Bridging the AI Divide: How the Lack of African Data and Context Fuels AI Bias—and What Africa Can Do About It
Artificial Intelligence (AI) is transforming every corner of the world—but Africa risks being left behind in both the development and benefits of this technology. A critical driver of this imbalance is data —or rather, the lack of African data, cultural context, and representation in the datasets that train the AI systems. This gap not only creates biased AI systems but perpetuates a modern form of data colonialism , where African digital realities are either erased or misrepresented by algorithms built elsewhere. The Problem: AI Bias Begins with Data AI models learn from the data they are fed. If the data lacks African languages, dialects, cultural practices, or socioeconomic realities, the resulting AI systems are likely to misunderstand, misrepresent, or entirely ignore African users. This has real-world consequences—from healthcare algorithms that misdiagnose African patients, to financial models that exclude informal economies, to language models that can’t understand...