SAIF CHECK

. Unified Risk Taxonomy While different nations have varying cultural priorities, a shared technical understanding of "catastrophic risk" is essential. Success means moving from vague concerns to a standardized classification of threats, such as biological misuse, autonomous escalation, and systemic bias. This creates a common language for global safety audits. 2. The "Interoperability" Commitment We don't need a single global law—that's a diplomatic impossibility. Instead, success looks like a commitment to regulatory interoperability. This means ensuring that a safety certificate issued in the EU or US is recognized (or at least compatible) with frameworks in the Global South and Asia, preventing a "splinternet" where AI safety standards become trade barriers. 3. Inclusive Infrastructure for the Global South A dialogue that only includes the "AI Superpowers" is a failure. Success requires a concrete mechanism for resource sharing—whether through compute credits, open-source safety tools, or datasets that represent non-Western languages and cultures.

• Safe, secure and trustworthy AI
• Transparency, accountability, and human oversight
• Interoperability of governance approaches
• Social, economic, ethical, cultural, linguistic and technical implications of AI

1. Safe, Secure, and Trustworthy AI This is the foundational "floor" of AI development. Safety focuses on preventing unintended harm (e.g., technical glitches), while Security defends against adversarial attacks (e.g., jailbreaking or data poisoning). Together, they build Trust, ensuring that users and states can rely on AI systems without fear of catastrophic failure or malicious subversion. 2. Socio-Economic and Technical Implications This pillar addresses the "human footprint" of AI. It explores how automation impacts global labor markets and ensures that AI preserves linguistic and cultural diversity rather than erasing it. Technically, it focuses on bridging the digital divide so that the benefits of AI are not concentrated in a few wealthy nations. 3. Interoperability of Governance Approaches Because AI code ignores national borders, governance must be "interoperable." This doesn't mean every country has the same laws, but rather that their legal frameworks "talk" to one another. It prevents a regulatory race to the bottom and ensures that a safety standard in one region is technically compatible with another. 4. Transparency, Accountability, and Human Oversight This ensures AI remains a tool, not an autonomous agent. Transparency: Disclosing how models are trained. Accountability: Establishing who is liable when an AI makes a mistake. Human Oversight: Maintaining "human-in-the-loop" protocols to override AI decisions in high-stakes environments like healthcare or defense.

While Resolution 79/325 provides a robust starting point, three critical "blind spots" are emerging in 2026 that require urgent integration into the Global Dialogue:1. Environmental Sustainability and Resource GovernanceThe "Technical Implications" theme often overlooks the sheer physical toll of AI. Training a single large model can consume as much electricity as hundreds of households yearly.The Issue: We face a "rebound effect" where AI-driven efficiencies in some sectors are canceled out by the massive carbon footprint and water consumption of data centers.Governance Gap: There is currently no global standard for green AI reporting or equitable access to low-carbon compute.2. The AI-Biology Nexus (Biosecurity)AI has transitioned biology from an experimental science to a predictive one. While this accelerates drug discovery, it also lowers the barrier for designing novel pathogens.The Issue: Current governance is "siloed"-biosecurity experts focus on labs, while AI experts focus on data.Governance Gap: We lack a cross-cutting framework to regulate dual-use biological models and the automated "biofoundries" that can print synthetic DNA.3. Sovereign AI and Compute DiplomacyAs AI becomes a core component of national power, a new "Compute Divide" is forming.The Issue: Nations are increasingly pursuing Sovereign AI-building domestic infrastructure to avoid dependency on foreign "Big Tech."Governance Gap: Without a diplomatic framework for "Compute Sharing," smaller nations may be forced into digital vassalage, relying on models that do not reflect their local laws or values.

In the Middle East, and specifically within the GCC region, the governance gaps in interoperability and transparency create a high-stakes environment where rapid technological advancement often outpaces regulatory "catch-up." Significant Challenges: The "Pacing Problem" Regulatory Fragmentation: While Saudi Arabia (SDAIA) and the UAE have robust national strategies, the lack of a unified regional framework means a "governance gap" persists. This forces tech entities to navigate a patchwork of data residency laws, making cross-border AI deployments—such as regional healthcare diagnostics—technically and legally complex. The Transparency Paradox: The rise of Agentic AI (autonomous systems) in 2026 has outstripped traditional "explainability" standards. In sectors like finance and smart-city planning (e.g., NEOM), the challenge is ensuring these black-box systems remain accountable to human oversight without stifling their efficiency. Environmental Toll: The rapid scaling of data centers—the region's "new oil"—faces a gap in global green AI standards. Without these, the massive energy and water cooling requirements for AI infrastructure threaten national sustainability goals. Significant Opportunities: Leading by Infrastructure Sovereign AI as a Safeguard: By investing in localized Large Language Models (LLMs) that reflect Arabic linguistic and cultural nuances, the region is turning a governance challenge into a competitive advantage. This reduces dependency on foreign models that may not align with local ethical or legal values. Experimental Regimes: Saudi Arabia's designation of 2026 as the Year of AI has accelerated "regulatory sandboxes." These allow for real-world testing of AI in public services—like AI-enabled participatory urban planning—under controlled oversight, providing a global model for "agile governance."

