Global AI Safety Ignores African Harms, Analysis Finds
The global agenda for AI safety is failing to address urgent issues and harms on the African continent, according to a new analysis from Tech Policy Press.

What happened?
A new analysis published by Tech Policy Press highlights how the global agenda for AI safety contains significant blind spots regarding African contexts. Current international frameworks and AI safety institutes primarily focus on catastrophic risks and Western challenges. Consequently, more immediate and locally rooted harms on the African continent—such as the lack of language support, the exploitation of data workers, and biased datasets—are being overlooked.
Key facts
| Källa för analysen | Tech Policy Press |
|---|---|
| Fokusområde | Global AI-säkerhet och afrikanska kontexter |
Why it matters
When global AI safety standards are designed from a narrow Western perspective, systems risk reinforcing existing divides and inequalities. Without consideration for local languages, cultural contexts, and regional labour conditions, AI tools can cause significant harm in regions that lack representation in primary decision-making forums.
Who is affected?
The findings are relevant to international bodies, AI researchers, policymakers, and technology companies involved in shaping global safety standards. It notably impacts users and developers in the Global South who are disproportionately affected by models unadapted to their local languages or socio-economic conditions.
Impact on the EU
Within the EU, work on AI safety has largely centred on the EU AI Act and strict regulations for high-risk applications. This Western focus risks overlooking how AI models affect countries outside the EU, potentially creating blind spots within global regulatory frameworks.
What else you should know
The Tech Policy Press report underlines that unless African researchers, civil society, and policy experts are included in the design of global AI frameworks, international initiatives risk remaining ineffective and exclusionary.
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