Ministry of interior - QATAR

Success for the first Global Dialogue on AI Governance must be defined by three concrete outcomes. 1. Establishing a Shared Baseline We first need a common understanding of what AI governance actually looks like in practice. Since there is no single international rulebook yet, a vital first step is creating shared definitions and principles. This doesn't necessarily mean drafting a binding treaty right away; rather, it's about a transparent, honest mapping of where national policies align and where they pull apart. 2. Radical Inclusivity in Agenda-Setting For this Dialogue to be meaningful, participation must be genuinely inclusive. Developing nations, fragile states, and civil society shouldn't just be "in the room"- they need to be the ones shaping the conclusions. If the final outcomes only mirror the interests of tech giants and advanced economies, the platform will have failed its mandate to be truly universal. 3. A Credible Path Forward A one-off meeting isn't governance; it's just a meeting. Success requires a clear, actionable roadmap that extends far beyond Geneva. We need a defined timeline and an accountability structure that ensures continuity. Without a commitment to what comes next, we risk hosting a prestigious conversation that lacks the teeth to create lasting change.

• AI capacity-building
• Protection and promotion of human rights
• Transparency, accountability, and human oversight
• Social, economic, ethical, cultural, linguistic and technical implications of AI

Key Priorities for AI Governance 1. AI Capacity-Building This is the most urgent gap globally. Most of the world - particularly in Africa, the Middle East, and post-conflict regions- lacks the institutional, technical, and regulatory capacity to engage meaningfully with AI governance. Without addressing this first, all other thematic discussions risk being conversations among the already capable. 2. Protection and Promotion of Human Rights AI systems are already making consequential decisions about people's access to services, justice, and opportunity. In contexts with weak institutions and historical marginalization, the human rights risks of ungoverned AI are immediate and severe - not theoretical. 3. Transparency, Accountability, and Human Oversight These are the functional prerequisites of any trustworthy AI system. Without them, no governance framework - however well designed - can be enforced or trusted by the public. This is especially critical in contexts where citizens have historical reasons to distrust state institutions. 4. Social, Economic, Ethical, Cultural, Linguistic and Technical Implications of AI AI governance cannot be reduced to a technical or legal exercise. Its implications cut across every dimension of society. A Dialogue that does not seriously engage with cultural, linguistic, and ethical diversity risks producing governance frameworks that are universally adopted but locally irrelevant.

Yes. Three significant issues remain underrepresented in the current thematic structure. First, AI governance in fragile, conflict-affected, and post-conflict states. The listed themes implicitly assume functioning institutions, stable regulatory environments, and a baseline of state-citizen trust. This excludes a substantial portion of the world's population living in contexts where these conditions do not exist. AI is already being deployed in humanitarian operations, conflict zones, and post-conflict reconstruction efforts - yet no dedicated governance conversation exists for these settings. Fragility is not a temporary exception.it is a permanent feature of the global landscape that AI governance must directly address. Second, the militarization and weaponization of AI. The use of AI in surveillance, autonomous weapons systems, and information warfare is accelerating rapidly. Yet this dimension is largely absent from the current thematic clusters, which focus primarily on civilian and economic applications. Third, environmental sustainability as an AI governance concern. The computational infrastructure required to develop and operate large AI systems carries significant energy and resource costs. As developing nations are encouraged to adopt AI, the environmental implications of doing so - particularly for countries already vulnerable to climate impact - deserve explicit attention in any governance framework that claims to be universal and future-oriented.

Post-conflict states represent one of the most significant blind spots in the current global AI governance landscape - and the consequences of this gap are already visible. On capacity-building, the challenge is not simply a lack of technology. It is the absence of the institutional foundations that make technology governable - regulatory bodies, data standards, trained public officials, and accountability mechanisms. Without these, AI systems introduced through humanitarian or development channels operate in a governance vacuum, with no framework to ensure they serve communities equitably or can be held accountable when they cause harm. On human rights, post-conflict societies carry deep histories of exclusion and state-sanctioned discrimination. AI systems trained on pre-conflict data risk encoding and automating those historical patterns, systematically disadvantaged the populations most harmed by conflict displaced communities, ethnic minorities, and those living outside formal institutional reach. The human rights implications here are not hypothetical; they are structurally embedded in how AI systems are built and deployed. On transparency and accountability, citizens in post-conflict contexts often have profound and justified distrust of state institutions. Opaque AI systems deployed without explainability mechanisms will deepen that distrust, not overcome it. Conversely, this is also an opportunity where legacy institutions have collapsed, post-conflict states can design AI governance frameworks from inception with transparency and accountability built in, rather than retrofitted. The opportunity is real. Post-conflict settings, precisely because they are not constrained by legacy infrastructure, can leapfrog incremental approaches and adopt governance-first AI frameworks that more stable nations struggle to implement. The Dialogue should recognize this potential - not only as a challenge to be managed, but as a governance innovation space with lessons for the entire international community.

