Ministry of Justice

A successful first Global Dialogue on AI Governance would be less about producing a single sweeping treaty and more about creating durable "coordination infrastructure" that can evolve with the technology. First, it would establish a shared baseline vocabulary and a set of widely endorsed principles—safety, transparency, accountability, human rights, and inclusion—that are specific enough to guide policy but flexible enough to accommodate different legal systems. The value is not novelty, but convergence: reducing fragmentation in how governments and companies define and assess AI risk. Second, success would look like agreement on practical interoperability mechanisms. This could include mutual recognition of certain risk assessment practices, shared documentation standards for advanced models, and voluntary reporting norms for frontier AI incidents. Even modest alignment here would significantly reduce regulatory gaps. Third, it would produce concrete cooperative tools: an international incident reporting channel, a repository of best practices, and a standing technical-scientific panel that can update risk assessments as capabilities evolve. These should be designed to be lightweight but credible, avoiding bureaucratic overload. Fourth, meaningful inclusion would be essential. A successful dialogue would ensure sustained participation from low- and middle-income countries, not just as observers but as co-authors of governance norms—paired with commitments to capacity building (funding, technical assistance, and access to safety tools). Finally, the dialogue should end with a road-map and clear follow-up milestones rather than a one-off declaration: timelines for pilot projects, recurring review meetings, and mechanisms to bring industry, civil society, and academia into ongoing governance. In general, success would mean moving from fragmented national conversations to a functioning global coordination system that can adapt as AI capabilities and risks continue to change

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

• These reflect the core requirements for reducing near-term risks while ensuring AI benefits are broadly accessible. Safety and trustworthiness address systemic technical and deployment risks
• human rights provides the normative baseline for governance
• transparency and oversight enable enforce ability and public trust
• and capacity-building is essential to prevent deepening global inequality in who can shape and benefit from AI systems.

Yes-there are a few cross-cutting and emerging issues that are not fully captured by the listed themes, or that sit awkwardly between them. One is compute and infrastructure governance. Much of AI capability is now shaped not just by models or data, but by access to large-scale compute, specialized chips, cloud platforms, and energy resources. Concentration in this layer creates structural dependencies and potential check points that are not explicitly addressed in the themes, yet strongly influence safety, competitiveness, and equity. A second is environmental and resource impact. The energy use, water consumption, and supply-chain implications of training and deploying large models are becoming increasingly significant. While related to "social, economic, ethical" implications, environmental constraints are often treated as secondary rather than a core governance concern, despite their growing material importance. Third, there is the issue of concentration of power and market structure. AI governance discussions often focus on model behavior, but fewer explicitly address the systemic effects of a small number of firms controlling frontier models, deployment channels, and data ecosystems. This has implications for accountability, innovation, and geopolitical balance. Fourth, security risks beyond traditional safety framing-including cyber-enabled crime, bio-security dual-use concerns, and model-assisted disinformation operations-are evolving faster than existing governance frameworks. These risks cut across "trustworthy AI" and "human rights" but often require more operational coordination than normative principles alone. Finally, long-term societal adaptation is under-emphasized: the need for education systems, labor market transitions, and institutional redesign to keep pace with AI-driven change. This is broader than capacity-building and involves sustained societal restructuring rather than one-time skill development. Together, these issues suggest that AI governance will need to extend beyond model focused regulation toward a more systemic view of infrastructure, power, and societal transformation.

In Egypt, governance gaps and uneven progress in AI-related policy and capacity are shaping both the risks and opportunities associated with AI adoption across public services, the private sector, and the wider digital economy. One of the most significant challenges is limited AI governance maturity relative to rapid digital transformation ambitions. AI is increasingly relevant in areas such as public service delivery, fin-tech, telecommunications, and content moderation, but frameworks for transparency, accountability, and algorithmic oversight are still developing. This can make it difficult to consistently assess system performance, ensure explain-ability in high-impact decisions, or establish clear responsibility when harms occur. A second challenge relates to AI capacity-building and talent retention. Egypt has a large, young, and increasingly digitally literate population, which is a major opportunity. However, there remains a gap between academic training, applied research capacity, and industry needs in advanced AI engineering, evaluation, and safety research. This can increase reliance on external technologies and limit domestic control over critical systems. In terms of human rights and trustworthy AI, key concerns include data protection, digital privacy, and equitable access to the benefits of AI systems, particularly as AI becomes more embedded in services that affect employment, credit, and public administration. Ensuring meaningful human oversight in these domains remains an ongoing governance priority. At the same time, there are clear opportunities. Egypt's scale, linguistic and cultural positioning in the Arab world, and growing digital infrastructure create potential for leadership in Arabic-language AI systems, regional digital services, and localized AI applications. Investments in governance frameworks, public-sector AI literacy, and interoperable standards could strengthen trust and attract innovation partnerships. Overall, the most significant dynamic is the balance between rapid adoption potential and the need to strengthen governance, skills, and institutional oversight to ensure safe, inclusive, and beneficial AI deployment.

