Plekhanov University (PRUE) Dubai Campus

A successful first Global Dialogue on AI Governance should deliver outcomes that are practical, inclusive, and scalable, moving beyond high-level principles toward implementation. First, a key outcome would be the launch of a Global AI Capacity Commons, a coordinated initiative to pool resources such as compute infrastructure, open datasets, and foundational models, particularly to support developing countries. This would directly address structural inequalities and enable broader participation in AI development, not just adoption. Second, the Dialogue should produce a baseline interoperability framework for AI governance. Rather than forcing uniform regulation, this would define a set of shared minimum standards on transparency, auditing, safety, and human oversight, that can be adapted across jurisdictions. Such a framework would reduce fragmentation while respecting national and regional contexts. Third, success would include establishing a multi-stakeholder implementation mechanism, a platform or working structure that continues beyond the event. This should bring together governments, private sector actors, academia, and civil society to co-develop and pilot solutions, ensuring the Dialogue is not a one-off exchange but the start of sustained cooperation. Additionally, the Dialogue should highlight a small number of flagship pilot initiatives—for example in healthcare, education, or climate, demonstrating how AI governance can directly contribute to the SDGs in measurable ways. Finally, an essential outcome would be a clear roadmap linking the Scientific Panel's insights to policy action, ensuring that technical expertise informs decision-making in an ongoing, iterative manner. In essence, success lies in shifting from dialogue to delivery by creating mechanisms, tools, and partnerships that make AI governance actionable, equitable, and globally relevant.

• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Transparency, accountability, and human oversight
• Open-source software, open data and open AI models

AI capacity-building, social impact, transparency, and open-source AI are critical priorities because they collectively determine whether AI becomes a driver of shared progress or a source of deeper global inequality. First, AI capacity-building is foundational. Without investments in skills, infrastructure, and institutional readiness, many countries, particularly in the Global South, risk being excluded from both the development and governance of AI. Capacity-building ensures not only access to technology, but also the ability to shape its direction, aligning AI systems with local needs and values. Second, social impact must remain central to AI governance. AI systems are already influencing access to healthcare, education, employment, and public services. Prioritizing social impact helps ensure that these systems advance the Sustainable Development Goals, reduce inequalities, and avoid reinforcing existing biases or harms. It shifts the focus from technological capability to human outcomes. Third, transparency is essential for trust and accountability. As AI systems become more complex and embedded in decision-making, stakeholders need clarity on how these systems function, what data they rely on, and how outcomes are produced. Transparency enables oversight, supports regulatory alignment, and empowers users and institutions to challenge or improve outcomes when necessary. Finally, open-source AI plays a crucial role in democratizing innovation. Open models, datasets, and tools lower barriers to entry, foster collaboration, and accelerate knowledge sharing across borders. They also support interoperability and reduce dependency on a small number of dominant actors, contributing to a more balanced and resilient AI ecosystem. Together, these priorities form the backbone of an inclusive, accountable, and development-oriented approach to AI governance.

Labor and economic transitions cut across all AI governance debates. Beyond job displacement, there are deeper shifts in value chains, informal labor (such as data annotation), and the concentration of economic power. Governance frameworks need to anticipate and manage these transitions more proactively.

Governance gaps in AI are already reshaping higher education, creating both structural challenges and new opportunities. Limited AI capacity-building widens disparities between institutions. Well-resourced universities can integrate advanced tools, develop curricula, and conduct frontier research, while others struggle with infrastructure, skills, and access to data. This risks deepening global and regional inequalities in knowledge production and talent development. At the same time, advances in AI capacity-building offer opportunities to expand access to high-quality education through adaptive learning systems, virtual labs, and AI-assisted teaching. If supported by shared infrastructure and partnerships, these tools can help institutions leapfrog traditional constraints. Social impact concerns are particularly visible in areas such as academic integrity, bias in AI-assisted assessment, and unequal student access to AI tools. Universities must balance innovation with safeguards that protect fairness, inclusion, and critical thinking skills. This creates pressure to update pedagogical models and evaluation methods. Transparency remains a major challenge. The growing use of opaque AI systems in admissions, grading, and research raises questions about accountability and trust. Without clear standards, institutions face reputational and ethical risks. Open source AI presents both promise and complexity. It enables collaboration, lowers costs, and supports local innovation, but also raises concerns about quality control, misuse, and the capacity required to deploy and maintain such systems effectively. Overall, higher education sits at the intersection of these developments. It must act not only as a user of AI, but as a key actor in shaping governance, building capacity, and ensuring that AI development aligns with public interest and societal values.

