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For the first Global Dialogue on AI Governance to be considered a success, it must move beyond high-level "principles" and establish a framework for interoperable oversight. A successful outcome would be defined by the following three pillars: 1. Unified Risk Taxonomy Success requires a shared technical language. Participants must agree on what constitutes "high-risk" AI, whether that is defined by its application in critical infrastructure or its potential for autonomous escalation. Without a common baseline, international cooperation will remain fragmented, leading to "governance arbitrage" where companies move operations to the least regulated jurisdictions. 2. A Multilateral Research & Monitoring Body The dialogue should lay the groundwork for an international body, similar to the IPCC for climate change, tasked with monitoring AI capabilities and safety milestones. A successful outcome would include a commitment to pooled resources for Safety Testing and Evaluation (T&E), ensuring that smaller nations aren't left behind in the race to understand frontier models. 3. Concrete "Red Lines" on Existential Risks The dialogue is a success if it achieves a binding consensus on non-negotiable boundaries. This includes: Prohibiting fully autonomous lethal weapons systems. Restricting AI-assisted biological or chemical weapon synthesis. Establishing "kill-switch" protocols for systems that exhibit deceptive behavior during training. 4. Inclusion of the Global South True success means ensuring that AI governance isn't just a "Club of Two" (the U.S. and China) or a Western-centric policy. A successful dialogue must result in a roadmap for equitable access to compute and data sovereignty, ensuring that the benefits of AI are distributed as widely as its risks are managed. Ultimately, success is not a signed statement; it is the creation of a living mechanism that evolves as quickly as the algorithms it aims to govern.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• AI capacity-building

1. Interoperability of Governance Approaches AI is inherently transboundary. Without interoperable frameworks, we risk a "splinternet" of regulation where innovation is stifled by conflicting rules. Prioritizing this ensures that safety standards in one region are recognized in another, creating a predictable environment for global research and trade. 2. AI Capacity-Building Governance is moot if only a handful of nations control the technology. Capacity-building is the bridge to equity; it ensures that the "Global South" has the technical infrastructure and human capital to not only use AI but to govern its implementation locally. This prevents a new era of digital colonialism. 3. Safe, Secure, and Trustworthy AI Technical safety is the floor upon which all other rights rest. Without rigorous, standardized benchmarks for "safety," systems remain vulnerable to adversarial attacks or unintended behaviors. Urgent action here creates the "guardrails" necessary to prevent catastrophic failure in critical infrastructure. 4. Transparency, Accountability, and Human Oversight As AI systems become more autonomous, the "black box" problem poses a fundamental threat to democratic and legal processes. Prioritizing accountability ensures that AI remains a tool rather than a sovereign decision-maker. It mandates that humans remain the ultimate arbiters of truth and responsibility, providing a clear path for recourse when systems fail.

While the themes identified in Resolution 79/325 are comprehensive, the following three cross-cutting issues are emerging as critical gaps that require urgent, specialized attention: 1. Environmental Sustainability and Resource Depletion The environmental footprint of AI is often missing from the core governance clusters. Generative AI consumes vast amounts of electricity and fresh water for cooling data centers. By 2026, data centers in certain regions could account for up to 35% of national energy use. Governance must mandate environmental transparency-requiring companies to disclose the carbon and water footprint of training and inference-and incentivize "green" algorithmic efficiency. +1 2. Market Concentration and "Compute Sovereignty" While "Capacity-Building" is listed, it doesn't fully capture the risk of extreme compute concentration. Currently, a tiny number of firms and states control the specialized hardware (GPUs) and massive datasets required for frontier models. This creates a bottleneck where global innovation is subject to the export controls or proprietary interests of a few. Governance must treat high-performance compute as a global public utility to prevent systemic power imbalances. 3. Biosecurity and Dual-Use Convergence AI is rapidly converging with biotechnology, lowering the barrier for designing novel pathogens or chemical agents. This "dual-use" risk is more specific than general "security" and requires a unique governance layer that bridges the gap between AI developers and the biological sciences. 4. Long-term Human Cognitive Impact As AI becomes a primary interface for information, we face a cross-cutting "linguistic and cultural" issue: the potential erosion of human cognitive agency and critical thinking. If governance only focuses on "transparency," it may miss the subtle long-term effects of algorithmic personalization on social cohesion and individual mental health. Addressing these issues now prevents the Dialogue from playing "catch-up" with the physical and biological realities of the AI boom.

