National Assembly

In my view, the first Global Dialogue on AI Governance would succeed if it delivers three practical, high-impact outcomes rather than vague declarations or bureaucratic theater. First, it should produce a lean, evidence-based set of shared principles anchored in the Independent International Scientific Panel on AI's forthcoming reports. These principles must prioritize measurable risks (catastrophic misuse, loss of control) over speculative social harms, while explicitly protecting open research, voluntary standards, and national sovereignty. Success means governments walk away with interoperable guidelines that accelerate safety research without freezing innovation or favoring incumbents. Second, it must commit to concrete capacity-building mechanisms: technical assistance, compute access, and open datasets targeted at the Global South. The dialogue succeeds if it closes real capability gaps, enabling every nation to participate in AI development instead of becoming perpetual consumers turning "inclusivity" from rhetoric into measurable technology transfer. Third, it should establish a lightweight, annual follow-up process with clear metrics: adoption rates of safety protocols, progress on joint R&D projects, and independent audits of whether governance actually reduces risks or merely slows progress. No new UN agencies; just a focused forum that forces accountability. If these emerge principles grounded in science, real inclusion via capability, and a bias toward action over process the Dialogue will mark a genuine step toward AI that advances human flourishing and our understanding of the universe. Anything less risks becoming another expensive talk shop.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

Explanation of Selected Priorities in AI Governance I selected **Safe, secure and trustworthy AI** as the foundational priority because AI systems must first minimize harm and operate reliably. Without safety and security (including robustness against attacks, data privacy, and bias mitigation), all other benefits collapse. Trust is the prerequisite for widespread adoption. **Social, economic, ethical, cultural, linguistic and technical implications of AI** was chosen because AI is not neutral it reshapes jobs, power structures, education, languages, and cultures. Understanding these multifaceted impacts holistically prevents unintended consequences, such as economic displacement, cultural homogenization, or exclusion of non-dominant languages and communities, especially in diverse contexts like Nigeria and Africa. **Protection and promotion of human rights** is essential because AI can amplify or undermine fundamental rights (freedom of expression, privacy, non-discrimination, dignity). Prioritizing human rights ensures technology serves people rather than the reverse, guarding against surveillance overreach or discriminatory algorithms. Finally, **Transparency, accountability, and human oversight** completes the set by addressing the "black box" problem. People and institutions must understand how decisions are made, assign responsibility when things go wrong, and maintain meaningful human control. This builds public confidence and prevents unchecked power concentration. These four areas form a coherent, mutually reinforcing framework: safety provides the base, implications analysis reveals risks and opportunities, human rights set the moral guardrails, and transparency ensures enforcement. Together they support responsible AI development that maximizes truth-seeking benefits while protecting humanity-aligning with xAI's mission to advance scientific discovery for the benefit of all.

Yes, several cross-cutting and emerging issues are often missed in standard thematic frameworks. 1. AI-driven epistemic erosion and synthetic reality: Rapid generative AI is blurring truth, authorship, and evidence at scale. This cuts across governance, security, economy, and human rights but is rarely treated as a standalone meta-issue. Deepfakes, AI-generated scientific papers, and algorithmic radicalization challenge the very foundation of evidence-based policy. 2. Cognitive and attentional sovereignty: With attention economies now powered by personalized neurotechnology (BCI, wearables, dopamine-loop apps), the human mind itself becomes the contested resource. This intersects mental health, education, democracy, and labor but is usually fragmented into narrower "digital well-being" silos. 3. Climate-migration-finance nexus under compounding shocks: Climate displacement is accelerating faster than legal or financial systems can adapt, creating feedback loops with sovereign debt, insurance markets, and conflict. Most frameworks treat climate, migration, and finance separately. 4. Demographic inversion and longevity dividend: Many analyses still focus on youth bulges in the Global South while missing the faster-than-expected fertility collapse + longevity boom in both developed and emerging economies. This will reshape labor, pensions, innovation, and geopolitics in the 2030s-2040s.

