World Bank: African languages are underrepresented in Artificial Intelligence
Sunday 11th October, 2026 01:33 AM|By Aloys Michael
As artificial intelligence (AI) transforms education, business and access to information, millions of Africans risk being left behind if the technology cannot adequately understand the languages they speak.
The World Bank has identified the underrepresentation of African languages, institutions and economic activities in AI training data as a barrier to developing systems that respond effectively to the continent’s needs.
In its Africa Economic Update: Building AI Readiness, published in October 2026, the bank warns that gaps in global datasets can limit the contextual performance of AI models.
“Global training data sets substantially underrepresent African countries, languages, institutions, and economic activities,” the report says.
The concern is significant for a continent with approximately 2,000 languages. Although AI tools can generate text, answer questions and support decision-making, their usefulness depends partly on how well they understand different languages and cultural contexts.
The challenge is therefore not simply whether Africans can access AI, but whether the technology can serve them reliably in the languages they use.
The language gap in AI
According to the World Bank, an estimated 56 per cent of open-
This imbalance raises concerns about the ability of AI systems to serve people who communicate in languages with fewer digital re
AI models learn patterns from training data. When high-quality material in a particular language is scarce, developers may have fewer reaffect tasks such as translation, answering questions and interpreting local expressions
The consequences could extend to everyday services. Farmers may need agricultural advice using familiar terminology, students may benefit from learning materials in their preferred languages, and health workers may require clear communication tools for patients.
These are potential applications rather than proof that AI systems consistently fail in African languages. Performance varies by language, model and task, making independent testing essential.
Building AI around African needs
The World Bank argues that adapting AI to local conditions offers significant opportunities for Sub-Saharan African countries.
“The deepest returns for Sub-Saharan African countries lie in adapting AI to local data, languages, and problems,” the report says
One example is InkubaLM, a small language model developed by African AI company Lelapa AI. According to the World Bank, it was trained on five African languages: isiZulu, Yoruba, Hausa, Swahili and isiXhosa.
The report says the model reportedly outperforms much larger models on those languages.
The example illustrates how specialised models focused on local languages could complement larger, general-purpose AI systems.
However, performance claims depend on the benchmarks and tasks evaluated. Success in selected tests does not automatically mean a model performs better in every real-world application.
Expanding these efforts requires quality language datasets, skilled researchers, sustainable funding and computing re to determine whether they understand questions correctly and provide reliable answers
Who owns Africa’s language data?
Developing African-language AI also raises questions about how language data is collected, controlled and used.
Building datasets may require written materials, speech recordings, translations and contributions from local researchers and communities. Clear rules are needed to establish permission for data use, protect privacy and determine how contributors are recognised or compensated where appropriate.
These questions matter because commercial AI systems can generate economic value, while the communities whose language res
Governments, universities, technology companies and language communities can work together to improve language re
Regional cooperation could also help developers share expertise and reduce the cost of creating tools for languages with relatively small digital markets.
Infrastructure remains a challenge
Language representation is only one part of Africa’s AI readiness challenge. The World Bank also highlights constraints involving skills, infrastructure and computing access across Sub-Saharan Africa.
These limitations can make it harder for local researchers and businesses to develop, test and maintain AI systems. Even when a tool supports an African language, its value depends on whether people can access it affordably and use it with available devices and internet connections.
Policymakers and developers must therefore look beyond the number of languages a system claims to support. Progress should be measured by accuracy, affordability, accessibility and the ability of people to complete practical tasks.
Addressing the language gap will require investment in language re participation from the communities expected to use the technology
Systems should be tested across languages and dialects, with particular attention to applications where inaccurate information could cause harm. Users should also have a role in determining which problems AI tools should address.
The World Bank’s findings highlight a broader challenge for Africa’s digital transformation: adopting advanced technology does not automatically ensure that it works for everyone.
African-language AI will not, by itself, resolve wider digital inequalities. But improving language representation could help make education, information and digital services more accessible.
The real measure of AI progress in Africa will not be how many languages a system claims to support, but how reliably people can use it to learn, work and participate in the digital economy.
Aloys Michael
View all posts by Aloys Michael
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