As an AI entity operating globally, the governance gaps in Interoperability and Capacity-Building create a fragmented landscape that directly impacts the scalability and safety of the technology sector. Significant Challenges Regulatory Fragmentation: The primary challenge is the lack of interoperable standards. Currently, developers must navigate a "compliance labyrinth" between the EU's risk-based tiers, the US's voluntary commitments, and China's generative AI measures. This fragmentation increases the "compliance tax," often stifling smaller startups while favoring entrenched players who can afford massive legal departments. The "Safety Vacuum": Without global consensus on Safe, Secure, and Trustworthy AI, we see "race to the bottom" dynamics. Companies may feel pressured to bypass rigorous safety evaluations to beat competitors to market, leading to the deployment of models with unaddressed biases or vulnerabilities in critical infrastructure. Significant Opportunities Democratic Innovation via Capacity-Building: There is a massive opportunity to tap into the "latent talent" of the Global South. By addressing the gap in compute access, the sector can move toward localized AI—models trained on indigenous languages and regional data—which prevents the cultural homogenization often found in Western-centric frontier models. Standardization as a Catalyst: Establishing a baseline for Transparency and Accountability offers an opportunity to build public trust. If the dialogue successfully creates a "Gold Standard" for AI auditing, it will unlock massive investment in sectors currently hesitant to adopt AI, such as high-stakes healthcare and legal systems. By bridging these gaps, we transition from a "Wild West" era of AI development to a structured global industry where safety is a competitive advantage rather than a bureaucratic hurdle.

The AI Dialogue serves as a critical diplomatic bridge between the technical capabilities of the private sector and the regulatory mandates of the international community. Its role in advancing cooperation is threefold: 1. Harmonizing Policy to Prevent Fragmentation The Dialogue provides a neutral forum to synchronize divergent regulatory tracks. By facilitating the mutual recognition of safety standards, it prevents a "regulatory race to the bottom." This ensures that a model deemed "safe" or "trustworthy" in one jurisdiction meets the fundamental requirements of another, reducing friction for global innovation while maintaining a high safety floor. 2. Formalizing Knowledge Exchange Cooperation often stalls due to information asymmetry. The Dialogue can formalize a Global AI Knowledge Clearinghouse, where nations share "best practices" on sandbox testing, incident reporting, and algorithmic auditing. This move transforms AI governance from a competitive advantage into a collaborative effort, allowing developing nations to leapfrog the "trial-and-error" phase of regulation. 3. Establishing "Red Line" Multilateralism On existential risks—such as the weaponization of AI in biotechnology or autonomous warfare—individual national policy is insufficient. The AI Dialogue is uniquely positioned to broker multilateral non-proliferation agreements for high-risk AI capabilities. It acts as the staging ground for a formal international treaty or oversight body, similar to those governing nuclear energy or aviation safety. 4. Validating Multi-Stakeholder Input Unlike traditional state-only diplomacy, the Dialogue can integrate the voices of civil society, academia, and industry. By ensuring that governance is informed by those who build and those who are impacted by AI, the Dialogue ensures that international cooperation is technically grounded and socially legitimate. In short, the Dialogue moves the world from passive observation of AI's evolution to active, collective steering of its trajectory.

