NIRA

In my view, the success of the first Global Dialogue on AI Governance should be measured by its ability to move beyond general discussions and deliver practical, inclusive, and actionable outcomes. First, a key achievement would be the establishment of a shared global framework or set of guiding principles for responsible AI governance. Such a framework should emphasize transparency, accountability, human rights protection, and ethical standards, while remaining adaptable to different national contexts. Second, the dialogue should prioritize inclusivity by ensuring meaningful participation from developing and fragile states. Countries with emerging digital systems, such as Somalia, must have a voice in shaping AI governance models that are relevant to their realities. This includes addressing issues of digital inequality, infrastructure gaps, and capacity constraints, so that AI does not widen existing global disparities. Third, concrete commitments toward capacity building and knowledge transfer would be essential. This could include the creation of partnerships, technical assistance programs, and funding mechanisms to support countries in developing legal frameworks, regulatory institutions, and technical expertise for AI governance. Fourth, the dialogue should promote interoperability and cooperation between governments, the private sector, and international organizations. AI governance cannot be effective without coordinated global efforts, particularly in areas such as data protection, cybersecurity, and cross-border data flows. Finally, a successful outcome would include a clear roadmap for implementation, with measurable targets and follow-up mechanisms. Without accountability and continuity, even well-designed frameworks risk remaining theoretical. In summary, the dialogue will be successful if it produces actionable, inclusive, and context-sensitive outcomes that support both innovation and the responsible use of AI globally.

• AI capacity-building
• Protection and promotion of human rights
• Open-source software, open data and open AI models
• Interoperability of governance approaches

I selected these priorities because they reflect the most pressing and practical needs for effective and inclusive AI governance at the global level. In my view, the rapid development of AI technologies is outpacing the capacity of many governments-especially in developing and fragile contexts-to regulate, manage risks, and ensure equitable benefits. Therefore, it is essential that global discussions focus not only on high-level principles but also on implementation and real-world impact. My professional experience in the public sector, particularly working with national systems and institutional development, has shown me that governance frameworks must be adaptable to different contexts. For countries like Somalia, where digital infrastructure is still evolving, priorities such as capacity building, technical support, and knowledge transfer are critical. Without these, global AI governance risks becoming dominated by technologically advanced countries, leaving others behind. I also emphasized inclusivity because AI governance must reflect diverse perspectives. Policies designed without input from underrepresented regions may fail to address key challenges such as digital inequality, data sovereignty, and limited institutional capacity. Ensuring broad participation strengthens both legitimacy and effectiveness. Furthermore, I prioritized cooperation and interoperability because AI systems operate across borders. Effective governance requires coordination between governments, private sector actors, and international organizations, particularly in areas such as data protection and cybersecurity. In summary, my selection is driven by a balance between principle and practice-ensuring that AI governance is not only ethical and forward-looking but also inclusive, implementable, and responsive to the needs of all countries.

Yes. While the listed themes cover core aspects of AI governance, several cross-cutting and emerging issues deserve greater emphasis. First, digital public infrastructure (DPI) and AI integration is a critical area. In many countries, AI will increasingly operate on top of foundational systems such as digital identity, payment platforms, and data exchange layers. Weak or fragmented DPI can undermine both the effectiveness and safety of AI deployment, particularly in fragile contexts. Second, data governance and data sovereignty require deeper attention. Questions around who owns, controls, and benefits from data-especially data originating from developing countries-remain insufficiently addressed. There is a growing risk of data extraction without fair value distribution, which may reinforce global inequalities. Third, AI in fragile and conflict-affected settings is an emerging concern. These environments present unique risks, including misuse of AI for misinformation, surveillance, or exacerbating social tensions. Governance approaches must be tailored to such contexts, where institutional capacity is limited and safeguards may be weak. Fourth, public trust and societal legitimacy should be treated as a cross-cutting issue. Beyond technical regulation, AI governance must ensure transparency, explainability, and meaningful public engagement. Without trust, even well-designed systems may fail to achieve adoption or legitimacy. Finally, institutional capacity and regulatory readiness remain fundamental challenges. Many governments lack the technical expertise, legal frameworks, and coordination mechanisms needed to govern AI effectively. Addressing this gap through sustained capacity building and international cooperation is essential. In summary, these issues highlight the need for AI governance to be grounded not only in global principles but also in practical realities, particularly in underrepresented and developing contexts.

