Lebanon IGF

Success for the first Global Dialogue on AI Governance will not be measured by the weight of its final document. It will be measured by whether the world leaves Geneva with three concrete outcomes. First, a shared governance lexicon. The EU, the US, China, and the Global South are currently speaking different languages when they say "AI governance." The first session must produce a common baseline of definitions and principles that does not privilege any single regulatory tradition. Without it, every future conversation starts from zero. Second, genuine representation of underrepresented voices. If the outcomes of July 2026 read as though they were written in Brussels or Washington with developing nations nodding along, the Dialogue will have failed regardless of how inclusive the process appeared. Real success means concerns about AI divides, local language exclusion, and capacity gaps are reflected as prominently as frontier AI risks. Third, an accountability structure with teeth. Not a working group to study a framework to recommend a committee. A concrete commitment with named responsibilities, measurable milestones, and a follow-up mechanism before the 2027 New York session. The world has watched too many historic digital governance moments produce elegant language that evaporated within eighteen months. If Geneva delivers these three outcomes, even partially, it will have established something genuinely new, that AI governance is a universal conversation, not a geopolitical chess match dressed in multilateral language. That would be worth the decade of effort it took to get here.

• Transparency, accountability, and human oversight
• Open-source software, open data and open AI models
• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI

These four priorities are interconnected and must be addressed together rather than in isolation. Safe, secure and trustworthy AI is the foundation without which nothing else holds. As a practitioner working across digital transformation and cybersecurity, I see daily how AI systems are being deployed in critical infrastructure, public services and enterprise environments without adequate security frameworks or accountability mechanisms in place. Trust cannot be assumed - it must be architected. Transparency, accountability and human oversight directly enable that trust. Governance without visibility is not governance. Organizations and governments need clear audit trails, explainability standards and meaningful human checkpoints - particularly in high-stakes domains like healthcare, justice and public administration. The social, economic, ethical, cultural, linguistic and technical implications of AI deserve equal urgency. AI is not a neutral technology. It amplifies existing inequalities and introduces new ones. Linguistic exclusion alone - where models perform poorly in Arabic, French African dialects, or minority languages - creates a two-tier world of AI access that will compound over decades if not addressed now. Open-source software, open data and open AI models are the great equalizer. They are the primary mechanism through which developing nations, independent researchers and civil society can participate in AI on their own terms rather than as consumers of closed systems built elsewhere. Openness is not just a technical preference - it is a governance imperative. Together these four priorities represent the difference between AI governance that is written about the world and governance that is built with it.

Three cross-cutting issues are conspicuously absent from the current thematic structure and deserve explicit recognition. AI and geopolitical fragmentation: The governance conversation assumes a level of international cooperation that the current geopolitical reality does not support. As AI becomes a strategic asset, nations are actively building incompatible AI ecosystems, not because of differing values, but because of competing interests. The Dialogue needs a dedicated mechanism to address how governance frameworks function in a fragmented world, not just an idealized cooperative one. Temporal governance: the speed asymmetry problem. AI capabilities are advancing on a timeline measured in months. International governance moves on a timeline measured in years. This asymmetry is not a logistical inconvenience, it is a structural vulnerability. The Dialogue must explore adaptive governance models that can evolve without requiring full multilateral renegotiation every time the technology makes a significant leap. AI and cognitive sovereignty: Recommendation algorithms, generative AI and synthetic media are reshaping how populations form beliefs, make decisions and understand reality. This is distinct from privacy or human rights as currently framed. It is about the structural influence of AI systems on collective cognition at a societal scale - something no existing governance framework adequately addresses. The line between personalization and manipulation is being crossed at scale, every day, with no international standard in sight. These issues cut across all four thematic clusters but belong to none of them exclusively. Leaving them unaddressed means the Dialogue will produce governance frameworks that are already behind the curve before the ink dries in Geneva.