The AI Dialogue can play a crucial role as a coordination platform between fragmented governance efforts, especially in a field where regulation is evolving unevenly across jurisdictions and technological change is rapid. First, it can help build convergence on shared minimum standards without requiring immediate binding treaties. By fostering agreement on baseline expectations—such as risk assessment practices, transparency norms, and safety testing approaches—the Dialogue can reduce regulatory divergence that would otherwise hinder cross-border innovation and oversight. Second, it can function as a bridge between technical and policy communities. Effective AI governance depends on translating rapidly evolving technical realities into actionable regulatory concepts. The Dialogue can institutionalize exchange between researchers, industry practitioners, and policymakers, ensuring that governance frameworks remain grounded in technical feasibility and current capability trends. Third, it can support early-warning and information-sharing mechanisms. As AI systems become more widely deployed, coordinated reporting of incidents, vulnerabilities, and misuse patterns will be increasingly important. The Dialogue can help design lightweight, trust-building mechanisms for sharing such information across borders. Fourth, it can strengthen capacity-building and inclusion, particularly for countries with limited regulatory and technical infrastructure. By facilitating training, shared tools (such as model evaluation benchmarks), and access to expertise, the Dialogue can reduce global disparities in governance readiness and prevent a two-tier AI ecosystem. Finally, it can provide continuity in global AI governance conversations, ensuring that cooperation does not depend on ad hoc summits or isolated initiatives. A standing dialogue structure can maintain momentum, track progress against agreed milestones, and adapt priorities as technology evolves. Overall, its most important role is to move the global system from fragmented national responses toward a coherent, adaptive, and inclusive governance architecture that can keep pace with AI development while preserving policy flexibility.

The AI Dialogue should build on a growing ecosystem of both multilateral and technical initiatives, while providing a higher-level coordination layer that connects them more effectively. On the multilateral side, relevant foundations include the OECD AI Principles and AI Policy Observatory, the G7 Hiroshima AI Process, the Global Partnership on AI (GPAI), and UNESCO's Recommendation on the Ethics of AI. These initiatives already provide normative frameworks, policy guidance, and in some cases practical tools for implementation. The AI Dialogue can help align these efforts, reduce duplication, and identify where principles translate into interoperable regulatory practices. On the technical and safety side, emerging work by standards bodies (ISO/IEC), model evaluation efforts, and industry-led safety commitments (including frontier AI safety frameworks and voluntary red-teaming practices) offer practical mechanisms for risk assessment and mitigation. The Dialogue could help surface best practices and encourage broader adoption beyond leading firms and advanced economies. It can also connect with UN system initiatives, such as digital cooperation work under the Office for Digital and Emerging Technologies, as well as capacity-building programs led by UN agencies that support digital transformation in developing countries. The added value of the AI Dialogue lies in its potential to serve as a neutral convening hub that integrates these parallel tracks. Unlike many existing mechanisms that are either region-specific, sector-specific, or voluntary industry commitments, the Dialogue can provide a global, inclusive space to: -Identify gaps between principles and implementation -Promote interoperability across regulatory regimes -Elevate perspectives from underrepresented countries -Coordinate capacity-building efforts to avoid fragmentation -Track emerging systemic risks in a unified way In essence, its role is not to replace existing initiatives, but to connect them into a more coherent global governance ecosystem that can adapt as AI capabilities and risks evolve.

Different stakeholders can contribute most effectively if the AI Dialogue is structured as a multi-layered, continuously operating forum rather than a single annual event. Governments should play a central role in setting priorities, sharing regulatory experiences, and identifying national needs and constraints. Their contribution would be strongest in structured peer-learning sessions where policies, enforcement challenges, and regulatory experiments are compared in a practical, non-prescriptive way. Industry and frontier AI developers can contribute technical expertise on model capabilities, limitations, and safety practices. Their involvement should be anchored in transparency expectations—such as sharing summaries of risk management approaches, evaluation methodologies, and incident learning—while ensuring appropriate safeguards for sensitive or proprietary information. Civil society organizations are essential for grounding the Dialogue in public interest concerns, including human rights, equity, and societal impacts. Their participation should be integrated into agenda-setting and evaluation processes, not limited to side events, to ensure accountability perspectives shape outcomes. Academia and technical experts can provide independent analysis, bench-marking methodologies, and foresight on emerging risks. A standing scientific advisory group could help synthesize evidence and translate it into policy-relevant insights. Countries with developing AI ecosystems should be enabled to participate meaningfully through dedicated support mechanisms, including travel funding, technical assistance, and capacity-building workshops. In terms of structure, the Dialogue would benefit from three interconnected layers: - A high-level annual forum for political alignment and priority-setting - Thematic working groups (e.g., safety, governance interoperability, capacity-building) that meet regularly to produce concrete outputs - A technical support and knowledge hub for data sharing, best practices, and training resources To remain effective, the Dialogue should emphasize continuity, with clear follow-up mechanisms, measurable milestones, and publicly accessible summaries of progress. This would ensure it evolves from a discussion platform into a functioning coordination system for global AI governance.

