By Zanele Dlamini, Senior International Correspondent at Phoenix24
Johannesburg, July 2025 —
In a township outside Nairobi, a facial recognition system installed to “prevent crime” consistently misidentifies dark-skinned teenagers as suspects. In Dakar, an AI-driven recruitment platform filters out candidates based on linguistic patterns associated with local dialects. In Lusaka, an algorithm used to assign healthcare priorities overlooks pregnant women in informal settlements due to incomplete datasets.
None of these systems were created in Africa. Yet their consequences are felt most acutely here.
Artificial intelligence, hailed globally as the engine of innovation and development, has arrived on the continent with promises of optimization and progress. But beneath that sheen lies a quiet danger—one coded into the architecture of these systems: racial and cultural bias embedded in their design, data, and deployment.
The problem is not that African countries are adopting AI too slowly. It’s that they are importing it uncritically, often under the guise of “development assistance” or tech philanthropy. These tools, designed and trained on datasets that reflect the demographic and social realities of the Global North, are now being deployed in contexts they were never intended to serve—where skin tones, names, languages, and sociocultural dynamics differ radically from the data that feeds the algorithm.
This is not a glitch. It is a form of algorithmic violence.
According to a 2024 report by The Alan Turing Institute and Data for Black Lives, less than 2% of global AI research funding is directed toward understanding racial equity in African contexts. Most African nations lack domestic regulatory frameworks robust enough to interrogate or constrain imported algorithms, leaving entire populations exposed to opaque decision-making systems. In fields like policing, hiring, credit scoring, and border control, these systems often reinforce colonial patterns of classification, exclusion, and control—only now, cloaked in technical neutrality.
I have reported from cities where facial recognition programs were installed without public consent. In Johannesburg, community leaders told me how an “urban security upgrade” led to increased surveillance of young Black men while white commercial areas remained unmonitored. In Accra, women entrepreneurs explained how AI-powered microloan apps penalized them for sharing phones with family members, a common practice in rural areas, but one the algorithm interpreted as fraud risk.
Who designs the datasets? Who trains the models? Whose reality is encoded, and whose is erased?
The racial bias of AI systems is not accidental—it reflects deeper inequities in the global distribution of technological power. When African people are only data points, not developers, the system is rigged from the start. As Joy Buolamwini’s groundbreaking research on facial recognition bias revealed, dark-skinned women are the most misclassified demographic group in commercial AI models. If this holds true in Boston, imagine the error margins in Kinshasa.
The urgency now is not simply to “decolonize AI”, but to reimagine its purpose altogether. African countries must move from being passive recipients of imported innovation to active authors of technological futures grounded in equity, inclusion, and contextual intelligence.
We need local datasets, built with informed consent and ethical oversight. We need AI literacy embedded in public education. We need regional coalitions that challenge the asymmetry of global tech governance—from Addis Ababa to Geneva. Most of all, we need to reclaim the narrative: AI is not neutral, and progress that leaves the most vulnerable behind is not progress—it is a digital echo of structural injustice.
And if the current trajectory continues, Africa risks becoming a testing ground for second-hand technologies with first-order consequences. But if disruption emerges—from local AI labs, grassroots data justice movements, or a new wave of African technologists—this story can shift. In a bifurcated future, we will either build systems that recognize our humanity or continue to train machines that inherit our blind spots.
Because when the algorithm cannot see you, it will not serve you. And when the code discriminates, it is not artificial intelligence—it is engineered exclusion.
Zanele Dlamini, South African senior correspondent at Phoenix24, covering African affairs, rights, and digital justice.