ICT Consultant

First, it should agree on a set of shared principles that countries and organizations can actually apply. Second, it should provide clear and practical guidance on how to adopt AI responsibly in public services and protect data. Third, it should include voices from underrepresented groups to reflect diverse realities. Fourth, it should define a clear roadmap with follow-up actions, working groups, and measurable milestones. Finally, it should strengthen trust between governments, the private sector, and society.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

These priorities balance risk, rights, and real world impact. They help ensure that AI is not only innovative, but also fair, accountable, and relevant to different contexts. Without basic safeguards, AI can amplify risks instead of solving problems. Priority should be on risk management, data protection, and system reliability. Especially in public sector use, people need to trust that systems work as intended and do not cause harm. AI must respect fundamental rights from the design stage. This includes privacy, non discrimination, and due process. Clear accountability is also key, people should understand when and how AI is used, especially in decisions that affect their lives. AI does not operate in isolation. Its impact depends on local realities. language, culture, and access to infrastructure shape how systems are used and who benefits and supports more inclusive adoption.

AI is increasingly embedded in digital public services. There is a need to ensure that AI aligns with existing digital public infrastructure, such as digital identity, data exchange, and payments. A global discussions are important, but implementation often happens at regional level. There is value in promoting regional frameworks that reflect shared realities, especially for developing countries.

Many institutions are still adapting to digital transformation, and AI adds a new layer of complexity. There are gaps in risk assessment, procurement, and oversight of AI systems. Data governance is another issue. Data is often fragmented, with uneven quality and limited interoperability. This affects the reliability of AI systems and increases the risk of bias or poor outcomes. There is also a skills gap. Public sector teams need practical knowledge to evaluate, implement, and monitor AI. Finally, trust remains fragile. If AI systems are not transparent or well understood, people may resist their use, especially in sensitive areas like public services. There is also an opportunity to build governance frameworks from the start, learning from global experiences but adapting them to regional realities. Regional collaboration is a strength. shared challenges create space for common approaches, joint capacity building, and exchange of good practices.

The AI Dialogue can act as a practical bridge between global principles and real implementation. First, it can align priorities. Today, many countries are moving at different speeds and with different approaches. Second, it can translate principles into practice. Many countries need clear guidance on how to apply them. Third, it can strengthen inclusion. International cooperation often overlooks the realities of developing countries. The Dialogue can create space for these perspectives to shape decisions, not just react to them. Fourth, it can support capacity building through collaboration. Instead of isolated efforts, countries can share knowledge, tools, and even technical resources. This includes peer learning, regional initiatives, and partnerships with technical communities. Fifth, it can build trust. AI governance requires cooperation between governments, private sector, and society. Finally, it can maintain continuity. The Dialogue can define follow-up actions, track progress, and keep the conversation active over time.

The AI Dialogue should build on what already exists and focus on connecting efforts, not duplicating them. One key reference is the OECD AI Principles. They provide a solid, widely accepted baseline. Another important initiative is UNESCO's Recommendation on the Ethics of AI. The work of the Global Partnership on AI is also relevant. It connects policy and technical expertise. The Dialogue can complement this by being more inclusive at the multilateral level and ensuring broader geographic representation. In the digital public infrastructure space, initiatives like GovStack offer practical building blocks. The Dialogue can link AI governance discussions with real digital service delivery, making the conversation more grounded. There are also regional efforts, including those within Central American Integration System (SICA), that reflect shared priorities and constraints. The Dialogue can amplify these experiences and encourage cooperation across regions with similar contexts.

The Dialogue should be structured, inclusive, and practical, where each stakeholder (Governments, Private sector, Academia and technical community, Civil society and Regional and international organizations) knows their role and contributes to results that can be used. - Organize the Dialogue around a small number of priority areas - Combine high-level sessions with technical working groups - Work in cycles with clear deliverables: guidance notes, toolkits, or pilot initiatives - Regional dialogues linked to global discussions - Use digital platforms to allow ongoing contributions - Track progress, share updates, and adjust priorities over time

Inclusion requires intention. It means lowering barriers, listening to real experiences, and making sure diverse perspectives influence decisions. Create ongoing spaces where these voices can shape outcomes and hold structured regional dialogues that feed into global discussions. This helps reflect real contexts. AI discussions often ignore cultural context, language diversity, and local knowledge. This can lead to systems that exclude or misrepresent them. Language remains a barrier. Many perspectives never reach global forums because discussions and materials are not accessible. People who actually implement systems in government rarely shape the global conversation. Yet they understand what works and what fails in practice. Most innovation does not come from large tech companies alone. Smaller actors face different challenges but have limited voice. Many are users of AI, they often engage with real impacts on rights and inclusion but lack consistent access to decision spaces.

The Dialogue should feel active, not passive, more working sessions, more practical exchange. Countries or institutions bring a concrete case: a draft policy, a pilot, or a challenge. Experts and peers give direct feedback in a structured session. This helps turn ideas into implementable actions. Discussions start at regional level, where contexts are shared. Key insights are then brought into a global session for comparison and alignment where governments, private sector, and civil society co-create solutions. An provide an online space where participants can submit inputs, vote on priorities, and comment on draft outputs.

There are examples that show how AI governance can move from principles to practice. An effective governance combines tools, rules, and institutions. The EU AI Act classifies AI systems by risk level and applies rules accordingly, some regions, governments and organizations have established anddeveloped strategies with clear ethical principles. These provide direction while leaving space for adaptation. Efforts such as GovStack promote modular and interoperable systems. This helps integrate AI into public services in a controlled and scalable way.