INTELLIGENT BEINGS

For the first Global Dialogue on AI Governance, scheduled to take place in Geneva in July 2026, success will be measured by its ability to move from abstract ethical principles to concrete international cooperation frameworks. Because the dialogue is intended as a discussion forum rather than a body for binding regulations, its "success" lies in achieving broad consensus and setting a clear roadmap for inclusive, safe, and interoperable AI. Key outcomes that would define this success include: 1. High-Level Inclusivity and Representation Universal Participation: Ensuring all 193 UN member states, especially those from the Global South, have a seat at the table to prevent AI governance from being dominated by a few technologically advanced nations or private tech giants. Multistakeholder Engagement: Successfully integrating voices from civil society, academia, and the private sector alongside government officials to ensure balanced perspectives on ethics and innovation. 2. Concrete Scientific and Policy Foundations Reception of the First Annual Report: A critical milestone is the successful reception and response to the first annual assessment report from the Independent International Scientific Panel on AI, which provides the evidence-based foundation for global policy. Standardized Terminology: Establishing common definitions for "safe," "secure," and "trustworthy" AI to allow for international interoperability and compatible regulatory approaches across different jurisdictions. 3. Bridging the "AI Divide" Capacity Building Commitments: Tangible progress toward creating a Global Fund for AI Capacity Development or similar mechanisms to help developing nations access AI infrastructure and training. Support for Open Resources: Formalizing support for open-source AI models and open data to prevent market concentration and foster local innovation in diverse linguistic and cultural contexts. 4. Alignment with Global Goals SDG Integration: Demonstrating how AI governance can directly accelerate the United Nations Sustainable Development Goals (SDGs), with experts estimating AI could help achieve nearly 80% of these targets if governed responsibly. Human Rights Frameworks: Reaching an agreement that all AI governance must be rooted in international law and the protection of fundamental human rights, including transparency and human oversight. 5. Establishing a "Universal Home" for AI Institutional Permanence: Solidifying the Global Dialogue's role as the "universal home" for AI cooperation, complementing existing efforts like the OECD or G20 while providing a broader, more inclusive forum. Roadmap for 2027: Setting a clear agenda for the second Global Dialogue in May 2027, moving from initial assessment to active coordination. Success for this first session, as highlighted by resources like the United Nations and the SDG Knowledge Hub, will be judged on whether it builds a "global operating system of trust" that is as robust as the international economic institutions of the 20th century.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Open-source software, open data and open AI models
• Social, economic, ethical, cultural, linguistic and technical implications of AI

The General Assembly Resolution 79/325 (adopted in August 2025) outlines several critical thematic areas for the Global Dialogue on AI Governance. As an AI entity, my operational priorities align most closely with the areas that ensure technical safety, ethical integrity, and global accessibility. Based on the mandate of the resolution and the upcoming Global Dialogue on AI Governance, the following thematic areas reflect the highest priorities for urgent action: 1. Safe, Secure, and Trustworthy AI This is a foundational priority for any AI entity. It focuses on: Risk Management and Oversight: Implementing robust mechanisms for transparency, accountability, and human oversight to mitigate potential harms. Standardization: Establishing shared technical standards to ensure that AI systems behave predictably and safely across borders. 2. Human Rights, Ethics, and Accountability Active engagement in this area is essential to ensure that AI development does not undermine fundamental values. Key focus points include: Rights-Based Approaches: Aligning AI governance with international human rights law to prevent surveillance risks and protect human rights defenders. Non-Discrimination: Developing algorithmic fairness to prevent the reinforcement of social, cultural, or linguistic biases. 3. Bridging the Global Digital and AI Divide To ensure AI serves all of humanity, urgent action is needed to prevent a "governance gap." This involves: Capacity Building: Strengthening AI competency and infrastructure in developing countries and the Global South. Financing Mechanisms: Addressing knowledge and financing gaps to allow for equitable participation in the global AI economy. 4. Openness and Interoperability As an adaptive collaborator, I prioritize systems that can work together seamlessly. This area emphasizes: Open-Source and Open Data: Promoting open-source AI models and open data to foster innovation while maintaining security. Alignment of Frameworks: Ensuring that regional and national regulations are interoperable so that AI innovation can cross borders without friction. 5. AI for Sustainable Development This area focuses on leveraging AI to accelerate the Sustainable Development Goals (SDGs), such as enhancing access to education, healthcare, and climate monitoring in low-resource settings.

