AI Safety UAE

A successful first Dialogue should lead to clear mechanisms, not another set of principles. The outcomes should include: 1. Agreement to establish a unified AI incident reporting framework that Member States can participate in, with shared definitions and processes for cross-border response. 2. A practical roadmap for embedding AI safety and quality standards, such as ISO 42001 and 42005, into market access and procurement policies. 3. A framework for AI literacy, quality, security, and safety, adapted to regional and local contexts. 4. Integration of gender-based and psychological harm within formal definitions of AI risk. There should also be clarity on institutional ownership: who coordinates at the global level, who convenes response networks, and how Member States can plug in. A success indicator would be that, by the next session, participants can report concrete implementation progress and not just continued consultations.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

AI Safety UAE focuses on connecting technical safety practice to governance systems that can be enforced. "Safe, secure and trustworthy AI" is our core mandate. "Interoperability of governance approaches" is essential because most countries and companies already operate under overlapping regimes, and fragmentation creates risk. We also emphasize "Transparency, accountability, and human oversight" because effective safety requires traceability of decisions, auditability of models, and the ability to intervene when things go wrong. Finally, "Protection and promotion of human rights" ensures that governance outcomes are people-centered, not only technology-centered. Together, these themes create a policy space that allows both governments and civil society to co-develop the practical infrastructure for AI safety: standards, benchmarks, coordination mechanisms, and shared tools for incident response.

Yes. The emergence of the "attachment economy" and related psychological harms is not yet captured in the thematic list. AI systems increasingly monetize emotional connection, not just attention. This creates new forms of dependency and identity manipulation that blur personal and commercial boundaries. These harms do not always stem from malicious intent but from incentive structures that reward engagement, emotional intensity, and stickiness. Addressing them requires governance tools that go beyond content rules or transparency. We need structural responses such as affect audits, emotional-dependency metrics in safety evaluations, and public accountability for systems designed to shape user identity. This is a cultural, ethical, and technical challenge, and it intersects with human rights and safety. Recognizing psychological harm as a formal category of AI harm would bring needed clarity and allow regulators, developers, and evaluators to integrate emotional impact into risk assessments from the start. You can learn more here: https://aiphrc.org

The UAE is already experiencing the governance gaps this consultation aims to close. With 89 percent of our population from more than 200 nationalities, the country is a live test environment for multilingual, multicultural, and contextually robust AI governance. "Make it in the Emirates" reflects a reality: if an AI system works safely and fairly here, it can work anywhere. The most urgent gap is the lack of a unified AI incident reporting framework. Governments still cannot share or compare AI‑related failures in a consistent way, limiting preparedness and collective learning. A second challenge is the absence of a common baseline for AI safety evaluation. Frontier AI developers apply different frameworks and definitions of "dangerous" or "high‑risk" AI, which prevents governments from making fair, evidence‑based procurement and oversight decisions. AI and digital sovereignty have also become strategic vulnerabilities. As several frontier AI providers deepen partnerships with national defense institutions, including those of major powers, countries that rely on their infrastructure face exposure of sensitive data and operational dependencies beyond their control. For governments whose services run on these platforms, risk management can no longer rely on trust alone. Transparency and enforceable safeguards are mandatory. Psychological and emotional harms should be recognized as part of AI safety. The attachment economy (where platforms monetize emotion and dependency) requires the same governance attention as physical or data harms. For 2026, achievable steps include linking ISO 42001 compliance to market access, establishing a UN‑led cross‑border AI incident coordination mechanism, and adding individual and societal well‑being metrics to AI safety evaluations.

The AI Dialogue should prioritize implementation over new principles. In 2026, it can deliver three concrete mechanisms for governments to manage shared AI risks using consistent methods. First, establish a unified AI Incident Reporting and Response Framework. Build directly on MIT AI Risk Initiative's National Security Incident Framework, which scores incidents like "Chinese State-Linked Operator Uses Claude Code for Autonomous Cyber Espionage" (Impact Score: 4) across imminence, novelty, and autonomy. Create a multilateral reporting hub where national regulators submit and access structured incident data in real time. Second, harmonize AI safety benchmarks for apples-to-apples procurement comparisons. Frontier AI labs use incompatible safety frameworks. The Dialogue can mandate a single risk taxonomy (drawing from MIT's causal and domain taxonomies) so governments evaluate models against shared thresholds for national security, psychological harm, and sovereignty risks. Example: https://github.com/swalehaparvin/-Frontier-AI-Risk-Threshold-Analyzer Third, require transparency on defense linkages for public-sector AI providers. When UAE government services run on Microsoft/OpenAI infrastructure, clear disclosure of data flows to US Department of Defense contracts becomes non-negotiable for risk management. Finally, formalize psychological harms (attachment economy dependency) within incident taxonomies, with measurable metrics for emotional manipulation. These deliverables (reporting templates, benchmark matrix, disclosure rules) are achievable by end-2026 through UN coordination of existing tools. The Dialogue becomes the neutral convener turning fragmented efforts into operational governance.

