Jeux Olympiques de la Jeunesse Dakar 2026 - Institut des Algorithmes du Sénégal

A successful first Global Dialogue on AI Governance would establish a shared foundation for international cooperation that is both inclusive and action-oriented. Key outcomes should include: A common understanding of principles for safe, ethical, and human-centered AI, aligned with international human rights standards. Concrete commitments from governments, private sector actors, and civil society to collaborate on governance frameworks, rather than remaining at the level of general discussion. Practical mechanisms for coordination, such as working groups, knowledge-sharing platforms, and follow-up processes to ensure continuity beyond the dialogue. Inclusion of Global South perspectives, ensuring that developing countries are not only represented but actively shape global AI governance priorities. Capacity-building initiatives, including technical assistance, funding, and training programs to reduce global inequalities in AI development and deployment. Trust-building across stakeholders, particularly between governments, technology companies, and citizens, through transparency and accountability commitments. Ultimately, success would mean moving from fragmented national approaches toward a more coherent, cooperative global framework that balances innovation with safety, equity, and respect for human dignity.

• Safe, secure and trustworthy AI
• AI capacity-building
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

These priorities reflect the need to ensure that AI development is both responsible and inclusive. First, safe, secure and trustworthy AI is essential to prevent harm, including misuse, bias, and unintended consequences. Without trust, AI adoption will face resistance and risks undermining public confidence. Second, AI capacity-building is critical to address global inequalities. Many countries, particularly in the Global South, lack the infrastructure, expertise, and resources needed to participate meaningfully in AI development and governance. Strengthening capacity ensures more equitable participation and benefits. Third, the protection and promotion of human rights must remain central. AI systems can significantly impact privacy, freedom of expression, non-discrimination, and access to opportunities. Embedding human rights safeguards helps ensure that AI serves society as a whole. Finally, transparency, accountability, and human oversight are necessary to make AI systems explainable and governable. Clear accountability frameworks and human-in-the-loop approaches help mitigate risks and ensure that decisions remain subject to oversight. Together, these priorities balance innovation with responsibility and equity.

Yes, several important cross-cutting and emerging issues deserve greater attention. One key issue is the environmental impact of AI, including energy consumption, carbon emissions, and resource use associated with large-scale models and data centers. Sustainable AI development should be integrated into governance discussions. Another emerging concern is data sovereignty and ownership, particularly for developing countries. There is a need to ensure fair access to data and prevent extractive practices where local data is used without equitable benefit-sharing. AI and labor transformation is also critical. Automation and AI-driven systems are reshaping job markets, requiring proactive policies on reskilling, social protection, and inclusive economic transition. Additionally, the concentration of power in a few technology companies raises concerns about competition, innovation, and global equity. Governance frameworks should address market dominance and ensure fair access to AI technologies. Finally, cultural and linguistic diversity in AI systems remains underrepresented. Ensuring that AI supports diverse languages and cultural contexts is essential for global inclusivity. Addressing these issues will strengthen the relevance and long-term impact of global AI governance efforts.

Governance gaps in AI are already having tangible impacts, particularly in developing regions, where regulatory frameworks and technical capacity are still evolving. One of the most significant challenges is the lack of robust regulatory and institutional frameworks for AI. This creates uncertainty for both public and private actors, limiting responsible adoption while increasing exposure to risks such as biased algorithms, misinformation, and data misuse. Weak enforcement mechanisms also make it difficult to ensure accountability and transparency in AI systems. A second major challenge is the capacity gap. Limited access to infrastructure, expertise, and funding constrains the ability of local institutions and businesses to develop, deploy, and govern AI effectively. This risks widening the global digital divide and increasing dependency on external technologies that may not align with local needs or values. There are also human rights concerns, particularly around data protection, surveillance, and potential discrimination. In contexts where legal safeguards are still developing, AI systems can unintentionally reinforce inequalities or undermine trust in digital technologies. At the same time, these developments present important opportunities. AI has strong potential to accelerate progress in sectors such as healthcare, education, agriculture, and public services, especially when adapted to local contexts. It can improve efficiency, expand access, and support evidence-based decision-making. Moreover, the current stage of AI governance offers an opportunity for countries in the Global South to actively shape emerging global norms, rather than simply adopting external models. By investing in capacity-building and inclusive governance, countries can position themselves as both users and contributors to responsible AI innovation.

The AI Dialogue can play a critical role as a neutral, inclusive, and multistakeholder platform to strengthen international cooperation on AI governance. First, it can help bridge fragmentation by aligning existing national, regional, and sectoral approaches. By facilitating dialogue among governments, the private sector, academia, and civil society, it can promote convergence around shared principles, standards, and best practices while respecting different contexts. Second, the Dialogue can serve as a space to amplify the voices of underrepresented regions, particularly developing countries. Ensuring meaningful participation from the Global South will help shape governance frameworks that are more equitable, context-sensitive, and globally legitimate. Third, it can catalyze practical cooperation, moving beyond high-level discussions toward concrete outcomes such as joint initiatives, technical working groups, and capacity-building programs. This includes fostering partnerships for knowledge transfer, infrastructure development, and skills training. Fourth, the Dialogue can support trust-building among stakeholders. Transparent exchanges on risks, safeguards, and policy approaches can reduce tensions and promote responsible innovation. Finally, it can act as a coordination hub, identifying gaps, avoiding duplication of efforts, and encouraging coherence across existing initiatives. By doing so, it can accelerate the development of a more interoperable and effective global AI governance ecosystem.