The Unique Value of the AI Dialogue The AI Dialogue holds a unique and vital position in today's international landscape - one that no other forum currently fills. Its true value isn't in duplicating the technical work happening elsewhere, but in offering something those processes lack: a genuinely universal, intergovernmental space where every nation has an equal seat at the table. Within this context, the Dialogue serves four critical roles: A Bridge Between Fragments: AI governance efforts are currently scattered across dozens of regional, multilateral, and bilateral processes . Each operating with different principles, priorities, and membership. The Dialogue can map these efforts, identify where they converge and where they contradict, and begin building coherence across them. Its role is not to replace existing processes, but to make them visible to one another and create the conditions for genuine coordination. A Platform for Legitimate Representation: Too many nations.specifically across the Global South, small island states, and regions affected by conflict have been left out of conversations that will change their futures. Because of its UN foundation, the Dialogue has the unique authority to correct this imbalance in a way that purely economic or technical forums simply cannot. An Early Warning System: This space allows us to flag emerging risks, governance gaps, and unintended consequences before they spiral into full-blown crises. This is especially vital for countries that don't yet have massive regulatory departments to monitor these shifts in real-time. An Incubator for Global Norms: While it doesn't need to set binding laws today, the Dialogue can cultivate the shared principles and voluntary standards that lead to formal cooperation later. We can learn from how we built international human rights and environmental protections - starting with a shared sense of responsibility. Ultimately, the Dialogue's greatest achievement will be making AI governance feel and function as truly global as the technology itself.

Existing Mechanisms to Connect - The OECD AI Policy Observatory tracks how countries are developing AI policies and where they align or conflict - giving the Dialogue a ready evidence base to work from. - The EU AI Act shows that comprehensive, binding AI regulation is achievable and offers practical lessons for other regions. - ISO 42001:2023 provides a concrete management standard that any organization, in any country, can use to govern AI responsibly. translating high-level principles into day-to-day practice. The Added Value of the AI Dialogue What makes the Dialogue different from every other forum is simple: it belongs to everyone. Unlike club-based processes, it gives all 193 UN member states equal standing . moving AI governance from decisions made by a few powerful economies toward genuine global consensus. It opens the door for the Global South, small island states, and civil society to help shape the rules, not just live by them. And by grounding its work in the findings of the Independent International Scientific Panel, it ensures that decisions are based on evidence rather than political or commercial interest. By connecting these existing building blocks, the Dialogue can turn a scattered, unequal governance landscape into something coherent, accountable, and truly global.

The value of the AI Dialogue depends entirely on whether its structure matches its ambition of being truly inclusive. Governments should move beyond formal statements toward structured peer exchanges sharing specific governance challenges and practical lessons. Smaller and developing nations need formats that give them genuine speaking time, not just symbolic presence. Civil society and affected communities should be integrated into thematic discussions from the start, not added as a final session. Their firsthand knowledge of how AI affects real people must directly inform deliberations. Researchers and academia can contribute most effectively through written submissions that are systematically synthesized and presented to participants in accessible, usable form. The private sector should participate under clear transparency requirements, with declared interests and structured separation between input and decision-making roles. On format, the Dialogue should prioritize interactive moderated discussions over one-way presentations. Hybrid participation must be fully functional - not a secondary experience - so that geography and resources do not determine who can meaningfully engage. Every session should produce a clear, publicly available summary to ensure that inclusive participation visibly shapes outcomes not just the agenda

The gap between who shapes AI governance and who is most affected by it remains one of its central failures. Conflict-affected and fragile states are almost entirely absent, despite facing AI's most immediate risks. The Global South participates far less than its population warrants held back by limited resources, language barriers, and the location of major forums. Indigenous communities, linguistic minorities, and women in low-income countries face disproportionate AI harms yet remain invisible in the spaces where solutions are designed. Including them requires three concrete steps: - dedicated participation funding built into the Dialogue's core budget - regional preparatory consultations held before Geneva - outcome documents that genuinely reflect these voices not just acknowledge their presence. Inclusion is only real when it changes what is decided, not just who is in the room.

Several examples offer concrete lessons for effective AI governance. Rwanda's national AI policy shows that developing nations can build governance frameworks that are both locally grounded and internationally aligned - demonstrating that governance capacity can be deliberately built, not simply inherited. The African Union's Continental AI Strategy proves that effective governance does not require importing frameworks designed elsewhere. It centers development priorities, data sovereignty, and capacity building as core governance concerns. ISO 42001:2023 translates abstract governance principles into auditable, day-to-day practice - making responsible AI management accessible to organizations in any country, regardless of institutional sophistication. Disclosure As a non-native English speaker, I used Claude (Anthropic) as an AI writing assistant to help refine clarity and language. All substantive content reflects my independent thinking and research.