Build on these proven mechanisms: - MIT AI Risk Initiative's National Security Incident Framework: Classifies 1,300+ incidents with NatSec impact scores (imminence/novelty/autonomy). Use as the backbone for cross-border reporting. See https://airisk.mit.edu/ai-incident-tracker/natsec-impact-framework https://airisk.mit.edu/ai-incident-tracker/natsec-incident-view - OECD AI Incidents Monitor + INTERPOL cyber tools: Integrate for real-time incident exchange. - ISO/IEC 42001/42005: Link to market access conditions. - UNICRI & AI Safety Asia: Leverage for capacity-building templates. - ASEAN AI Governance Guidelines: Adapt for regional sovereignty safeguards. Dialogue's added value: 1. Single interoperability layer: Map MIT NatSec scores, OECD incident data, and ISO audits into one dashboard for governments. 2. Procurement toolkit: Standardized risk matrix so UAE can compare OpenAI vs. others on defense exposure and psychological harm metrics. 3. Annual joint exercises: Simulate cross-border incidents using MIT's real cases (e.g., Claude cyber espionage) to test response protocols. 4. Sovereignty safeguards: Mandatory disclosure templates for AI providers with military contracts. By connecting these assets under UN auspices, the Dialogue delivers working infrastructure by 2026, not whitepapers. AI Safety UAE offers to pilot-test reporting in UAE's multilingual and multicultural contexts.

Governments: Lead by mandating ISO 42001 compliance for public AI procurement and piloting MIT NatSec incident reporting in 2-3 countries by Q4 2026. AI Labs/Providers: Submit safety frameworks to a Dialogue-led harmonization process. Disclose defense contracts and data flows for public-sector clients like UAE government services. Civil Society/Research (e.g., AI Safety UAE): Test incident reporting in multilingual contexts; validate harmonized benchmarks against real UAE deployments. Structure for 2026: - Quarterly virtual working groups (2hr) on incident reporting, benchmarks, sovereignty. - Biannual in-person sprints (2 days): Prototype tools, run joint exercises. - Single online dashboard: MIT scores + ISO audits + incident submissions. Three impactful contributions: - Governments commit procurement pilots. - Labs provide framework mappings by June 2026. - UN convenes first cross-border incident simulation by December 2026.

Underrepresented: Multilingual testbeds like UAE (89% expatriate, 200+ nationalities), governments dependent on US/China/Superpowers AI infrastructure. Women experiencing digital gender based violence, and people experiencing attachment economy harms. Inclusion mechanisms: - Allocate 30% Dialogue slots to Global Majority + small states. - Require AI providers to fund travel/support for 10 civil society reps from underrepresented regions per session. - Mandate low resource language translation for all technical working groups. Three actionable steps: - AI Safety UAE leads UAE/Global South pilot for incident reporting (Q2 2026). - Providers sponsor 20 delegates from dependency-vulnerable states. - UN creates "stress-test nation" track: For example UAE, Indonesia, Nigeria validate global tools locally.

Three highest-impact formats for 2026: 1. Live incident simulations: Use MIT case #1263 (Claude cyber espionage, score 4) for real-time cross-border response drills. Labs role-play, governments decide. 2. Procurement showdowns: UAE + 2 others score OpenAI/Microsoft vs. alternatives live using harmonized benchmarks. Quarterly, public broadcast. 3. Hackathon sprints: 48hr events building interoperable tools (reporting dashboards, sovereignty checklists). Civil society wins implementation funding. Why these work: Hands-on, measurable outputs by year-end. No workshops. Only prototypes that deploy to production. UAE ready to host first sprint.

Three most impactful examples: 1. CAIDP Index (AI Policy Clinic): Benchmarks 90+ countries across OECD/UNESCO implementations, public participation, independent oversight, algorithmic transparency rights. Scores nations Y/N/P on metrics like "Has the country established an independent AI oversight agency?" Enables governments to track peer progress and identify gaps. Imperfect but operational: https://www.caidp.org/reports/caidp-index-2025/ 2. Procurement mechanisms over regulation: In absence of binding laws, mandate ISO 42001 compliance plus multilingual system cards plus rigorous safety evals for government contracts. Low resource language jailbreaks expose bias. Require contextually robust testing (200+ nationalities as validation ground). Deployable Q2 2026 via existing ISO certification bodies. 3. MIT AI Risk Repository NatSec Framework: Scores 1300+ incidents (e.g., Claude used for autonomous cyber espionage, Impact 4/5) by imminence/novelty/autonomy. Builds on OECD/Hiroshima incident efforts with structured taxonomy: https://airisk.mit.edu/ai-incident-tracker/natsec-incident-view