In Somalia, gaps in AI governance are closely linked to broader challenges in digital governance, institutional capacity, and regulatory frameworks. As the country advances in building foundational systems—particularly digital identity through the National Identification and Registration Authority (NIRA)—the absence of clear AI and data governance policies creates both risks and missed opportunities. One of the most significant challenges is the lack of comprehensive legal and regulatory frameworks for data protection, privacy, and AI use. This exposes citizens to potential misuse of personal data and limits trust in emerging digital systems. Additionally, institutional capacity remains constrained, with limited technical expertise to design, regulate, and monitor AI systems. This gap is particularly critical as AI technologies begin to intersect with public services such as identity management, security, and service delivery. Another challenge is digital inequality. While mobile and digital financial services are widespread, access to advanced digital infrastructure remains uneven. Without inclusive governance, AI adoption risks deepening existing disparities between urban and rural populations. However, these gaps also present significant opportunities. Somalia has the advantage of being in an early stage of digital transformation, allowing it to adopt "leapfrogging" strategies. By integrating AI governance principles into the development of digital public infrastructure—such as national ID systems, data platforms, and e-government services—the country can build more secure, efficient, and citizen-centered systems from the outset. Furthermore, there is strong potential for international partnerships and knowledge transfer to support capacity building, regulatory development, and technological innovation. With the right frameworks in place, AI can enhance public service delivery, improve transparency, and strengthen governance outcomes. In summary, while governance gaps pose real risks, they also create a unique window for Somalia to shape a forward-looking, inclusive, and responsible AI governance model.

The AI Dialogue can play a pivotal role in advancing international cooperation by serving as a platform that translates global principles into coordinated and practical action. First, it can facilitate the development of shared norms and standards for responsible AI, ensuring alignment on key issues such as ethics, human rights, transparency, and accountability. A common baseline is essential to prevent fragmented regulatory approaches and to enable safe cross-border deployment of AI systems. Second, the Dialogue can strengthen inclusivity by amplifying the voices of developing and underrepresented countries. Effective AI governance must reflect diverse contexts, particularly those with emerging digital infrastructures. By fostering equitable participation, the Dialogue can help ensure that global frameworks are not dominated by a small number of technologically advanced nations. Third, it can act as a catalyst for capacity building and knowledge exchange. Through partnerships between governments, academia, and the private sector, the Dialogue can support technical assistance, training, and institutional development. This is especially important for countries seeking to build regulatory frameworks and technical expertise from the ground up. Fourth, the Dialogue can promote interoperability and policy coherence across jurisdictions. AI systems and data flows operate globally, making international coordination essential in areas such as data protection, cybersecurity, and standards for AI safety. Finally, the Dialogue can establish mechanisms for follow-up, monitoring, and accountability. By setting clear priorities and tracking progress, it can ensure that commitments lead to measurable outcomes rather than remaining aspirational.

The AI Dialogue should build upon and connect with existing global and regional initiatives that are already shaping the governance of artificial intelligence. Key among these are UNESCO's Recommendation on the Ethics of Artificial Intelligence, which provides a comprehensive normative framework grounded in human rights, and the OECD AI Principles, which have been widely adopted and offer practical policy guidance. In addition, the Global Partnership on AI (GPAI) serves as a multi-stakeholder platform advancing research and collaboration, while the United Nations system—including initiatives under the UN Secretary-General's roadmap for digital cooperation—continues to play a central role in coordinating global digital governance efforts. Regional frameworks, such as the European Union's AI Act, also provide important regulatory models that can inform global standards, particularly in areas such as risk-based classification and accountability. Furthermore, emerging Digital Public Infrastructure (DPI) initiatives and data governance partnerships are highly relevant, especially for developing countries seeking scalable and inclusive digital systems. The added value of the AI Dialogue lies in its ability to bridge fragmentation across these initiatives. Rather than duplicating existing efforts, it can serve as a coordination platform that aligns principles, promotes interoperability, and facilitates knowledge exchange between different frameworks and regions. Importantly, it can also elevate the perspectives of developing and fragile states, which are often underrepresented in global AI governance discussions. Additionally, the Dialogue can focus on implementation by translating high-level principles into actionable guidance, capacity-building programs, and measurable commitments. By connecting policy frameworks with practical support mechanisms—such as technical assistance, funding, and institutional development—the AI Dialogue can help ensure that global AI governance becomes more inclusive, coherent, and effective.

Different stakeholders can contribute to the AI Dialogue by bringing complementary expertise, perspectives, and practical experience. Governments should lead in setting policy priorities, regulatory frameworks, and ensuring alignment with public interest objectives. The private sector, as a primary developer and deployer of AI technologies, can provide technical knowledge, innovation capacity, and insights into implementation challenges. Academia and research institutions contribute evidence-based analysis, ethical frameworks, and independent evaluation. Civil society organizations play a critical role in representing public concerns, safeguarding human rights, and promoting accountability. Importantly, participation from developing and fragile states must be actively ensured, as their perspectives are essential for inclusive and globally relevant AI governance. To maximize effectiveness, the AI Dialogue should adopt a structured, multi-layered format. First, high-level plenary sessions can set strategic direction and build political commitment. Second, thematic working groups should focus on key areas such as data governance, AI ethics, capacity building, and digital infrastructure, enabling more technical and solution-oriented discussions. These groups should include diverse stakeholders to encourage cross-sector collaboration. Third, regional consultations should be integrated into the process to capture context-specific challenges and priorities, particularly from underrepresented regions. This would ensure that global outcomes are informed by local realities. Fourth, the Dialogue should incorporate continuous engagement mechanisms, such as virtual platforms and periodic follow-up meetings, to maintain momentum and track progress. Finally, clear outputs—such as policy recommendations, implementation roadmaps, and partnership initiatives—should be defined, along with monitoring and accountability mechanisms. An inclusive, structured, and action-oriented approach will ensure that the AI Dialogue is both representative and impactful.

