By Rebecca Aboagyewah Oppong | Veebeckz Tech Media | Published on 25th July, 2024
In a brightly lit office in Accra, a young data scientist runs a sentiment analysis on a dataset of Ghanaian tweets. Across the continent in Lagos, a startup founder is deploying a loan approval algorithm trained on historical mobile money data. Meanwhile, in Nairobi, a police department tests facial recognition tools to enhance public security. All of these are powered by artificial intelligence. But behind every algorithm lies a deeper, often invisible question: whose values are encoded, whose voices are excluded, and who ultimately benefits?
Artificial Intelligence (AI) is often portrayed as a neutral force of progress—intelligent, efficient, and unbiased. But the truth is more complicated. AI is not magic. It is made. And what it is made of—data, assumptions, objectives—reflects the people and systems that create it. For Africa, a continent still navigating postcolonial power structures, economic inequality, and cultural diversity, the ethical concerns around AI are not just theoretical—they are immediate and deeply personal.
One of the foremost ethical concerns is bias in datasets. Machine learning systems learn from data, and data carries the fingerprints of the societies it’s collected from. Most publicly available datasets used in AI research are dominated by Western sources—faces from European populations, voices with American accents, financial patterns from middle-class economies. When these datasets are used to build systems in African contexts, the results can be not just inaccurate, but dangerous.
In facial recognition, for instance, numerous studies—including those from MIT’s Media Lab—have shown that commercial AI systems misidentify dark-skinned faces far more often than light-skinned ones. This isn’t just a bug—it’s a byproduct of exclusion. In Ghana, where biometric systems are used for elections, social protection, and security, an AI error is not a minor inconvenience—it could mean disenfranchisement, denial of services, or false accusations.
Then there’s the issue of automation and employment. AI-powered platforms are increasingly being used in hiring processes, customer service, banking, and retail. But what happens when a bank uses an AI system to filter loan applicants and that system has been trained on data that disproportionately excludes women, the unbanked, or those from certain regions? In many African countries, where labor markets are informal and traditional CVs don’t tell the whole story, rigid AI filtering systems could reinforce exclusion instead of dismantling it.
The lack of local control and transparency in AI systems is another major concern. Many of the AI tools used across Africa are developed by companies outside the continent. Their algorithms are black boxes, their decision-making criteria proprietary. Local businesses and governments use these tools without fully understanding how they work. This creates a power imbalance where decisions affecting Africans’ lives—whether about credit, healthcare, or policing—are being made by systems we neither build nor control.
There is also growing unease about surveillance and AI in governance. While AI can help detect fraud, manage traffic, and enhance security, it can also be weaponized for political control. Several African governments have acquired surveillance technologies that include AI facial recognition and internet monitoring, often with minimal public oversight. Without strong data protection laws, the potential for abuse is enormous. What begins as a tool for tracking criminals can easily become a tool for tracking activists, journalists, or ordinary citizens.
Moreover, data ownership and consent are often overlooked in the AI conversation. In many African countries, digital literacy is still growing. People may not fully understand how their personal data is being collected, stored, or used. When a farmer signs up for a weather app, is she aware that her behavioral patterns might be used to train agricultural AI tools for commercial entities? When schoolchildren use free educational platforms, who owns the learning data generated? Consent must be informed, not assumed—and in most cases, it simply isn’t.
But all is not bleak. Across the continent, a movement is growing to decolonize and democratize AI. In Nigeria, the Data Science Nigeria initiative is training local AI talent with a mission to make solutions for African problems, not just adapt Western tools. The Masakhane Project, a pan-African collective of NLP researchers, is building language models that recognize and process African languages—from Twi and Yoruba to Xhosa and Swahili. These efforts are vital. They assert that Africa is not just a passive recipient of AI but a creator, custodian, and ethical voice in its evolution.
Ghana has a unique role to play in this journey. With its robust education sector, growing startup ecosystem, and increasing policy focus on digital inclusion, the country can become a hub for ethical AI innovation. But it will require deliberate choices. Our universities must embed ethics in computer science and data science programs. Our regulators must demand transparency and accountability in AI deployment. Our innovators must think beyond profit and consider long-term social impact. And our citizens must be educated about their digital rights and empowered to ask the hard questions.
The future of AI in Africa should not be a copy-paste from the Global North. It must reflect our realities, respect our diversity, and protect our people. Ethical AI isn’t a luxury. It’s the foundation for trust, inclusion, and sustainable innovation. And the time to shape it is now.
“Algorithms are not neutral. They reflect the societies that train them. Africa must shape its own intelligence—artificial or not.”