In northern Nigeria, a maternal health app called MomConnect NG struggled with accuracy. Why? It relied on English-centric NLP models that didn’t understand Hausa or Yoruba languages spoken by millions.
A collaboration between Data Science Nigeria, UNICEF, and the Masakhane NLP Project sought to fix that by building multilingual datasets tailored to the region’s linguistic landscape.
The impact was immediate:
40% increase in correct maternal health information delivery
30% drop in misdiagnosed symptoms
Enhanced user trust and regional adoption
By honoring local languages and dialects, the app became not only more accurate, but more humane.
Building a Trustworthy Future
Bias isn’t a rare glitch in AI—it’s a foundational challenge. Tackling it starts at the source: the data. From how data is collected and labeled to how it’s shared and audited, every step in the pipeline shapes the fairness of the final model. Without careful scrutiny at these stages, bias doesn’t just creep in—it gets baked in.
To build AI that serves humanity equitably, we must shift our mindset from extractive to inclusive—from statistical representation to social justice. Because behind every data point is a human story—and it deserves to be told fairly.