Several important voices remain underrepresented in global AI governance discussions, and their absence can skew both priorities and solutions. First, low- and middle-income countries, particularly from Africa, parts of Latin America, and smaller Asian and Pacific states, are often present in name but have limited influence on agenda-setting. This can result in governance models that reflect the capacities and constraints of highly industrialized AI ecosystems rather than global realities. Inclusion would require sustained funding for participation, regional preparatory forums, and technical assistance to support engagement in complex policy and standards discussions. Second, workers and labor organizations are often missing despite being directly affected by AI-driven automation, algorithmic management, and platform-based labor systems. Their inclusion could be strengthened through formal consultation mechanisms with trade unions, worker associations, and informal economy representatives, ensuring labor impacts are treated as core governance issues rather than downstream effects. Third, communities affected by AI systems at the local level—such as individuals subject to automated decisions in welfare, migration, credit, or policing systems—are rarely represented. Their perspectives could be incorporated through structured civil society input, participatory policy design processes, and case-based evidence gathering from real-world deployments. Fourth, indigenous communities and linguistic minorities are underrepresented in discussions about data governance, language models, and cultural impact. Inclusion requires targeted outreach and support for participation, as well as recognition of data sovereignty and linguistic diversity in AI development. Finally, technical safety researchers and independent auditors outside major corporate labs often have limited access to data and resources needed to meaningfully contribute. Expanding open evaluation frameworks, funding independent research, and enabling secure data access environments would help balance this gap. Overall, inclusion requires more than invitation—it requires structural support, capacity-building, and decision-making influence, ensuring that participation is meaningful rather than symbolic.

To foster meaningful and dynamic engagement, the AI Dialogue would benefit from formats that move beyond traditional plenary statements and instead emphasize interaction, iteration, and shared problem-solving. One effective approach would be scenario-based policy simulations ("AI governance drills"). Participants could work through realistic cases—such as a cross-border AI incident, a major model failure, or coordinated disinformation event—forcing governments, industry, and civil society to test how existing governance tools respond in practice. This helps expose gaps that abstract discussions often miss. Another useful format is technical-policy "translation labs." These sessions would pair policymakers with engineers and researchers to jointly unpack topics like model evaluation, compute governance, or auditability. The goal would be to reduce the gap between technical detail and regulatory design, producing shared language and actionable insights. The Dialogue could also use living working groups with rolling outputs, where recommendations are continuously updated and published rather than finalized at the end of a cycle. This would better match the speed of AI development and keep outputs relevant. Regional deep-dive sessions would strengthen inclusion by allowing different regions to contextualize global principles within local economic, cultural, and regulatory realities. These could feed directly into global-level synthesis discussions. In addition, public-interest foresight panels could bring together foresight experts, scientists, and affected communities to explore medium- and long-term AI trajectories. This would help ensure governance is not purely reactive but anticipatory. Finally, a transparent digital collaboration platform could allow asynchronous participation, document sharing, and iterative drafting of principles or standards between meetings, making engagement more continuous and accessible. Together, these formats would shift the Dialogue from a periodic consultation into a continuous, practice-oriented governance ecosystem, improving both the depth and inclusiveness of participation.

Several existing policies and practices already provide useful building blocks for effective AI governance, even if none is fully comprehensive on its own. On the policy side, the EU AI Act is a leading example of a risk-based regulatory framework that differentiates obligations based on the level of harm potential. Its structured approach to high-risk systems, conformity assessments, and transparency requirements offers a concrete model for translating broad principles into enforceable rules, even though implementation will be complex. The OECD AI Principles and UNESCO's Recommendation on the Ethics of AI provide widely endorsed normative foundations. While not legally binding, they have been influential in aligning national strategies and creating a shared vocabulary for trustworthy AI, particularly around fairness, accountability, and human rights. In practice, algorithmic impact assessments (AIAs) used in some public-sector contexts (e.g., in parts of Canada and select municipal governments) are a strong tool for embedding accountability before deployment. These structured assessments help identify risks, document decision logic, and establish mitigation plans. From the industry side, frontier AI safety frameworks and practices such as red-teaming, model evaluations, and staged deployment ("release with safeguards") demonstrate how technical risk management can be ope-rationalized. When paired with independent audits, these approaches can significantly improve system reliability. In terms of platforms, open-source model ecosystems (such as widely used foundation model repositories) contribute to transparency and research access, though they also highlight the need for complementary safety and misuse mitigation mechanisms. Finally, international coordination forums like GPAI and the G7 AI process illustrate the value of policy experimentation and peer learning across jurisdictions. Taken together, these examples show that effective AI governance is emerging through a combination of regulatory frameworks, technical safety practices, and collaborative international mechanisms, rather than a single unified model.