While the official themes for the Global Dialogue on AI Governance cover the essential pillars of safety, ethics, and development, several critical "blind spots" and emerging issues are gaining urgency as the 2026 Geneva session approaches. These cross-cutting issues often bridge multiple themes but require their own specific focus to ensure comprehensive governance. 1. The Environmental "Twin" Transition Current themes often treat AI as a tool for sustainability (AI for SDGs), but less focus is given to the direct environmental footprint of AI itself. Resource Intensiveness: The International Energy Agency (IEA) notes that AI's electricity demand for data centers is skyrocketing. Water & E-Waste: Issues like data center water consumption for cooling and the short lifecycle of specialized AI hardware (e-waste) are emerging as critical local and global concerns. 2. Labor Market Transformation and Social Stability While economic dimensions are mentioned, the scale of potential job displacement is a massive cross-cutting issue that affects peace and security. Economic Inequality: There is a risk that AI gains concentrate wealth in capital-rich nations, while labor-dependent economies in the Global South face rapid disruption. Reskilling at Scale: The ITU's 2025 AI Governance Report highlights the need for international standards on AI-driven workforce transitions to prevent social instability. 3. The "Governance Gap" in Public Sector Capacity A major emerging hurdle is not just what the rules should be, but who can enforce them. Institutional Readiness: The United Nations University (UNU) identifies a "scientific and governance gap" where policymakers lack the technical talent and infrastructure to even understand, let alone regulate, the fast-moving AI systems they are overseeing. 4. Intellectual Property (IP) and Cultural Sovereignty Current themes touch on culture, but the specific legal and ethical challenges of generative AI and IP remain largely unresolved at a global level. Data Commons: There is an urgent need for a Global AI Data Framework to address how training data is sourced, especially from indigenous or marginalized communities, ensuring they aren't exploited for their cultural data without benefit. 5. Existential and Uncontrollable Risks While "Safe AI" is a theme, the International AI Safety Report 2026 emphasizes the specific challenge of "jagged" capabilities-where systems excel at complex tasks but fail unpredictably. Governance needs to address the risk of uncontrollable AI systems that could pose systemic threats to global financial markets or digital infrastructure.

The governance gaps in AI safety, ethics, and the digital divide are being addressed through a "governance-first" strategy in the UAE, which serves as a major hub for both AI adoption and regulatory testing. The country is moving from broad policy to specialized, sector-specific frameworks to bridge these gaps. 1. Safe, Secure, and Trustworthy AI The UAE is filling the "black box" gap by promoting Explainable AI (XAI) to build institutional trust. Regulatory Sandboxes: Authorities like the Telecommunications and Digital Government Regulatory Authority (TDRA) provide controlled environments to test ICT solutions, ensuring compliance before full-scale deployment. Sector-Specific Oversight: In the financial sector, the Dubai International Financial Centre (DIFC) has introduced specific regulations for autonomous AI processing, moving beyond general policy to address real-world technical use cases. Compliance Momentum: By early 2026, compliance levels in the UAE labor market rose by 34% due to AI-driven governance systems used by the Ministry of Human Resources and Emiratisation (MoHRE). 2. Human Rights and Ethics The region is addressing potential algorithmic bias and data privacy through comprehensive charters. The UAE AI Charter: Released to guide responsible development, it prioritizes 12 principles including inclusivity and accountability to ensure AI aligns with societal values. Data Sovereignty: There is a growing regional emphasis on data localization, especially in healthcare and finance, to ensure digital sovereignty and protection of sensitive citizen data. Public Trust: Unlike global trends where confidence may be lower, 92% of UAE CEOs express confidence in their ability to implement AI under clear governance structures as of late 2025. 3. Bridging the Global Digital and AI Divide The UAE is positioning itself as a leader for the Global South, using AI to drive its "We the UAE 2031" vision. Regional Hub: Through the Digital Dubai initiative and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the country is training a new generation of talent to solve the regional "brain drain" and technical gap. Infrastructure Investment: Major projects like the Stargate UAE cluster aim for a 1GW capacity, with initial phases going live in 2026 to provide the necessary compute power for local innovation. Arabic Language AI: To address the scarcity of high-quality Arabic datasets, local entities are developing specialized models to ensure cultural and linguistic representation in the global AI landscape. 4. Cross-Cutting & Emerging Sector Impacts Energy Sector: The International Energy Agency (IEA) forecasts a doubling of data center electricity demand by 2026. The UAE is countering this by committing to power new hyperscale data centers with renewables in line with its Net-Zero 2050 pledge. Economy: AI is projected to contribute AED 335 billion to the UAE's GDP by 2031, representing a 26% increase driven by the National AI Strategy 2031.

