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AI systems, especially those used in healthcare, are often described as "black boxes." This term refers to the fact that we can see the input and the output—but what happens in between remains largely invisible. At Veebeckz Tech Hub, we wanted to change that.
In our investigation into AI use in Ghana's healthcare sector, we explored tools from the field of Explainable AI (XAI) to demystify these complex algorithms. Our goal was simple: make it possible for non-technical stakeholders—healthcare workers, patients, journalists—to understand why an AI model made a particular prediction.
We used techniques like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) to break down model predictions into understandable components. For example, if an AI tool predicted a high TB risk for a patient, we could show whether that was due to age, coughing duration, prior exposure, or other inputs.
This clarity had two benefits. First, it allowed healthcare providers to trust—or challenge—the AI's decision based on real-world knowledge. Second, it opened a conversation about accountability. If an AI consistently made errors for women over 50 in rural districts, that pattern became visible and actionable.
Explainable AI isn't just for researchers. It is an essential tool for transparency and equity in African digital health. By making algorithms visible, we bring power back to the people.