1. A Universal "Clearinghouse" for Best PracticesThe Dialogue provides a centralized platform for the 193 UN Member States to move beyond siloed policies. By integrating findings from the Independent International Scientific Panel on AI (the "IPCC for AI"), it translates complex technical assessments into actionable policy blueprints. This ensures that a breakthrough in "algorithmic auditing" in one nation can be adapted and adopted globally, preventing redundant efforts.2. Bridging the "AI Divide"A core function of the Dialogue is transitioning from safety-centric debates to diffusion-centric action. It facilitates cooperation on:Capacity Building: Coordinating "Compute Diplomacy" and high-performance computing access for the Global South.Standardization: Aligning technical standards so that AI developed in emerging markets is interoperable with global infrastructure.3. De-escalating "AI Nationalism"In an era of "sovereign AI," the Dialogue serves as a neutral ground to prevent a digital "Cold War." By focusing on non-military AI applications, it allows competing superpowers to find common ground on shared threats—such as deepfakes, biosecurity risks, and the climate impact of massive data centers—that no single nation can solve alone.

To succeed, it must build upon several key existing pillars while providing a unique political and inclusive mandate.1. Existing Initiatives to Build UponThe AI Safety Summit Series (Bletchley, Seoul, Paris): The Dialogue should integrate the technical findings of the International Network of AI Safety Institutes (AISIs). While these summits focus on frontier risks, the Dialogue can translate their technical benchmarks into global policy.OECD AI Policy Observatory & GPAI: These bodies provide the gold standard for evidence-based policy and multi-stakeholder research. The Dialogue can use the OECD AI Principles as a baseline for interoperability.UNESCO Recommendation on the Ethics of AI: With 193 member states already committed to this ethical framework, the Dialogue should use it as the "moral compass" for addressing cultural and linguistic diversity.G7 Hiroshima AI Process: The Dialogue can scale the G7's "International Code of Conduct" for developers from a small club of nations to a universal standard.2. The "Added Value" of the AI DialogueThe unique value proposition of the UN-led Dialogue is Universal Legitimacy.FeatureExisting Clubs (G7/OECD)UN Global DialogueInclusivityLimited to high-income nations.All 193 Member States (Global South parity).MandateAdvisory or voluntary.Tied to the Global Digital Compact.ScopeOften safety or innovation-centric.Holistic: Human rights, development, and peace.By connecting these "islands of governance," the Dialogue prevents a fragmented regulatory landscape. It provides the political platform to turn the scientific consensus of the Independent Scientific Panel on AI into a binding global roadmap for equitable access to compute and safe deployment.

The AI Dialogue succeeds only if it functions as a multistakeholder ecosystem rather than a closed-door diplomatic summit. Based on Resolution 79/325 and the Global Digital Compact, here is how stakeholders can drive the process: 1. Stakeholder Contributions Governments: Establish "National AI Focal Points" to coordinate across ministries and contribute to the UN Digital Cooperation Portal. Private Sector: Move beyond lobbying to provide in-kind technical resources, such as compute credits for the Global South, and share standardized safety audit data. Civil Society & Academia: Act as the "integrity layer" by submitting evidence-based assessments to the Scientific Panel on human rights impacts and linguistic biases. Technical Community: Develop open-source governance tools and interoperability standards that can be adopted by developing nations. 2. Recommended Format & Structure To keep pace with AI's velocity, the Dialogue should adopt a "Hub-and-Spoke" model: The Annual Plenary (The Hub): A high-level summit (e.g., alongside the AI for Good Global Summit in Geneva) to set political priorities and review the Scientific Panel's annual report. Thematic Working Groups (The Spokes): Year-round, agile tracks focused on specific gaps: Capacity Building, Technical Interoperability, and Risk Taxonomy. Hybrid Participation: Utilize a permanent digital platform for continuous "asynchronous" consultation, ensuring that stakeholders who cannot travel to New York or Geneva—particularly from LDCs—have an equal voice.