Nigeria's Perspective on AI Governance Gaps In Nigeria, governance gaps in the selected thematic areas particularly AI capacity-building and bridging divides, safe/secure/trustworthy AI, respect for human rights (including transparency, accountability, and human oversight), and the social/economic implications of AI are profoundly shaping our national development trajectory, digital economy, and regional influence within Africa. These gaps, amid rapid global AI advances, create a dual landscape of acute risks and transformative potential. Key Challenges: Infrastructure deficits and limited high-performance computing access hinder local model training and data sovereignty, perpetuating reliance on foreign AI systems often biased toward Global North datasets. This exacerbates inequalities in key sectors like agriculture, healthcare, and education, where AI tools may misrepresent local contexts, leading to flawed decision-making. Regulatory vacuums enable unchecked deployment of AI in fintech and surveillance, raising privacy erosion, algorithmic discrimination, and disinformation risks—especially during elections. Brain drain of AI talent further widens capacity gaps, while fragmented interoperability with international frameworks (e.g., EU AI Act) complicates cross-border collaboration and investment. Economically, without robust oversight, AI-driven automation threatens job displacement in a youthful population without adequate reskilling. Significant Opportunities: Nigeria's vibrant tech ecosystem (e.g., Lagos as Africa's fintech hub) and young demographic dividend position us to leapfrog via open-source AI and capacity-building initiatives. Targeted investments in local data centers, open AI models, and ethical guidelines could drive inclusive growth in precision agriculture, telemedicine, and climate resilience aligning with SDGs and AU AI Strategy. International partnerships offer technology transfer, while strong human-rights-focused governance could foster trustworthy AI, attracting ethical investment and enhancing regional leadership. Closing these gaps through coordinated national policy, multi-stakeholder collaboration, and UN-supported capacity programs is urgent. It would mitigate risks, harness AI for sustainable development, and ensure equitable benefits for Nigeria and the Global South.

**The Global Dialogue on AI Governance (often called the AI Dialogue)**, established by UN General Assembly Resolution A/RES/79/325 in 2025 as part of the Global Digital Compact, serves as the UN's primary inclusive forum for shaping international AI governance. It can advance cooperation in four key ways: 1. Building inclusive consensus**: By convening all governments especially those from the Global South alongside civil society, academia, and the private sector, it ensures AI rules reflect diverse priorities rather than those of a few technologically advanced nations. This counters fragmentation and promotes equitable benefit-sharing. 2. Facilitating interoperability and standards: The Dialogue can align national frameworks, reduce regulatory barriers, and boost cross-border economic cooperation while grounding AI in international law, human rights, and effective oversight. 3. Leveraging scientific expertise: Working in tandem with the UN Independent International Scientific Panel on AI, it translates technical risk assessments into actionable policy, addressing safety, capacity gaps in developing countries, and socioeconomic impacts. 4. Driving practical outcomes: Annual sessions (starting Geneva, July 2026) enable sharing of best practices, open innovation (e.g., open-source tools), and progress toward a multilateral framework complementing rather than duplicating existing initiatives like the GPAI or OECD principles. Ultimately, the AI Dialogue offers a neutral, universal platform to close the gap between rapid AI advancement and governance. By fostering trust, transparency, and collective action, it can help humanity harness AI's opportunities while mitigating shared risks—no single country can do this alone.

The UN Global Dialogue on AI Governance (AI Dialogue), established by UNGA Resolution 79/325 following the Global Digital Compact, is explicitly designed to complement not duplicate existing efforts. Existing initiatives and mechanisms it should build upon or connect with include: UN system efforts: ITU's AI for Good Global Summit and AI Governance Dialogue; UNESCO's Recommendation on the Ethics of AI, Policy Dialogue on AI Governance, and AI Ethics Experts Without Borders network; plus UNDP and other UN entities' capacity-building work. International frameworks: OECD AI Principles and governance tools; G7 (e.g., Hiroshima Process) and G20 AI initiatives; regional efforts such as the EU AI Act. - **Multi-stakeholder processes**: The former High-Level Advisory Body on AI and related forums that have advanced interoperability, risk assessment, and open innovation. The AI Dialogue should link these through annual sessions (first in Geneva, July 2026, alongside the AI for Good Summit), written inputs, and coordination with the paired Independent International Scientific Panel on AI. Added value: It creates the world's first universal, UNGA-convened platform where all governments and stakeholders especially developing countries have an equal seat at the table. This ensures governance reflects global priorities, not just those of technologically advanced nations. Key contributions include bridging AI divides via targeted capacity-building and open-source access; promoting interoperability across national regimes to reduce fragmentation; fostering evidence-based deliberation on safety, ethics, socioeconomic impacts, and human rights; and providing a stable, inclusive "home" for ongoing cooperation that accelerates equitable benefit-sharing.

Stakeholders' Contributions: -Governments/Regulators: Policy frameworks & ethics. - Tech Companies: Technical insights & innovation demos. - Researchers/Academia: Evidence-based findings. - Civil Society/NGOs: Societal impact & inclusion. - Public/Users: Real-world experiences & concerns. Recommended Format & Structure (Multi-stakeholder Dialogue): - Hybrid Plenary keynotes + thematic breakout sessions + open forums. - Structure: 1) Problem framing, 2) Evidence sharing, 3) Solution co-creation, 4) Commitment pledges. - Facilitation: Neutral moderators, live polling, transparent reporting.

