidimensional phenomenon that affects societies, fundamental rights, cultural diversity, and development trajectories. Integrating these dimensions into governance approaches is therefore critical for achieving balanced and sustainable outcomes. Finally, "AI Capacity-Building" is an essential priority, given the significant disparities in technical and institutional capabilities both across and within countries. Strengthening skills, infrastructure, and institutional frameworks will enable more inclusive and effective participation in the global AI ecosystem. Taken together, these priorities aim to foster a safe, accountable, and inclusive AI environment, support international cooperation, and contribute to the development of a sustainable and equitable global AI governance framework. If a fifth option had been available, open-source software and open AI models would have been chosen, to foster innovation, transparency and broader access to AI technologies.

While the existing themes provide a solid foundation, several cross-cutting and emerging issues warrant more explicit attention to ensure a comprehensive AI governance framework. First, data governance stands out as a critical dimension, encompassing data privacy, security, quality, and cross-border flows. Data should not be treated merely as a technical input, but as a strategic asset closely linked to trust, fundamental rights, governance capacity, and sovereignty. Its transnational nature further underscores the need for coordinated international approaches. Second, institutional and regulatory capacity should be recognized as a distinct priority. Effective AI governance depends not only on well-defined principles but also on the ability of public institutions, regulators, and judicial bodies to implement, monitor, and enforce them in practice. Third, access to remedy and procedural safeguards deserve greater emphasis. A human rights-based approach requires not only preventive measures but also effective redress mechanisms, due process, contestability, and independent oversight, particularly in high-impact AI applications. In addition, the growing concentration of advanced AI capabilities-including computing infrastructure, data resources, and model development-raises concerns regarding technological dependence and market concentration. This dynamic poses challenges not only in economic terms but also for strategic autonomy, equitable participation, and the inclusiveness of global governance processes, especially for developing countries, while recognizing the role of innovation ecosystems and open collaboration. Finally, the security and resilience of critical digital infrastructure should be treated as a cross-cutting issue. Protecting these systems against cyber threats, disruptions, and external dependencies is essential for ensuring the safe and reliable functioning of AI ecosystems. Taken together, these issues should be addressed as integral components of AI governance, as they will play a decisive role in shaping a secure, inclusive, and sustainable global AI landscape.

One of the most significant challenges is that the rapid advancement of AI technologies is not matched by the pace of regulatory and governance frameworks. The growing scale of cross-border digital services further increases the need for international alignment and interoperability. In addition, the growing demand for electricity and advanced digital infrastructure presents scaling challenges, particularly for AI and cloud services in local markets. From Türkiye's perspective, in the thematic areas of "transparency, accountability, and human oversight," a key gap lies not in policy formulation but in implementation. While both horizontal and sector-specific frameworks have been introduced, institutional capacity, inter-agency coordination, and supervisory mechanisms continue to evolve in line with rapid pace of technological developments. Türkiye's 2021–2025 National Artificial Intelligence Strategy has provided a strong foundation, contributing to increased awareness and institutionalization across the public sector. Many public institutions have developed their own AI strategies and established dedicated units. However, as AI adoption accelerates in both public services and the private sector, challenges related to "AI capacity-building," "safe, secure and trustworthy AI," and the "linguistic and technical implications of AI" have become more pronounced. In particular, gaps in technical expertise, regulatory enforcement capacity, and language-inclusive AI systems remain key areas requiring further development. At the same time, these developments present important opportunities. AI offers significant potential to enhance the efficiency and quality of public services, accelerate industrial digital transformation, and strengthen national innovation capacity. Türkiye's growing technical talent pool, strategic geopolitical position, and active engagement in digital diplomacy also provide a strong basis to expand bilateral and multilateral cooperation. Leveraging these advantages can support more inclusive capacity-building efforts, contribute to addressing shared governance challenges, and position Türkiye as a constructive actor in shaping a sustainable and balanced global AI governance framework.