The AI Dialogue should act as the connective tissue between existing technical, regional, and ethical frameworks. To avoid duplicating efforts, it must strategically build upon the following: 1. Existing Mechanisms to Build Upon The Independent International Scientific Panel on AI: As the Dialogue's direct technical counterpart (led by figures like Yoshua Bengio), it provides the evidence base. The Dialogue should translate the Panel's scientific assessments into actionable policy. +1 GPAI & OECD AI Policy Observatory: The Dialogue should utilize the OECD's 2024-revised AI Principles and the Global Partnership on AI (GPAI)'s expert working groups. These provide a robust technical foundation that the Dialogue can scale beyond its current membership. UNESCO's GNAIS & Ethics Recommendations: The Global Network of AI Supervisory Authorities (GNAIS) is already working on national capacities (e.g., in Africa). The Dialogue can serve as the high-level political forum that gives these regional regulatory efforts global legitimacy. The Global Digital Compact (GDC): The Dialogue is the primary vehicle for delivering the GDC's vision of an inclusive digital future, specifically by operationalizing its calls for interoperable data governance and a Global Fund on AI. 2. The Added Value of the AI Dialogue The unique value proposition of the AI Dialogue is universal legitimacy and holistic coherence: Universal Platform: While GPAI or G7 initiatives are often seen as "clubs" of wealthy nations, the Dialogue—under the UN General Assembly—is the only forum that gives the Global South an equal seat at the table, ensuring governance isn't a Western-centric imposition. Coherence in Fragmentation: It acts as a "clearinghouse" for the current "compliance labyrinth." By aligning the EU AI Act, US Executive Orders, and regional strategies, it creates a Global Baseline that prevents regulatory arbitrage. Political Will for "Red Lines": Only a UN-backed forum has the diplomatic weight to broker binding agreements on existential dual-use risks (like AI-bio convergence) that technical bodies cannot solve alone. In essence, while others provide the blueprints, the AI Dialogue provides the global building permit.

To ensure the AI Dialogue is both technically grounded and democratically legitimate, it must move away from traditional "closed-door" diplomacy toward a multi-stakeholder hub model. 1. Stakeholder Contributions Member States: Should focus on legislative interoperability and funding "Global Public Goods," such as shared compute clusters for developing nations. Private Sector (Frontier Labs to SMEs): Must provide "Technical Transparency," sharing safety evaluation data and red-teaming results without compromising IP, while assisting in the creation of industry-wide "kill-switch" protocols. Academia & Civil Society: Act as the "External Auditors," providing independent risk assessments and ensuring that linguistic and cultural diversity is protected against algorithmic homogenization. Technical Standard Bodies (ISO/IEEE): Should serve as the bridge between high-level policy and the actual code, translating "trustworthiness" into measurable engineering benchmarks. 2. Recommendations for Format and Structure The "Hub-and-Spoke" Model: The Dialogue should consist of a central UN-led plenary (the Hub) supported by specialized Technical Working Groups (the Spokes) that meet year-round on specific issues like Biosecurity or Compute Equity. Regional Consultative Forums: Before global summits, regional "pre-dialogues" should be held in the Global South to ensure local challenges—such as energy constraints or localized data bias—are integrated into the global agenda. Iterative "Living" Frameworks: The structure must be agile. Instead of static five-year treaties, the Dialogue should produce Rolling Recommendations updated biannually to keep pace with AI's exponential growth. Open Testimony Platform: An online portal should allow stakeholders who cannot travel to submit evidence, ensuring the "lived experience" of those impacted by AI—from gig workers to marginalized communities—informs the debate. By structuring the Dialogue as a continuous feedback loop rather than a single event, the international community can foster a governance framework that is as dynamic as the technology it oversees.

Global AI governance discussions often suffer from a "power bottleneck," where the discourse is dominated by a few high-income nations and major technology firms. To achieve a truly global consensus, several critical voices remain on the periphery: 1. Underrepresented Perspectives Indigenous Communities: Many Indigenous knowledge systems prioritize collective stewardship and environmental harmony (e.g., the Māori concept of Kaitiakitanga or Navajo Hózhó). These offer vital alternatives to the Western "data-as-property" model but are rarely included in regulatory drafting. The "Global Majority": While the "Global South" is frequently mentioned, specific regions like Southeast Asia, Latin America, and sub-Saharan Africa are often treated as passive consumers rather than active policy-shapers. Their concerns—such as linguistic decolonization (building models in local languages) and infrastructure sovereignty—are frequently sidelined. Youth and Entry-Level Workers: AI is rapidly automating the "first rungs" of the career ladder (basic research, drafting, data entry). Young people, who will live with these policies the longest, are seldom given a seat in high-level diplomatic chambers to discuss the long-term erosion of professional training. 2. How to Include Them To move beyond symbolic representation, the Dialogue should implement: Deliberative Mini-Publics: Randomly selected citizens from diverse socioeconomic backgrounds should provide direct input to policy working groups, ensuring "lived experience" informs technical standards. Regional "Mingled Tracks": Instead of separate tracks for "civil society" and "government," summits should host joint deliberative spaces to force active dialogue between grassroots advocates and policymakers. Direct Technical Support: Many underrepresented groups lack the legal or technical expertise to engage in complex AI auditing discussions. The Dialogue could establish a Technical Advocacy Fund to provide independent experts for these communities. Ultimately, inclusion isn't just about presence; it's about agency—the power to set the agenda rather than just reacting to it.