Several voices remain underrepresented in global discussions on AI governance, particularly those from developing and fragile states, grassroots communities, and non-technical sectors. Countries with emerging digital systems—especially in Africa and similar regions—often have limited representation despite facing some of the most significant risks and opportunities associated with AI adoption. Their exclusion can lead to governance frameworks that do not reflect diverse institutional realities or development priorities. Local communities, including rural populations, informal sector workers, and marginalized groups, are also frequently overlooked. These groups are directly affected by AI-driven decisions in areas such as access to services, financial inclusion, and social protection, yet they rarely have a platform to influence policy design. Similarly, perspectives from practitioners in public service delivery—such as those working in identity systems, healthcare, and local administration—are often underrepresented, despite their practical insights into implementation challenges. Another gap exists in interdisciplinary representation. AI governance discussions are often dominated by technical experts and policymakers, while voices from legal professionals, social scientists, and human rights advocates may not be sufficiently integrated. This can limit the ability to address broader societal and ethical implications. To address these gaps, deliberate inclusion mechanisms are needed. These include targeted outreach and funding support to enable participation from underrepresented countries, regional consultations to capture local perspectives, and partnerships with local institutions and civil society organizations. Hybrid participation models—combining in-person and virtual engagement—can also reduce barriers to entry. Additionally, structured stakeholder representation within working groups can ensure that diverse voices are not only present but actively influence outcomes. Inclusion must be intentional, sustained, and embedded in the design of the AI Dialogue itself.

Several voices remain underrepresented in global discussions on AI governance, particularly those from developing and fragile states, grassroots communities, and non-technical sectors. Countries with emerging digital systems—especially in Africa and similar regions—often have limited representation despite facing some of the most significant risks and opportunities associated with AI adoption. Their exclusion can lead to governance frameworks that do not reflect diverse institutional realities or development priorities. Local communities, including rural populations, informal sector workers, and marginalized groups, are also frequently overlooked. These groups are directly affected by AI-driven decisions in areas such as access to services, financial inclusion, and social protection, yet they rarely have a platform to influence policy design. Similarly, perspectives from practitioners in public service delivery—such as those working in identity systems, healthcare, and local administration—are often underrepresented, despite their practical insights into implementation challenges. Another gap exists in interdisciplinary representation. AI governance discussions are often dominated by technical experts and policymakers, while voices from legal professionals, social scientists, and human rights advocates may not be sufficiently integrated. This can limit the ability to address broader societal and ethical implications. To address these gaps, deliberate inclusion mechanisms are needed. These include targeted outreach and funding support to enable participation from underrepresented countries, regional consultations to capture local perspectives, and partnerships with local institutions and civil society organizations. Hybrid participation models—combining in-person and virtual engagement—can also reduce barriers to entry. Additionally, structured stakeholder representation within working groups can ensure that diverse voices are not only present but actively influence outcomes. Inclusion must be intentional, sustained, and embedded in the design of the AI Dialogue itself.

Several policies and practices provide concrete and transferable models for effective AI governance. First, the European Union's AI Act offers a comprehensive, risk-based regulatory approach. By categorizing AI systems according to their level of risk-ranging from minimal to unacceptable-it enables proportionate oversight while supporting innovation. This model is particularly valuable for countries seeking to balance regulation with technological development. Second, Estonia's e-governance ecosystem, including its digital identity infrastructure and secure data exchange platform (X-Road), demonstrates how strong digital public infrastructure can support trustworthy and efficient AI deployment. By ensuring interoperability, data integrity, and citizen control over personal data, Estonia provides a practical foundation for responsible AI integration in public services. Third, the OECD AI Principles and UNESCO's Recommendation on the Ethics of AI offer widely endorsed normative frameworks grounded in human rights, transparency, and accountability. These instruments are useful for countries developing national AI strategies or legal frameworks aligned with international standards. Fourth, algorithmic impact assessments (AIAs), as implemented in countries such as Canada, represent a practical tool for evaluating the risks of AI systems before deployment. AIAs enhance transparency, identify potential harms, and support informed decision-making in the public sector. Fifth, public-private partnerships in GovTech ecosystems-such as innovation sandboxes and regulatory pilot programmes-enable governments to test AI solutions in controlled environments while ensuring compliance with ethical and legal standards. These approaches encourage innovation while managing risk. Finally, capacity-building initiatives, including technical training, policy toolkits, and institutional strengthening programmes, are essential to operationalizing AI governance, particularly in developing contexts. Together, these examples highlight that effective AI governance requires a combination of clear regulation, strong digital infrastructure, practical assessment tools, and sustained capacity development.