The AI Dialogue can play a pivotal role in advancing international cooperation on AI governance by providing an inclusive and structured platform for building shared understanding, mutual trust, and practical collaboration among States and relevant stakeholders. First, it can help develop a common baseline around key governance principles such as safety, transparency, accountability, and respect for human rights, while accommodating different national contexts and levels of development. By facilitating the exchange of knowledge, best practices, and regulatory experiences, the Dialogue can also contribute to reducing capacity gaps and supporting more balanced and inclusive participation, particularly for countries with limited technical and institutional resources. Second, the Dialogue can serve as a bridge between countries with varying levels of readiness by promoting capacity-building initiatives, technical cooperation, and peer learning. In this regard, it should go beyond general policy discussions and encourage practical follow-up through regular consultations, thematic working tracks, and targeted cooperation mechanisms. Third, given the inherently cross-border nature of AI, the Dialogue can strengthen coordination on issues that no single country can effectively address alone, including data governance, interoperability, risk management, and the broader social and economic implications of AI. This would contribute to greater policy coherence and help avoid fragmentation in the global governance landscape. Ultimately, the Dialogue's most important role is to establish a continuous, action-oriented, and multi-stakeholder consultative framework for cooperation. If it succeeds in translating discussions into sustained engagement and concrete collaborative outcomes, it can make a meaningful contribution to a more effective, inclusive, and internationally coordinated AI governance ecosystem.

The AI Dialogue should build upon and connect with existing international and regional initiatives on AI governance, digital cooperation, and technical standard-setting, particularly those within the United Nations system and other multilateral and regional frameworks. It should also actively engage with multi-stakeholder initiatives that bring together governments, the private sector, academia, and civil society, especially those focused-on AI safety, responsible innovation, interoperability, and capacity-building. Key reference points include the OECD Working Party on AI Governance, the Council of Europe Steering Committee for New and Emerging Digital Technologies, and the Global Partnership on AI, all of which contribute to policy development and governance discussions. In parallel, international standard-setting bodies such as International Organization for Standardization, Institute of Electrical and Electronics Engineers, National Institute of Standards and Technology, CEN-CENELEC, and the International Telecommunication Union play a critical role in developing technical standards and should be closely aligned with the Dialogue's work. Beyond institutional frameworks, cooperation with research centers, think tanks, and non-profit policy initiatives is also essential to ensure that governance discussions are informed by interdisciplinary expertise and evolving technological developments. The added value of the AI Dialogue lies in its ability to act as a connecting platform across these diverse initiatives, reducing fragmentation and avoiding duplication. By bringing together different stakeholders and disciplines under a single, inclusive framework, it can foster greater coherence between policy discussions and technical standard-setting processes. Moreover, by prioritizing implementation-oriented outcomes, such as practical guidance, capacity-building efforts, and coordinated action, it can help support the translation of existing principles into effective governance by relevant authorities, thereby strengthening the overall global AI governance ecosystem.

Different stakeholders, including governments, the private sector, academia, international organizations, civil society, and technical communities, can contribute to the AI Dialogue by bringing complementary expertise, perspectives, and resources to support informed and inclusive policy discussions led by States. Governments can share national strategies, regulatory approaches, and policy priorities, contributing to mutual understanding and alignment. The private sector can provide insights based on practical experience in developing and deploying AI systems, as well as technical expertise and innovation capacity. Academia and research institutions can offer evidence-based analysis, foresight, and methodological support, while civil society can highlight societal impacts, human rights considerations, and inclusiveness. Technical communities and standard-setting bodies can contribute to the development of interoperable and implementable solutions. Stakeholders can also play an important role in supporting capacity-building efforts, including through technical assistance, knowledge-sharing, and voluntary contributions, particularly to enable the meaningful participation of developing countries. To maximize effectiveness, the AI Dialogue should adopt a structured, multi-layered, and action-oriented format. This could include a high-level governmental segment to facilitate intergovernmental exchange, complemented by a multi-stakeholder plenary to ensure broad and inclusive participation, in support of intergovernmental processes. In addition, dedicated thematic working groups or tracks should be established to allow in-depth discussions on specific issues, supported by interactive formats such as workshops, roundtables, and breakout sessions. A hybrid structure, combining in-person and virtual participation, would enhance accessibility and inclusiveness. Importantly, the Dialogue should aim to produce clear and practical outputs, such as recommendations, best practice compilations, and a forward-looking roadmap. Such a format would enable the Dialogue to function as a continuous, transparent, predictable and results-oriented platform for sustained international cooperation on AI governance.

