On 26 August 2025, the United Nations General Assembly adopted Resolution A/RES/79/325 by consensus, formally establishing the Independent International Scientific Panel on Artificial Intelligence. That decision did not simply add another body to the UN system. It marked the moment when AI governance became a permanent global priority.
The message was unmistakable: Artificial Intelligence is no longer experimental. It is infrastructure.
AI already shapes how students learn, how doctors diagnose, how banks prevent fraud, and how information spreads across societies. When a technology influences education, health, finance, labour markets, and democracy simultaneously, informal oversight is no longer sufficient. Governance must be structured, continuous, and grounded in science.
The seriousness of this shift became clear in early 2026. After an open global call that attracted more than 2,600 applications from over 140 countries, the UN appointed 40 independent experts to serve on the Panel. Their mandate is advisory, not regulatory. They will not enforce laws or restrict innovation. Instead, they will publish annual, evidence-based assessments on AI’s societal, economic, and ethical impacts in the non-military domain.
Their first major report is scheduled for July 2026 at the UN Global Dialogue on AI Governance in Geneva. That publication will initiate something unprecedented: a recurring, science-driven global evaluation of AI’s trajectory.
This global process may appear distant from Kigali. It is not.
I build AI systems in Rwanda, primarily in education and healthcare. My experience reflects the broader reality of innovation in the Global South: our limitation is rarely talent. It is scale.
When I developed a facial recognition attendance system later published in a Q1 academic journal, I did so without access to advanced computing clusters. Every model choice had to account for limited hardware. Efficiency was not optional; it was survival.
When I built a course-specific AI assistant for universities using LangChain, I deliberately constrained it to instructor-approved academic materials. That was not just a technical configuration. It was a governance decision. I wanted a system rooted in our curriculum, our context, and our standards rather than one that relied entirely on external outputs trained on distant data.
Later, while developing a prototype AI screening assistant for neglected tropical skin diseases, I aligned the model strictly with official WHO clinical guidelines. In healthcare, precision and traceability are non-negotiable. Trust determines adoption. Without it, even the most advanced model becomes unusable.
These experiences reveal a larger structural truth: innovation in developing countries is constrained not by intelligence, but by computational infrastructure, research funding, and institutional scale.
Meanwhile, most frontier AI systems are trained in a small number of technologically dominant countries. Training large-scale models demands enormous computational resources, specialized chips, and vast datasets. Those who control that infrastructure shape standards, capture economic returns, and influence global norms.
That concentration of power matters.
Africa does not lack ideas. It lacks sustained investment in AI capacity.
This is where the UN Scientific Panel becomes strategically significant. It will not build supercomputers in Rwanda. It will not directly fund African startups. But it can reduce informational inequality.
By producing independent, globally accessible scientific assessments, the Panel creates a shared knowledge foundation. Policymakers in emerging economies will gain access to neutral, evidence-based analysis rather than relying solely on corporate narratives or geopolitical messaging. For countries designing national AI strategies, that shared knowledge strengthens decision-making.
The composition of the Panel reinforces its legitimacy. The 40 experts represent all UN regions, diverse disciplines, and balanced gender representation. That diversity is not symbolic. AI systems behave differently across linguistic, cultural, and economic contexts. Language models trained primarily on English data often underperform in African languages. Automation disrupts manufacturing economies differently than service-based ones. Governance must reflect those variations.
However, diversity alone will not guarantee impact. Independence will.
AI has become strategic infrastructure for major powers and multinational corporations. Political and commercial pressures are inevitable. The Panel’s credibility will depend on transparency, rigorous peer review, and strong conflict-of-interest safeguards. In science, credibility flows from evidence not authority.
At the same time, global discussions must remain grounded in present realities. While headlines focus on speculative superintelligence, communities today are confronting misinformation, deepfakes, biased algorithms, and labour disruption.
When I engineered my facial recognition system, dataset balance was not theoretical, it was essential to prevent biased outputs. When I restricted my university AI assistant to verified materials, I did so to limit misinformation. These everyday engineering decisions are, in fact, governance decisions.
Global AI governance does not require identical systems across nations. It requires shared principles: transparency, safety testing, accountability, data protection, and respect for human rights. Trust is the foundation. Without trust, adoption falters.
For Rwanda, this historical moment presents a clear sequence of opportunity.
First, we can use the Panel’s independent assessments to strengthen our national AI strategy.
Second, we can align with emerging global standards to enhance credibility and attract partnerships.Third, we can push for deeper regional collaboration across Africa, pooling research capacity, data resources, and policy coordination.
But opportunity does not equal transformation.
No UN initiative will convert Rwanda from an AI consumer into an AI producer. That transformation demands domestic commitment: sustained investment in STEM education, research funding, computational infrastructure, and stronger university–industry collaboration.
When I built the AI screening assistant for neglected tropical diseases, I witnessed how locally designed systems can directly address local health realities. That is the model we must scale: AI built for our challenges, in our languages, aligned with our priorities.
The establishment of the UN Independent International Scientific Panel on AI marks the institutional beginning of a new era in global technology governance. The infrastructure for coordinated global oversight now exists.
The question is no longer whether AI will shape the future. It already does.
The real question is who will shape AI.
If only a few nations design, train, and govern advanced systems, global inequality will intensify. But if Rwanda and other countries in the Global South engage proactively, invest strategically, and build responsibly, we can help define the next technological chapter.
The timeline has begun.
The global structure is in place.
The opportunity is real.
What we choose to do next will define the coming decade.
