Algorithms Need Context: Africa’s Wake-Up Call On AI Sovereignty

At the bustling Africa Tech Festival in Cape Town, amidst product launches and policy declarations, one voice stood out not for its volume, but for its urgency. Phil Anderson, Sales Manager for Digital Business Solutions at Datacentrix, took the stage with a presentation that felt less like a keynote and more like a call to arms. Titled “Algorithms Need Context,” his message was disarmingly simple but deeply consequential: Africa’s future with AI hinges on whether it can inject its own reality, its languages, markets, systems, and values, into the models that are already shaping the world.

Anderson began by grounding the audience in a truth too often overlooked in AI conversations: the systems being built in Silicon Valley and Shenzhen know very little about Africa. From the informal markets that dominate trade in townships and villages to the fragmented supply chains that define local commerce, the daily rhythm of African life is missing from the data that feeds most AI engines. “We buy from places that aren’t logged in any global inventory system. None of those actions are linked. So how can an algorithm trained on Western retail patterns know anything useful about our behavior?” he asked.

It wasn’t just rhetorical. Anderson backed his message with a demonstration. At one point, he cued up a commercial voice model, asking it to generate accents. British? Perfect. American? Spot on. African? Silence. “It’s not a glitch,” he told the crowd. “It’s the result of deliberate design choices. The companies building these models have said it themselves: ‘We don’t have the data. We know the bias is there.’”

This isn’t just about speech synthesis. Anderson connected the absence of African context in AI models to real-world consequences: poor credit assessments, irrelevant search results, language exclusions in customer support, and opaque decision-making in public services. When algorithms don’t understand a population’s context, they default to assumptions that often discriminate or exclude.

But this wasn’t a lament. It was a rallying cry. Anderson called for Africa to create its own infrastructure, not just data centers and cloud storage, but a Pan-African protocol layer designed to feed contextual intelligence into the models that will run future services. He argued for federated systems that respect national sovereignty while allowing collaboration across borders. “We need a system that predicts for Africa, in Africa, using African data,” he said.

Anderson’s reflections stemmed from over two years of engagement with governments, startups, and civil society across the continent. He described a shared frustration that Africa is moving too slowly to shape the digital rails it needs. Conversations abound, but execution lags. The reasons vary: lack of funding, regulatory inertia, or simply not knowing where to begin. Still, the momentum is building.

Perhaps the most powerful moment came when Anderson turned the lens on himself. “What is my contribution going to be?” he asked, pausing. “What’s yours?” The question lingered in the air. It wasn’t directed at CEOs or ministers; it was for everyone in the room. Whether developer, policymaker, or student, the implication was clear: everyone has a role to play in giving Africa’s algorithms the context they need to serve their people fairly and effectively.

He encouraged participants to pick something, anything, and start building—a dataset in a local language, a procurement model that reflects African logistics, a healthcare decision tool based on regional disease patterns. “Even if you just fix one thing, that’s how we begin,” he said.

As the presentation drew to a close, Anderson offered a final note of gratitude and hope. “To everyone helping lead this AI revolution in Africa, the builders, the entrepreneurs, the doers, thank you. Our destiny is ours to claim. We rise.”

It wasn’t a product pitch. It wasn’t a branding exercise. It was a reminder that in a world where data drives decisions, being invisible in the data means being left behind. Phil Anderson’s message was clear: the algorithms of tomorrow must carry Africa’s voice not just because it’s fair, but because it’s necessary. And that voice begins with context.

CIO Africa Reporting from Cape Town