In global AI governance discussions, actors with strong technical and economic capabilities, particularly advanced economies and large technology companies, tend to be the most visible and influential. In contrast, least developed countries, which often face limitations in technical, financial, and institutional capacity, remain underrepresented. Similarly, startups and SMEs, as well as workers and professional groups directly or indirectly affected by AI, are not sufficiently included in current debates. As a result, governance discussions may reflect the priorities of technologically advanced actors, while the needs, constraints, and expectations of others remain less visible. For a more balanced and legitimate global AI governance framework, it is essential to meaningfully include those who are most affected by AI-driven transformations. This includes not only states with limited capacity, but also local innovators, small businesses, labor groups, and communities experiencing the social and economic impacts of AI adoption. To address these gaps, more accessible and inclusive participation mechanisms should be established. This could involve structured consultation processes, dedicated stakeholder engagement channels, and support for participation through capacity-building, funding, and technical assistance. In particular, enabling the effective engagement of developing countries requires strengthening institutional and human capital capacities, as well as facilitating access to relevant expertise. In addition, hybrid participation formats and open consultation platforms can help lower barriers to entry and broaden representation. Ensuring contributions are duly considered in decision-making processes from underrepresented groups are systematically reflected in outcomes is equally important for building trust and legitimacy. Overall, a more inclusive approach, supported by practical measures to enable meaningful participation, would contribute to a more equitable, representative, and effective global AI governance ecosystem.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond static, meeting-based formats and adopt continuous, interactive, and solution-oriented engagement mechanisms. Establishing thematic working groups that bring together diverse stakeholders, governments, private sector, academia, civil society, and technical experts, would be particularly effective. These groups should not be limited to periodic sessions but supported by persistent digital collaboration platforms, enabling ongoing exchange, iterative input, and sustained cooperation throughout the Dialogue process. In addition, interactive formats such as breakout sessions, multi-stakeholder workshops, and roundtables can facilitate more focused and inclusive discussions. Scenario-based exercises and policy simulations could further enhance engagement by allowing participants to explore real-world challenges, test governance approaches, and better understand trade-offs in complex AI policy environments. Hybrid participation models are also essential to ensure broad accessibility. Leveraging digital tools, such as real-time polling, collaborative drafting platforms, and virtual whiteboards, can enable active participation, even in remote settings, and help capture diverse inputs more effectively. To strengthen impact, the Dialogue could also incorporate innovation-oriented formats such as collaborative consultation sessions where stakeholders jointly develop practical solutions, guidelines, or pilot initiatives. These formats can help bridge the gap between high-level discussions and implementable outcomes. Overall, combining continuous engagement through thematic groups with interactive and hybrid formats would create a more inclusive, agile, and results-oriented Dialogue, capable of generating both substantive insights and practical contributions to global AI governance.

AI governance in Türkiye is a relatively recent and evolving area, and fully developed, large-scale implementation examples are still emerging. However, several international policies, frameworks, and platforms provide valuable references and practical approaches that can inform effective AI governance. The European Union's regulatory sandboxes under the EU AI Act represent an important practice, particularly for SMEs and start-ups. These sandboxes enable the testing of AI systems in controlled environments under regulatory supervision, supporting innovation while ensuring compliance with safety and ethical requirements. Institutional developments such as national AI Safety Institutes also illustrate a growing trend toward more structured governance in areas including AI safety, model evaluation, risk assessment, and technical standard-setting. In parallel, emerging work on governance frameworks for agentic AI reflects efforts to address new and evolving risks associated with increasingly autonomous systems. Standards and risk management frameworks provide another key pillar. The NIST AI Risk Management Framework, ISO/IEC 42001, and ISO/IEC 23894 contribute to more systematic approaches in organizational governance, internal controls, and trustworthiness of AI systems, alongside emerging regional and national frameworks from different parts of the world. In addition, data-driven platforms such as the OECD AI Incidents and Hazards Monitor and the MIT AI Risk Repository play a crucial role in strengthening evidence-based policymaking by documenting real-world AI risks, incidents, and trends. Taken together, these examples highlight the importance of combining regulatory experimentation, institutional capacity-building, technical standards, and evidence-based tools to develop more effective, adaptive, and trustworthy AI governance systems.