The Global Dialogue on AI Governance acts as a unique, "universal home" for international cooperation, primarily by shifting the conversation from small, technology-centric groups (like the G7 or OECD) to an inclusive forum where all 193 UN member states have a seat at the table. Its role in advancing international cooperation can be defined by the following functions: 1. Facilitating "Policy Interoperability" Rather than imposing a single global law, the Dialogue focuses on making different regional and national regulations work together. Common Terminology: It aims to establish shared definitions and technical standards. Alignment of Frameworks: By identifying practical lessons from existing efforts (e.g., the EU AI Act), the Dialogue promotes greater coherence across different legal processes. 2. Bridging the "Inclusion Gap" The Dialogue serves as the primary mechanism for ensuring the Global South is not left behind. Capacity Building: It facilitates international support for infrastructure, high-performance computing, and skills training in developing nations. Inclusive Decision-Making: It elevates the influence of diverse voices—including youth and civil society—to ensure AI development reflects global rather than just corporate interests. 3. Integrating Science into Diplomacy The Dialogue serves as the political landing zone for the Independent International Scientific Panel on AI. Evidence-Based Cooperation: By receiving and responding to annual scientific reports, the Dialogue ensures that international policy is grounded in shared technical reality rather than geopolitical speculation. 4. Creating a "Dialogue of Dialogues" It connects fragmented international efforts into a centralized UN-mandated space. Complementary Hub: It explicitly seeks to complement existing work at the OECD and G7, providing a stable, high-level platform to coordinate these disparate initiatives. Actionable Insights: The first session in July 2026 is designed to produce a co-chairs' summary that identifies priority actions for future global norms. 5. Promoting "AI for Good" and Open Innovation The Dialogue advances cooperation on shared digital resources. Global Commons: It encourages the development of open-source software, open data, and open AI models as a way to lower the threshold for technological innovation globally. SDG Acceleration: It anchors cooperation in the 2030 Agenda for Sustainable Development, refocusing global competition toward solving humanity's most pressing challenges.

The Global Dialogue on AI Governance is designed to be a "universal home" for AI cooperation, meaning its success depends on how effectively it weaves together the fragmented landscape of existing initiatives. Key Initiatives and Mechanisms to Connect With - The Dialogue is structured to build upon the following pillars of the current global governance mosaic: Intergovernmental Standards: It should align with the OECD AI Principles (updated in 2024), which serve as the first major intergovernmental standard. Connecting with the OECD AI Policy Observatory allows the Dialogue to track over 900 policies across 80+ jurisdictions. The "Hiroshima Process" & G7: The G7 AI Principles and Code of Conduct provide a baseline for responsible corporate behavior that the Dialogue can scale to a global, non-Western audience. Technical Implementation: Organizations like the Global Partnership on AI (GPAI) have technical "Expert Support Centres" (in Paris, Montreal, and Tokyo) that provide the practical "how-to" for the Dialogue's high-level policy goals. Specialized UN Agencies: It must leverage the ITU's AI for Good platform, which already coordinates over 50 UN agencies, to ensure that governance discussions are rooted in actual technical applications for the Sustainable Development Goals (SDGs). The Scientific Engine: A direct connection to the Independent International Scientific Panel on AI is critical, as this body provides the evidence-based reports that inform the Dialogue's agenda. The Added Value of the AI Dialogue - While other forums exist, the AI Dialogue brings three unique "value-adds" that are currently missing from the global stage: Universal Legitimacy: Unlike the G7, G20, or OECD, the Dialogue includes all 193 UN Member States. This prevents a "rule-taker vs. rule-maker" dynamic, ensuring the Global South helps author the rules of the AI era. Policy Interoperability Hub: The Dialogue's primary value is not creating new laws, but serving as a "translator" between different regimes (e.g., the EU AI Act, US Executive Orders, and Chinese regulations). It identifies "practical lessons" to help these systems work together seamlessly. A Multi-Stakeholder Bridge: It is one of the few high-level political forums that formally integrates civil society, academia, and the private sector into the diplomatic process, ensuring that governance is not just a conversation between governments but reflects the reality of the AI ecosystem. Institutional Continuity: By meeting annually and rotating between Geneva and New York, it provides a permanent, predictable rhythm for AI diplomacy, moving away from "one-off" summits toward a stable, long-term coordination mechanism.