The AI Dialogue can play a pivotal role in advancing international cooperation by acting as a bridge between fragmented initiatives, diverse stakeholders, and uneven national capacities. Its primary value lies in creating a trusted, neutral space where governments, private sector actors, academia, and civil society can align on shared priorities while respecting different development contexts. One important role is to promote convergence around baseline principles and practical standards. By facilitating agreement on areas such as transparency, safety, and human oversight, the Dialogue can reduce regulatory fragmentation and support interoperability across jurisdictions. This is especially important for technologies that operate across borders. The Dialogue can also strengthen cooperation on AI capacity-building. By coordinating existing efforts and mobilizing partnerships, it can help scale access to infrastructure, skills, and knowledge, particularly in developing countries. This contributes to more equitable participation in both AI development and governance. It can help connect technical expertise with policy processes, ensuring that scientific insights translate into actionable and inclusive governance frameworks.

The AI Dialogue should build on existing initiatives such as the OECD AI Principles, UNESCO Recommendation on the Ethics of AI, the Global Partnership on AI etc. It should also connect with technical and multi-stakeholder efforts like the AI policy and safety Institutes, open source communities, and regional frameworks including the EU AI Act, India AI Mission and African Union AI strategies. Its added value lies in bridging these fragmented efforts into a more inclusive and globally representative platform. Unlike smaller groupings, it can amplify voices from developing countries and align governance discussions with the Sustainable Development Goals. The Dialogue can also link policy frameworks with practical implementation by fostering partnerships, coordinating capacity-building efforts, and promoting interoperable approaches that translate principles into action across different contexts.

Different stakeholders can contribute by aligning their strengths with practical outcomes. Member States can provide policy direction and ensure legitimacy. The private sector can share technical expertise, data, and infrastructure while committing to transparency. Academia can contribute research and independent evaluation. Civil society can represent affected communities and ensure accountability. The technical community can help translate principles into implementable tools and standards. To be effective, the AI Dialogue should combine plenary discussions with smaller, action oriented formats such as thematic working groups and policy labs. Interactive sessions should focus on specific challenges and produce concrete outputs. A continuous structure is also important, with follow up mechanisms, shared platforms, and clear timelines to sustain collaboration beyond the event.

Several voices remain underrepresented in global AI governance discussions, including stakeholders from low and middle income countries, marginalized communities, indigenous groups, and workers involved in data labeling and other invisible parts of the AI value chain. Small and medium enterprises and local innovators are also often excluded despite their role in deploying context specific solutions. Inclusion can be strengthened through targeted funding for participation, regional consultations, and multilingual engagement formats that lower barriers to entry. Creating dedicated channels for community based organizations and affected groups to share lived experiences is essential. Partnerships with local institutions can help surface context specific priorities. In addition, hybrid participation models and open calls for input can broaden access and ensure that governance processes reflect a wider diversity of perspectives.

As mentioned in earlier answers, innovative engagement formats should prioritize interaction, co creation, and tangible outputs. Policy labs can bring diverse stakeholders together to solve specific governance challenges and produce actionable proposals within short timeframes. Scenario based exercises using futures thinking can help participants explore risks and tradeoffs, encouraging more forward looking discussions. Multi stakeholder design sprints can be used to prototype governance tools such as audit frameworks or data sharing models. Smaller thematic working groups can enable deeper exchange and trust building, while rotating roundtables ensure balanced participation across regions and sectors. Digital collaboration platforms can support real time input, voting, and synthesis of ideas. To sustain impact, these formats should be linked to follow up processes that refine and implement the outcomes beyond the Dialogue.

Several policies, practices, platforms, and approaches have demonstrated effectiveness in promoting responsible AI governance and addressing its challenges. The OECD AI Principles provide a widely recognized framework for ethical, transparent, and accountable AI, guiding governments and organizations on human centered development. The UNESCO Recommendation on the Ethics of AI emphasizes inclusion, fairness, and human rights, offering a global normative reference. Regional frameworks such as the EU AI Act establish regulatory standards for high risk AI applications, including transparency and human oversight requirements. On the practical side, open source platforms like Hugging Face and Big Tech's open models enable collaboration, democratize access to advanced AI tools, and support reproducibility. Data sharing initiatives and federated learning approaches allow AI development while preserving privacy and respecting data sovereignty. Multi stakeholder partnerships such as the Global Partnership on AI facilitate dialogue between governments, academia, and the private sector to share best practices, align governance approaches, and support capacity building. Design thinking and policy lab methodologies provide structured approaches for co creating solutions, prototyping governance tools, and iteratively refining policies. Together, these policies, practices, and platforms demonstrate that effective AI governance combines normative guidance, operational frameworks, inclusive participation, and open innovation.