An analysis of national governance strategies for AI in African countries
# African AI strategies × WHO digital-health competencies
Quantitatively maps African national **AI strategies** onto the competency
domains of the **WHO Global Strategy on Digital Health** (2020–2027), using
multilingual sentence-transformer embeddings, and visualises the result as
heatmaps.
For each country, the strategy text is chunked into passages, every passage is
embedded and compared (cosine similarity) against each WHO competency-domain
description, and the passage scores are aggregated into one score per
(country, competency). The output is a country × competency alignment matrix.
> **What the score means.** It measures how strongly a document *engages with the
> WHO digital-health competencies in text* — not a country's real-world
> digital-health capability, and not AI-strategy quality in general. A horizontal
> AI policy that barely mentions health will score low even for a country that
> leads in digital health in practice (see *Two analysis tracks* below).
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## Two analysis tracks
The corpus contains two genres of document, analysed separately so they stay
comparable:
| Track | Corpus | What it answers |
|---|---|---|
| **Primary** | National **AI strategies** (`data/national_strategies/`) | How much does each country's AI strategy engage digital health? |
| **Secondary** | Dedicated **digital-health / eHealth strategies** (`data/digital_health_strategies/`) | How well do purpose-built digital-health strategies align with the WHO framework? |
A digital-health document aligns with WHO *digital-health* competencies almost by
construction, so mixing the two genres breaks comparability. Keeping them
separate is what makes each track interpretable. **Rwanda is the canonical
example:** its horizontal *National AI Policy* ranks last in the primary track
(0.43 — it barely mentions health), while its dedicated *Digital Health Strategy*
scores 0.61 in the secondary track. Same country, two documents, two correct
answers.
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## Quickstart
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