Working across the Middle East and emerging markets in digital transformation, AI governance and cybersecurity, I observe governance gaps playing out in ways that are concrete and consequential rather than theoretical. The most significant challenge is the absence of contextually relevant AI governance frameworks. Most organizations in the region are navigating AI adoption using frameworks designed for the regulatory environments of the EU or the US. These frameworks do not account for the linguistic diversity, infrastructure constraints, data sovereignty sensitivities or local cultural contexts that define how AI actually behaves and impacts communities here. The result is governance by imitation rather than governance by design. In the cybersecurity domain specifically, AI is simultaneously the most powerful defensive tool available and the fastest-growing attack surface. Governance gaps around secure AI development practices, adversarial AI and autonomous threat response are leaving critical infrastructure exposed. Regional governments are making significant AI investments without commensurate investment in the governance structures that make those deployments safe. On the opportunity side, the Middle East is in a unique position. Several governments in the region have made AI national strategy a top priority, which means there is political will, investment capacity and a genuine appetite for governance frameworks that work. The region could become a meaningful testbed for governance models that bridge the gap between the Global North's regulatory sophistication and the Global South's development priorities. The opportunity the Dialogue presents is to ensure that regions like mine are not handed a finished governance product but are genuine co-architects of it. The challenges we face are not edge cases. They represent the reality of the majority of the world's population navigating AI without adequate governance infrastructure. That perspective belongs at the center of this conversation, not at its margins.

The AI Dialogue occupies a position no other forum currently holds, a universal table where every nation has standing, not just the technologically dominant ones. That position is either its greatest asset or its greatest missed opportunity, depending on how it is used. The most valuable role it can play is serving as the world's shared early warning system. Not just a space to negotiate frameworks after problems emerge, but a live mechanism where governments, practitioners and civil society surface emerging risks before they become crises. No bilateral agreement or regional bloc can do that at the scale the moment requires. The second role is legitimacy transfer. When governance norms are built inside the UN system with genuine multilateral input, they carry a different weight than standards written by industry consortiums or powerful single jurisdictions. The Dialogue can give global AI governance the democratic legitimacy it currently lacks. The third and most underutilized role is capacity bridging. Not through aid logic but through genuine knowledge exchange — connecting nations that are ahead on implementation with those that are building foundational infrastructure, so the governance gap does not permanently mirror the development gap. Done right, the Dialogue does not just coordinate. It equalizes.

Several existing initiatives have laid important groundwork that the AI Dialogue should actively build upon rather than duplicate. The OECD AI Principles and the UNESCO Recommendation on the Ethics of AI represent the most widely adopted normative frameworks to date. The Dialogue should use these as a shared baseline while explicitly extending them to cover governance realities that neither body was designed to address — particularly those of developing nations and emerging markets. The ITU's AI for Good platform and the Global Partnership on AI have built valuable multi-stakeholder communities and technical knowledge bases. The Dialogue's added value here is not content but authority, translating community-driven insights into intergovernmental commitments that actually bind. The EU AI Act, while a regional instrument, is already functioning as a de facto global standard through market influence. The Dialogue can play a critical counterbalancing role by ensuring that one jurisdiction's regulatory choices do not become the world's governance architecture by default. Critically, the Dialogue must also connect with regional governance bodies such as the Arab League, the African Union and ASEAN. These institutions carry cultural legitimacy and contextual understanding that global frameworks cannot replicate. Partnering with them ensures that AI governance is not just translated into local languages but genuinely rooted in local realities, values and priorities. The added value the Dialogue brings to all of these is irreplaceable, universality, legitimacy and a seat at the table for the nations and communities that have been recipients of AI governance rather than authors of it. That shift from recipient to author is the Dialogue's most important contribution. And one more important note, we should not leave the AI players openai, google, Xai, anthropic lead this conversation since the reality of things they are doing it by force.

The AI Dialogue will only be as strong as the diversity of voices that genuinely shape it, not merely observe it. Governments must move beyond endorsing principles toward committing to concrete policy actions with accountability mechanisms between sessions. The private sector must participate with transparency obligations, not as lobbyists but as technical contributors required to disclose known risks and governance gaps in their own systems. Civil society and academia should be structurally embedded in thematic working groups from the start, not invited as observers after positions are already formed. Practitioners working at the intersection of AI and real-world sectors —, healthcare, education, public administration — hold knowledge that neither governments nor tech companies fully capture. Dedicated practitioner roundtables feeding into each thematic cluster would address this gap directly. On format, the Dialogue should replace plenary declarations with structured working sessions that have clear deliverables, named accountability and published progress reports between Geneva and New York. Each thematic cluster should produce a living document updated continuously rather than a static outcome text negotiated at the last minute. Inclusion is not about how many stakeholders are in the room. It is about how much of what they say actually changes the outcome.

