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Gathering Real-world Data in African Regions is Difficult, Expensive, and Time-consuming 

The data availability and consumption discrepancy between Africa and the rest of the world reflects more profound disparities and has significant repercussions. As a result, critical innovations in health, education, and transportation operate better for the rest of the globe than for Africa. For example, due to Africa’s relative data scarcity, AI systems in Africa have fewer data sets to learn from than the rest of the globe. Synthetic data is presented as a strategy to supplement this data scarcity. The generative adversarial networks (GANs) AI method is often used to create synthetic data. GANs consist of two neural networks (akin to an artificial brain), the generator and the discriminator. The generator produces new data, while the discriminator discriminates between real and fake data.