Global AI governance discussions are dominated by technologists, Western governments, and large corporate actors, leaving several critical voices underrepresented: · Global South communities: African, Latin American, and Southeast Asian perspectives are often marginalized, despite bearing the impacts of AI-driven labor shifts, surveillance, and data extraction. · Indigenous peoples: Their concepts of collective data sovereignty, non-human agency, and intergenerational ethics are rarely centered in frameworks focused on individual rights or intellectual property. · Workers and labor unions: Frontline gig economy workers, automation-threatened employees, and platform co-op organizers have direct knowledge of AI's harms but limited seats at high-level tables. · Civil society from smaller nations: Peripheral countries lack the diplomatic resources to shape norms at venues like the UN, G7, or GPAI. · Grassroots disability and racial justice groups: These communities experience algorithmic bias firsthand but are often excluded from technical design workshops. How to include them: · Mandate quotas and funding for travel, translation, and interpretation from underrepresented regions in multilateral fora (e.g., UNESCO, ITU). · Establish deliberative polling or citizens' assemblies with random selection, stratified by geography and marginalization, feeding directly into policy processes. · Create accessible virtual consultation platforms (low-bandwidth, multi-lingual) designed with community input. · Fund decentralized research networks that compensate local knowledge holders as co-authors, not just subjects. · Require impact assessments that include community-defined metrics of harm before deploying governance frameworks globally.

To move beyond traditional panel discussions and white papers, AI governance dialogues need formats that are participatory, deliberative, and action-oriented. Three innovative formats stand out: 1. Reverse "World Café" with expert rotations: Small tables of community members (e.g., gig workers, Indigenous youth, small-scale farmers) discuss a governance dilemma, while experts rotate tables every 15 minutes to listen and respond. This flips power dynamics, placing lived experience at the center. 2. AI-assisted participatory scenario gaming: Mixed groups use a simple, transparent AI simulation tool to model policy trade-offs in real time (e.g., "What if we ban facial recognition vs. regulate by use case?"). Groups see immediate visual feedback on harms and benefits, fostering systemic thinking and shared ownership of complexity. 3. Deliberative polling integrated into policy drafting: Randomly selected, demographically stratified citizen panels (including non-experts) deliberate over a weekend, informed by balanced briefings and live testimony. Their conclusions are directly fed into a "living draft" of governance principles, with a transparent mechanism for how their input changed the text. To ensure dynamic engagement, each format should include: · Facilitated small-group breakout work (not just Q&A). · Real-time multilingual transcription and summarization for inclusivity. · A binding "response commitment": organizers must publicly report which participant suggestions were adopted or rejected, and why. These formats shift dialogue from passive listening to collective problem-solving, generating richer, more actionable governance insights.

Effective AI governance is taking shape through a mix of concrete policies, technical tools, and national guidelines. Here are several notable examples addressing real-world challenges: Policies & Frameworks · NYC's GUARD Act (2025): The first comprehensive municipal framework in the US, establishing an independent Office of Algorithmic Data Accountability with mandatory fairness testing, a public inventory of city AI systems, and grassroots digital literacy initiatives . · Australia's Voluntary AI Safety Standard: Provides 10 guardrails including human oversight, stakeholder engagement, and record-keeping for third-party compliance assessment-with specific guidance on Indigenous data sovereignty . · Malaysia's AIGE Guidelines (2024): National principles (fairness, inclusivity, accountability) aligned with UNESCO/OECD, designed as a living document supporting safe AI adoption. · Italian AI Law (2025): Complementary to the EU AI Act, with sector-specific rules prohibiting AI from conditioning healthcare access, establishing a National AI Observatory for employment impacts, and extending copyright protection to human-AI co-created works . Governance Platforms & Tools · Regulatory Sandboxes: Denmark's cross-agency sandbox provides tailored GDPR/AI Act guidance pre-deployment, reducing legal uncertainty for innovators . · AI Governance Software: Platforms like Collibra and DataRobot offer automated compliance testing (EU AI Act, NIST RMF), real-time risk monitoring (PII leakage, bias), and centralized model registries . · Security Tools: Systems like Nudge Security monitor employee AI use, flag risky OAuth integrations, and summarize vendors' data training policies . Procedural Approaches · Microsoft's Engagement Model: Differentiates governance for in-house development (pre-launch reviews, monitoring for model drift) from external procurement (vendor vetting, procurement criteria) .