Country profile · Somalia
Somalia links human responsibility to the capacity to assess AI
Somalia’s intervention connected human accountability with the expertise, infrastructure and local data needed to evaluate AI independently. A 2025 retrospective study of Somali survey data offers a bounded example of machine-learning analysis with Somali institutional participation.
Ali Mohamed Omar
State Minister for Foreign Affairs
UN Security Council meeting 10228 · 23 September 2026
UN position
What Somalia argued
UN proposition — paraphraseHuman responsibility and independent African evaluation capacity should prevent conflict and technological dependency.
Somalia argued that legal, ethical and moral accountability for high-consequence AI decisions must remain with people. The intervention emphasised human responsibility in military decisions, including decisions involving force, autonomous weapons and nuclear command and control.
It called for safety before deployment, including incident reporting, layered safeguards, independent audits and the ability to pause deployment when risks cannot be managed. These were proposals; the intervention did not define specific capability thresholds.
Somalia linked meaningful oversight to local expertise, reliable connectivity, computing, energy, regulatory capacity and data suited to African languages and contexts. It argued that African participation should include the ability to assess systems and shape governance, not merely receive or supply technology.
The intervention supported inclusive, UN-centred governance and opposed arrangements shaped only by a small group of technologically powerful states and companies. These are diplomatic arguments and proposed safeguards, not demonstrated outcomes.
Key themes
Absolute human responsibilityAccountability in military and other high-consequence decisionsSafety before deployment, incident reporting and independent auditsLocal data and independent evaluation capacityAfrican participation and digital sovereigntyInclusive UN-centred governance
Scientific context
Selected AI × science example
Observational/public-health researchA Scientific Reports study published in 2025 applied seven machine-learning algorithms and SHAP explanations to survey data on fertility preferences among women in Somalia. This was completed retrospective population-health research. A separate dengue forecasting proposal remains a proposal and is not treated as a completed research result.
Evidence boundary
What the evidence does show
The study documents retrospective machine-learning analysis of Somali survey data using seven algorithms and SHAP explanations, with Somali institutional participation.
What it does not show
The retrospective analysis does not establish causal effects, a deployed clinical or public-health service, or demonstrated health benefit. One study does not establish national AI research capacity. The separate dengue forecasting proposal is not a completed result.
Speech–science connection
The study illustrates locally relevant analytical work and why data and evaluation capacity matter. It does not demonstrate independent evaluation capacity at national scale or validate the intervention’s proposed safeguards.
Sources
UN Security Council meeting 10228 · 23 September 2026. Diplomatic position is paraphrased; audio verification is incomplete.
Somalia
Sani et al., Scientific Reports / PMC · Scientific Reports / PMC
“Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.” Published 20 July 2025. DOI 10.1038/s41598-025-04704-y.
Supports: selected observational/public-health ML example using 2020 Somali survey data.
Evidence boundary: retrospective/cross-sectional analysis; no deployed service, causal inference or demonstrated health benefit.
View sourceDOI: 10.1038/s41598-025-04704-y