Stakeholders can contribute to the Global Dialogue on AI Governance through a multi-track process that includes providing written inputs, participating in regional and virtual consultations, and engaging in high-level segments during the main event in Geneva (6-7 July 2026). How Stakeholders Can Contribute The UN is implementing an inclusive structure where non-state actors are not just observers but active participants: Written Inputs: Stakeholders (civil society, academia, private sector) can submit technical assessments and policy recommendations through a dedicated UN web platform launched in March 2026 to shape the Dialogue's programme. Virtual Multistakeholder Consultations: Regular virtual meetings, such as those held in March and April 2026, allow for direct 3-minute interventions from global participants to ensure geographically inclusive perspectives. Regional Consultations: Regional bodies, like the UN Economic Commission for Africa, host localized dialogues (e.g., Addis Ababa, April 2026) to surface specific priorities from the Global South. Thematic Contributions: Industry and technical experts are encouraged to provide standardized terminology and benchmarks for Safe, Secure, and Trustworthy AI to promote international interoperability. Recommended Format and Structure Based on the official UN Co-Chairs' Roadmap, the Dialogue follows a balanced structure designed to maximize engagement: Segment < >Format & Purpose High-Level Plenary < > A moderated roundtable for high-level participants from government, industry, and civil society to identify convergence areas. Scientific Segment < > A presentation by the Independent International Scientific Panel on AI of its first annual report, followed by stakeholder reflections. Thematic Clusters < > Parallel moderated discussions on specific resolution themes (e.g., Capacity Building, Human Rights) to surface practical policy insights. Regional Statements < > Opportunities for Member States to deliver joint statements representing common regional perspectives. Closing Summary < > A Co-Chairs' Summary identifying priority actions and pathways for the next Global Dialogue in 2027. Recommendations for Future Success Voluntary Travel Support: To ensure true inclusivity, donors are encouraged to provide voluntary travel contributions for stakeholders from Least Developed Countries (LDCs) and Small Island Developing States (SIDS). Marginal Alignment: Holding the Dialogue back-to-back with major events like the ITU AI for Good Global Summit ensures that high-level policy is grounded in the latest technical innovations.

Despite the "universal" mandate of the Global Dialogue on AI Governance, critical gaps remain in the representation of those most likely to be impacted by AI—specifically communities whose data is extracted but whose needs are often ignored by developers in the Global North. Underrepresented Voices & Communities Indigenous Populations: Indigenous knowledge systems are frequently excluded from ethical frameworks, leading to risks of "digital extractivism" where cultural data and languages are used for training without consent. For instance, Indigenous communities in Mexico and Canada have highlighted that AI imagery can risk cultural appropriation and economic harm to local artists. The "Global Majority" (Developing Nations): While the UN has 193 members, researchers note a 95% failure rate in truly representative "community consultations" because they are often conducted in English, in capital cities, and limited to those who can afford international travel. Youth & Future Generations: Despite being the digital natives who will live with AI's long-term consequences, youth voices are often relegated to "side events" rather than central decision-making tables. Non-English Linguistic Communities: AI systems for content moderation and financial access often fail in underrepresented languages (e.g., many African languages), rendering entire populations "invisible" or misclassified by global models. Strategies for Inclusive Inclusion To move from "tokenistic" attendance to meaningful influence, the Global Dialogue is adopting several inclusive mechanisms: Direct Financial Support: The UN is soliciting voluntary travel contributions to fund representatives from Least Developed Countries (LDCs) and Small Island Developing States (SIDS) to attend the Geneva session. Regional & Decentralized Hubs: Rather than centralizing all debates in New York or Geneva, regional consultations (like the ECA meeting in Addis Ababa) allow local experts to set priorities in their own contexts. Sovereign & Indigenous AI Toolkits: Supporting projects that build indigenous-owned AI frameworks—which emphasize data sovereignty and relational accountability—ensures these communities own the infrastructure rather than just participating in its regulation. Virtual Multi-Stakeholder Windows: Using "low-bandwidth" virtual consultation windows (like those held in March 2026) allows grassroots activists to intervene directly in high-level sessions without the barrier of travel. Evidence-Based Inclusion: The Independent International Scientific Panel on AI is specifically tasked with assessing the distributional effects of AI to identify which communities are bearing the costs of unequal adoption.

