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Data Quality Limiting AI’s Effectiveness in Africa

AI can help improve trade logistics by making delivery systems, customs processes, and tracking and tracing more efficient. It can find the best ways to move goods, predict delays, and give traders real-time information. In addition, AI can analyse trade data to identify patterns, trends, and connections. This can help policymakers make better choices and predict and respond to future trends better. AI-powered systems can make cross-border e-commerce more efficient, a sector expected to grow under the AfCFTA. AI can create personalized product recommendations and automate customer service. In education and training, AI can help people acquire the skills they need for an African economy that works better together. However, for AI to achieve the goals of the AfCFTA, it is essential to have good AI governance and regulation. Furthermore, we need to invest in digital infrastructure and education.

But the data availability and consumption discrepancy between Africa and the rest of the world reflects more profound disparities and has significant repercussions for Africa. 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 the continent’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 2 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. This process helps create AI solutions on the continent, it is far from ideal. Regulations such as those discussed in at the session on data transfers – see above – can further reduce available data needed to create effective AI.

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Alastair Tempest

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