By Sooter Saalu
AI in Africa faces challenges due to data scarcity, language diversity, and bias, hindering effective solutions.
If you’ve ever relied on a digital map for directions, you’ve likely encountered issues where the voice assistant mispronounced a familiar location or failed to understand your accent. This isn’t a rare occurrence, it happens frequently with voice assistants such as Apple’s Siri and Google’s Gemini. Imagine this challenge magnified across an entire continent where AI systems are built on incomplete or limited data. This problem isn’t just technical; it reflects deeper disparities in technology development, representation, and access to resources.
Data scarcity in Africa is a significant hurdle, largely driven by linguistic diversity. While about half of the online content available globally is in English, fewer than 20% of Africans speak it. With over 2,000 languages spoken across the continent, most AI models are trained on datasets dominated by Western languages and references. In Nigeria, home to over 200 million people and projected to be the world’s third most populous nation by 2050, fewer than ten publicly available datasets support local language processing. Consequently, voice recognition tools struggle with indigenous languages like Yoruba, rendering them ineffective. The issue extends beyond speech recognition, AI models in health diagnostics and financial algorithms often overlook region-specific diseases, healthcare practices, and financial systems unique to Africa.
AI solutions built in Silicon Valley don’t necessarily translate well to places like Computer Village in Lagos, or smallholder farms in rural Kenya. Agricultural AI, optimized for farms in Minnesota, may not adapt to Benue, where smallholder farmers rely on rain-fed agriculture and face unique pest threats. Similarly, chatbots trained on US or UK datasets often misinterpret African-specific contexts, leading to irrelevant or misleading recommendations. As a result, many African consumers and businesses face a constant struggle to adapt to AI systems that were designed without understanding their unique needs and challenges.
A stark example of how localized solutions can work is the success of mobile money apps like M-Pesa. M-Pesa thrived because it was tailored specifically to the way Kenyans manage their money, rather than attempting to impose a financial model based on Western banking systems. M-Pesa understood the complexities of Kenya’s cash economy, providing a solution that was both simple and highly effective. AI must adopt the same approach, training on data that reflects Africa’s diverse markets, languages, cultures, and social norms. Without this, technology risks becoming a tool that amplifies existing power imbalances, imposing foreign models without giving local populations a meaningful voice in their development.
The stakes are high. Africa’s AI market is rapidly growing, and its success hinges on whether the data driving this growth is accurate, inclusive, and relevant. If AI is built on incomplete or biased data, it could exacerbate existing inequalities rather than help to reduce them. One well-documented issue in AI development is biased facial recognition, models trained predominantly on lighter-skinned individuals struggle in cities like Accra and Addis Ababa, where the majority of people have darker skin tones. Similarly, generative AI systems trained on Western literature may fail to recognize the significance of African literary traditions, such as Swahili poetry or Amharic storytelling, thereby overlooking content that is central to local cultures and communities. Research has shown that a large percentage of data used in machine learning comes from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies, leading to AI systems that often fail to account for the realities of non-Western societies.
Grassroots initiatives like Masakhane, which crowdsources African language datasets, and Lacuna Fund, which finances underrepresented data projects, demonstrate the power of local expertise. These initiatives are pivotal in making sure that Africa’s unique languages and contexts are represented in global AI systems. While these grassroots efforts are essential, they alone cannot solve the broader issue, there must be a concerted effort from the global tech community to engage African researchers, startups, and communities. This isn’t simply a matter of charity or corporate social responsibility; it is a practical necessity. With 60% of Africa’s population under the age of 25, and smartphone adoption rates continuing to rise, the continent’s young, tech-savvy population will be key players in shaping the next era of global technology. Africa will help define the future of AI, with or without Silicon Valley’s involvement.
The question isn’t whether AI can work in Africa; it’s whether AI is ready to learn from Africa. To serve people effectively, AI must first understand them and their perspectives, in every language, market, and town. AI systems must evolve to represent the rich diversity of African cultures, languages, and contexts. This empowers Africa to move beyond a narrative of dependency, fostering a future where the continent actively drives and innovates within the global AI landscape. It’s time to acknowledge that Africa is not a passive recipient of technology, but an active participant in shaping it. By prioritizing local data, knowledge, and expertise, we can create an AI ecosystem that is not just inclusive, but also transformative for the world.
About the Author
Sooter Saalu is a data professional and technical writer specializing in documentation for data and DevOps products. As a documentation specialist at Draft.dev, he consults on technical articles and has contributed to over 100 pieces for clients like Redpanda and Dataiku. With a background in psychology and computer science, Sooter effectively communicates complex concepts to diverse audiences and has also worked on open-source projects such as Bokeh and Bacalhau.
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