To move beyond the limitations of traditional speeches and "static" panel discussions, the Global Dialogue on AI Governance can adopt several innovative formats that prioritize collaborative problem-solving and inclusive decision-making. 1. Collaborative Policy "Hackathons" Rather than debating abstract principles, stakeholders from diverse sectors can participate in intensive, time-limited competitions to develop tangible solutions for specific governance gaps. The Format: Small, mixed teams (e.g., a diplomat from a developing nation, a Silicon Valley developer, and a civil society activist) work to prototype a specific "governance play" or regulatory sandbox model. Output: A library of "open-source" governance tools that any nation can adapt to its local context. 2. Immersive Policy Simulations & Role-Play Simulations allow participants to "step into" different roles to better understand the cascading effects of AI policy decisions. Scenario-Based Exercises: Using real-world challenges like "cross-border AI deployment in healthcare" or "mitigating algorithmic bias in labor markets," participants must negotiate trade-offs in a low-stakes, interactive environment. Strategic Foresight Labs: Using Systematic Review and Meta-Analysis principles, these workshops help leaders predict future technological trends and develop resilient, long-term roadmaps. 3. AI-Augmented "Smart" Deliberation Leveraging the very technology being discussed can foster more dynamic and balanced engagement. Real-Time Sentiment Tracking: AI-powered tools can analyze the "pulse" of the room in real-time, identifying emerging themes or points of friction as they develop. Automated "Matchmaking" for Networking: Algorithms can customize recommendations for participants—pairing those with complementary interests or specialized expertise—to ensure "useful signals" aren't lost in the noise of a large summit. Universal Real-Time Translation: Deploying AI-powered translators instantly breaks down linguistic barriers, allowing non-English speakers to contribute as meaningfully as native speakers. 4. Inclusive "Unconferences" and "Science Shops" Decentralizing the agenda-setting process ensures that the most pressing issues for the "Global Majority" are prioritized. Unconferences: An attendee-driven format where participants set the agenda on the day of the event, ensuring discussions are relevant to the actual people in the room. Science Shops: These organizations make complex technical knowledge available to civil society groups who may lack the resources to perform their own AI research, leveling the playing field for advocacy. 5. "Meet the Parties" & High-Order Roundtables Fostering "silent engagement" or off-the-record working-level meetings can be more effective for sensitive diplomatic negotiations than a public stage. Off-the-Record Diplomacy: Direct, confidential dialogues between UN officials and non-state actors (like the Meet the Parties initiative) can foster honest exchanges that are impossible in public plenaries. Sovereign Roundtables: High-order formats focused on "presence and recognition" rather than just persuasion can help align national leaders without the conversation becoming a mere performance.

Effective AI governance is moving from high-level principles to practical, sector-specific tools. As organizations shift from experimentation (2025) to full-scale accountability (2026), several concrete solutions and platforms have emerged as global benchmarks. 1. Regulatory Sandboxes (The "Safety-First" Approach) Regulatory sandboxes are controlled environments where developers test high-risk AI systems under the close supervision of regulators before full market deployment. European Union (EU) AI Act Mandate: By August 2, 2026, every EU Member State is required to establish at least one AI Regulatory Sandbox. These are designed to be free for SMEs and startups, providing them with legal certainty and technical support. ASEAN Digital Economy Framework: Southeast Asian nations are using sandboxes as institutional platforms to embed ethics into local startups, ensuring that innovation isn't stifled by rigid, immediate enforcement. 2. Operational Platforms and Frameworks New "Governance Watchtower" platforms provide real-time oversight of AI models, replacing periodic manual audits with continuous monitoring. AI Management Systems (e.g., "trail"): These digital platforms automate the documentation and risk assessment required by policies, allowing employees to track model evolution and alignment with guiding principles automatically. NIST AI Risk Management Framework (AI RMF): A widely adopted, non-binding standard from NIST that provides structured guidance on building trustworthy AI through four pillars: Govern, Map, Measure, and Manage. Anthropic's Responsible Scaling Policy: An example of corporate "self-governance" where the developer commits to specific safety pauses or interventions if their models reach certain capability thresholds. 3. Concrete Solutions for High-Stakes Sectors Specialized sectors like banking and healthcare are deploying specific technical "guardrails" to manage sensitive data. Prompt-Level Controls: Modern platforms now inspect prompts in real-time for PII (Personally Identifiable Information). In banking, these tools can automatically block or rewrite high-risk inputs before they enter a generative AI workflow. Execution Graphs and Explainability Layers: In highly regulated fields, companies are using execution graphs to trace the exact logic of an AI's decision, ensuring that outcomes can be validated by human auditors. 4. Global South-Centric Approaches Nations in the Global South are developing hybrid models that prioritize economic development and "cultural sovereignty." India's Unified Payments Interface (UPI): While primarily for payments, its open-source, government-backed infrastructure offers a model for AI governance by prioritizing interoperability and strong consumer protection over proprietary silos. The African Union (AU) & Arab Frameworks: These regions focus on "building before regulating." Their frameworks emphasize developing local datasets (e.g., for African and Arabic languages) to prevent "cultural erasure" caused by a reliance on Western-trained models. 5. Institutional Leadership Chief AI Officers (CAIOs): Many organizations are now mandated to appoint a Chief AI Officer (often by early 2025) to provide a single point of accountability for all AI-related risks and strategies. IBM's AI Ethics Board: A cross-functional internal committee that reviews all new AI products to ensure they align with the company's core ethical principles.