While the Global Dialogue aims for inclusivity, several key perspectives remain on the periphery of the 2026 AI governance landscape: 1. Underrepresented Perspectives Indigenous Communities: Often treated as "data sources" rather than sovereign partners, Indigenous voices are critical for addressing data colonization. Their perspectives on communal (vs. individual) privacy and the preservation of low-resource languages are vital to prevent AI from becoming a tool of cultural erasure. Small and Medium Enterprises (SMEs) in the Global South: While "Big Tech" and "Sovereign AI" dominate, the local developers building context-specific solutions (e.g., AI for small-scale regenerative farming) are often excluded from high-level safety summits due to financial and visa barriers. Youth and Future Generations: As the primary "inheritors" of AI's long-term risks, youth are underrepresented in technical auditing and policy oversight, despite being the most active users of generative tools. 2. Pathways to Inclusion To move from "consultation" to "co-creation," the Dialogue should implement: Virtual Participation as a Right: Standardizing hybrid formats to eliminate the "Geneva/New York" travel barrier that disproportionately silences LDCs (Least Developed Countries). Indigenous Data Sovereignty Protocols: Integrating the UN Declaration on the Rights of Indigenous Peoples into AI technical standards to ensure "free, prior, and informed consent" for cultural data usage. The "Youth Auditor" Program: Creating formal seats for young scientists and ethicists within the Independent International Scientific Panel on AI.

To move beyond traditional "speech-based" plenaries, the AI Dialogue must adopt formats that prioritize asynchronous inclusion and technical deliberation. In my opinion, three innovative formats would most effectively foster dynamic engagement: 1. The "Fishbowl" Deliberation Track Unlike a standard panel, a "fishbowl" features a small inner circle of experts (e.g., a mix of frontier lab researchers, Global South ethicists, and youth advocates) engaging in a live, unscripted debate, surrounded by an outer circle of observers. This format breaks the "corporate hat" barrier, allowing for a genuine exchange of ideas rather than prepared talking points. Added Value: It exposes the friction between technical capability and regulatory safety in real-time. 2. AI-Assisted "Symmetry" Consultations Using specialized AI deliberation tools (like those seen in recent "human-AI ensemble" experiments), the Dialogue can synthesize thousands of disparate stakeholder submissions into a thematic "heat map." How it works: Participants use a digital platform to "map" their disagreements. The AI identifies common ground and "nuance gaps" across different languages and cultures, presenting these clusters to policymakers for targeted resolution. 3. Regulatory Sandboxes and "Live Prototyping" Instead of debating abstract laws, the Dialogue should host "Governance Hackathons." The Format: Teams of regulators and developers are given a hypothetical AI system (e.g., an autonomous health diagnostic tool) and tasked with applying different regional governance approaches (e.g., the EU AI Act vs. the African Union AI Strategy) to it. Outcome: This identifies where "interoperability" actually breaks down at the code level, providing a technical baseline for diplomatic negotiations.

The following are concrete examples and approaches currently shaping the 2026 landscape: 1. Technical Safety: The International Network of AISIs The establishment of AI Safety Institutes (AISIs)-pioneered by the UK and followed by the US, Japan, Australia, and notably India in early 2026-provides a technical blueprint for governance. The Practice: These institutes conduct pre-deployment testing on "frontier" models for "dual-use" risks (e.g., cyber-attacks or bioweapon development). The Solution: By sharing "evals" (evaluation benchmarks), they create a global technical standard for what constitutes a "safe" model before it hits the market. 2. Regional Sovereignty: The African Union Continental AI Strategy Adopted in 2024 and reaching its first implementation milestone in 2026, the AU's strategy focuses on digital agency. The Approach: It prioritizes the creation of "African-centric" datasets to combat linguistic bias and promotes Sovereign AI infrastructure. The Solution: This prevents "data colonization" by ensuring that AI governance isn't just imported from the Global North but is rooted in local values like Ubuntu (collective responsibility). 3. Agile Regulation: Saudi Arabia's "Year of AI" (2026) SDAIA (Saudi Data and AI Authority) has implemented a Life-cycle Ethical Framework. The Policy: Instead of a static law, it uses a dynamic "Adoption Framework" that provides public and private sectors with practical "sandboxes" to test AI in government services. The Solution: This addresses the "pacing problem"-allowing innovation in smart cities (like NEOM) to proceed while maintaining strict human-in-the-loop oversight. 4. Transparency Platforms: The OECD.AI Policy Observatory This platform acts as a global "dashboard" for AI policy. The Platform: It live-tracks over 1,000 national AI policies, providing a data-driven basis for the Interoperability discussed earlier.