Asia and the Arab world together represent over half the world's population and some of the most dynamic AI deployment environments on the planet, yet their internal diversity is almost entirely flattened in global AI governance discussions. Both regions are treated as monoliths when they are anything but. Southeast Asian nations, many of them rapidly digitizing with limited regulatory infrastructure, are navigating AI adoption without governance frameworks that reflect their development stage, legal traditions or cultural contexts. Countries like Myanmar, Cambodia and Laos have virtually no presence in technical governance forums despite being significantly exposed to AI-driven financial systems, content moderation and automated public services affecting millions daily. South Asian communities, representing nearly two billion people across diverse languages and economic realities, are underrepresented both in AI training data and in governance deliberations, producing systems that perform poorly for them and frameworks that do not prioritize their protection. Arab nations bring a distinct and urgent perspective. The Arabic language remains one of the most underserved in large language models despite representing 400 million native speakers. Across the Arab world, governments are making bold AI investments while civil society organizations working on digital rights and algorithmic accountability lack meaningful pathways into formal governance processes. The region's unique position bridging Global North ambitions and Global South realities makes its voice essential, not optional. Inclusion requires funded participation, regional consultation hubs in cities feeding directly into Geneva and New York, and genuine co-authorship of thematic outputs. These regions are not complications for global AI governance. They are its most important test cases.

The traditional UN format of prepared statements, plenary sessions and negotiated outcome texts is structurally incapable of producing the dynamic engagement that AI governance actually requires. Innovation in format is not a nice-to-have it is a prerequisite for relevance. Live adversarial panels should replace one-sided presentations. Rather than sequential statements, structured debates between genuinely opposing perspectives a frontier AI developer and a digital rights advocate, a regulator and a civil society researcher would surface real tensions that polished position papers deliberately obscure. AI governance simulations modeled on crisis exercises would be transformative. Participants assigned roles across a simulated AI incident a cross-border algorithmic harm, an autonomous system failure in critical infrastructure, a deepfake-driven political crisis would reveal governance gaps faster than any working group report. What cannot be handled in simulation cannot be handled in reality. Citizen juries drawn from diverse national contexts, presented with real governance dilemmas and asked to deliberate and decide, would inject democratic legitimacy into a process that currently derives its authority primarily from state representation. Their findings presented directly to member states would change the dynamic of formal sessions meaningfully. Asynchronous digital participation tracks with genuine influence over thematic outputs would extend the Dialogue beyond those who can afford to be in Geneva. Contributions should feed into working documents in real time, not disappear into a consultation archive nobody reads. Finally, a Red Team track dedicated groups tasked with finding the weaknesses, loopholes and unintended consequences in every proposed governance framework before it is adopted — would produce more resilient outcomes than consensus-driven drafting processes that reward vagueness over precision. The Dialogue should be designed for the complexity of what it is governing, not for the comfort of those governing it. Nb soon we will be having robotics to also governance under AI Brains.

Based on experience working across digital transformation, AI governance and cybersecurity in the Middle East and emerging markets, the following represent proven approaches worth scaling globally: Regulatory Sandboxes with Mandatory Reporting: The UAE and Singapore have implemented AI regulatory sandboxes allowing controlled deployment with real-time monitoring. The critical addition is mandatory public reporting of outcomes, including failures, creating institutional learning rather than just controlled experimentation. National AI Strategies Anchored in Governance Frameworks Saudi Arabia's SDAIA and the UAE's AI Office demonstrate that dedicated national AI authorities with cross-ministerial mandates produce more coherent governance than distributing responsibility across fragmented ministries. Algorithmic Impact Assessments Canada's Directive on Automated Decision-Making requires algorithmic impact assessments before deploying AI in public services. This pre-deployment accountability model should become a global baseline, not a regional exception. UNESCO's Ethics of AI Recommendation The first global normative framework on AI ethics adopted by 193 member states. Its implementation toolkit offers a practical bridge between principles and policy that many nations are already using as a starting point. Open Government AI Registers New Zealand and the UK maintain public registers of AI tools used in government decision-making. Transparency of this kind builds public trust and creates accountability pressure without requiring heavy regulatory infrastructure. Civil Society Algorithmic Auditing Independent organizations like AlgorithmWatch and AI Now Institute demonstrate that civil society-led auditing of deployed AI systems surfaces harms that self-regulation consistently misses. Formalizing and funding this role globally would strengthen governance without expanding bureaucracy. Certification Track with the UN university for experts/professional companies to be able to audit ITU AI for Good as a Knowledge Exchange Platform Already connecting practitioners across geographies, it needs a direct pipeline into formal governance outputs rather than remaining a parallel conversation. Having all of these under one dedicated website for better dissemination not going to multi agency hunting information and compliance.