The AI Dialogue should build upon and connect with existing international and regional initiatives to avoid duplication and enhance coherence. Key initiatives include the UNESCO Recommendation on the Ethics of Artificial Intelligence, which provides a global normative framework; the OECD AI Principles, which guide responsible AI development; and the Global Partnership on AI (GPAI), which promotes collaboration between governments and experts. Regional efforts such as the European Union AI Act and the African Union AI Strategy also offer important policy models and context-specific approaches. In addition, initiatives led by international organizations, development banks, and multistakeholder coalitions—such as capacity-building programs and digital cooperation frameworks—should be integrated into the broader ecosystem. The added value of the AI Dialogue lies in its ability to act as a convening and connecting platform across these efforts. It can: Foster greater interoperability between governance frameworks Promote inclusive participation, especially from developing countries Translate principles into practical, implementable actions Identify gaps and emerging challenges not yet addressed Encourage resource mobilization and coordination for capacity-building By linking existing initiatives and promoting coherence, the AI Dialogue can strengthen collective impact and help move toward a more unified, effective global approach to AI governance.

Different stakeholders should contribute according to their expertise while engaging in a genuinely collaborative process. Governments can share regulatory experiences, policy priorities, and national strategies. Private sector actors can provide technical insights, risk assessments, and implementation perspectives. Academia and technical experts can contribute evidence-based research and foresight on emerging risks and opportunities. Civil society and NGOs can highlight human rights, inclusion, and societal impacts. International organizations can support coordination and norm-setting. To ensure effectiveness, the AI Dialogue should adopt a multilayered structure: High-level plenaries to set political direction Thematic working groups for in-depth discussions and drafting recommendations Regional consultations to capture diverse contexts and priorities Open consultation mechanisms (written inputs, digital platforms) It should also include clear follow-up mechanisms, such as action plans, timelines, and periodic review meetings, to move from dialogue to implementation.

Several key groups remain underrepresented in global AI governance discussions. These include: Developing countries, particularly from Africa, small island states, and least developed countries Local communities and grassroots organizations, who are directly affected by AI systems but rarely consulted Women and gender-diverse groups, who are often excluded from technical and policy spaces Youth and future generations, who will experience long-term impacts of AI Linguistic and cultural minorities, whose needs are often overlooked in AI development Small and medium-sized enterprises (SMEs) and local innovators To ensure inclusion, the AI Dialogue should: Provide financial and logistical support for participation (travel, connectivity, translation) Enable remote and hybrid participation Organize regional and community-level consultations ahead of global meetings Promote multilingual engagement and accessible formats Establish quotas or representation targets to ensure diversity Meaningful inclusion requires not just participation, but influence in decision-making processes.

To foster meaningful and dynamic engagement, the AI Dialogue should go beyond traditional panel discussions. Innovative formats could include: Interactive workshops and co-creation labs, where participants jointly develop policy solutions or frameworks Scenario-based simulations, exploring real-world AI governance challenges (e.g., crisis response, AI misuse) Multi-stakeholder roundtables, designed for small-group, solution-oriented discussions Policy hackathons, bringing together diverse actors to rapidly prototype governance tools or guidelines Digital collaboration platforms, enabling continuous engagement before and after the Dialogue Case study clinics, where countries or organizations present concrete challenges and receive peer feedback Youth and community forums, ensuring bottom-up perspectives are integrated Blending in-person and virtual formats can increase accessibility and participation. These approaches can make the Dialogue more practical, inclusive, and outcome-driven, helping translate discussions into actionable solutions.

Several existing policies and practices offer valuable lessons for effective and inclusive AI governance. At the global level, the UNESCO Recommendation on the Ethics of Artificial Intelligence provides a comprehensive normative framework grounded in human rights, transparency, and accountability. It is particularly relevant for countries seeking adaptable guidance aligned with international standards. The OECD AI Principles have also been influential in promoting trustworthy AI, offering practical guidance on fairness, robustness, and accountability that has been adopted by multiple countries and organizations. At the regional level, the European Union AI Act represents a risk-based regulatory approach, categorizing AI systems according to their potential impact and applying proportionate obligations. This model provides a concrete example of how to operationalize governance while supporting innovation. Emerging regional initiatives such as the African Union AI Strategy highlight the importance of context-specific approaches, focusing on capacity-building, data governance, and inclusive development tailored to regional needs. In terms of practical tools, algorithmic impact assessments (AIAs) are increasingly used to evaluate risks before deployment, particularly in the public sector. Similarly, AI ethics review boards and independent auditing mechanisms help strengthen accountability and oversight. Open and collaborative approaches, such as open-source AI frameworks and shared datasets, can also promote transparency, innovation, and equitable access, especially when combined with safeguards for privacy and fairness. Finally, regulatory sandboxes provide a flexible environment for testing AI systems under supervision, enabling innovation while managing risks. Together, these examples demonstrate that effective AI governance requires a combination of principles, regulation, practical tools, and inclusive international cooperation.