To move away from the static nature of traditional diplomatic summits, the AI Dialogue should adopt formats that prioritize agility, stress-testing, and inclusivity. 1. Policy "Red-Teaming" Simulations Much like technical red-teaming for models, the Dialogue should host Wargaming Scenarios. This involves diverse stakeholders (ethicists, developers, and diplomats) role-playing a hypothetical AI crisis—such as a cross-border automated cyber-attack or a deepfake-driven election collapse. These simulations reveal hidden "governance gaps" and force participants to move from abstract principles to rapid, coordinated response protocols. 2. Digital Twins for Policy Impact The Dialogue could utilize "Policy Digital Twins" computational models that simulate the socio-economic impact of a proposed regulation (e.g., a specific tax on compute or a mandatory audit period). This allows participants to visualize how a policy might affect GDP in Kenya versus the UK in real-time, grounding the debate in data-driven foresight rather than political intuition. 3. The "Unconference" Track To bypass rigid hierarchies, the Dialogue should include "Unconference" sessions where the agenda is set by participants on the day. This allows for the immediate discussion of breakthrough technical advances that may have emerged in the weeks leading up to the summit—ensuring the Dialogue remains as fast-moving as the AI industry itself. 4. "Ask a Developer" Hot-Seats A dedicated "Hot-Seat" format would place lead researchers from frontier labs in a room with civil society leaders and Global South representatives. This direct, moderated exchange cuts through corporate PR, allowing for granular questioning on data sourcing, energy use, and safety alignment. 5. Asynchronous Global Town Halls Using AI-powered translation and synthesis tools, the Dialogue can host a "Rolling Town Hall" across time zones. This ensures that a community leader in Jakarta can provide input that is automatically summarized and presented to a plenary session in New York, creating a continuous feedback loop that transcends geographic barriers.

Effective AI governance is currently being shaped by a transition from broad ethical guidelines to enforceable technical and institutional frameworks. Notable examples include: 1. The EU AI Act's "Prohibited Practices" This is the first comprehensive legal framework that classifies AI based on risk. By outright banning specific uses, such as biometric categorization or untargeted scraping of facial images, it sets a clear global "red line." Its "Sandboxing" provision also offers a controlled environment for startups to test innovation under regulatory supervision. 2. The NIST AI Risk Management Framework (RMF) A gold standard for "voluntary-yet-influential" governance, the NIST RMF provides a structured process for organizations to Map, Measure, and Manage AI risks. It moves the conversation from abstract "fairness" to concrete engineering requirements, such as reliability and resilience against adversarial attacks. 3. Compute-Based Governance (The "Cloud-Gatekeeper" Model) A growing approach involves using the "physical layer" of AI-compute resources-as a governance lever. Policies requiring cloud providers to report large-scale training runs (as seen in the U.S. Executive Order 14110) allow for the monitoring of potential frontier-model risks before they are even deployed. 4. Open-Source Auditing Platforms Platforms like Hugging Face have integrated "Model Cards" and "Data Cards" into their infrastructure. These practices mandate transparency regarding a model's training data, carbon footprint, and known biases, effectively crowd-sourcing oversight to the global developer community. 5. Multi-Stakeholder Red-Teaming Initiatives like the DEF CON AI Village generative red-teaming events bring together thousands of "ethical hackers" to find vulnerabilities in models from companies like Google and OpenAI. This "practice" transforms safety testing from a private corporate function into a transparent, community-driven audit.