This article is adapted from a presentation I delivered on July 26, 2025, at a webinar hosted by RAI Alliance on Responsible Artificial Intelligence in Africa (Kenya). The talk was part of a broader discourse aimed at AI practitioners, policymakers, and technology leaders, exploring how AI can be designed and deployed responsibly, without reinforcing existing inequalities.
For every success story of artificial-intelligence-driven innovation in African communities, there is a risk of unintended harm. From biased hiring algorithms that sideline qualified candidates to flawed facial recognition systems that misidentify citizens, the rapid, largely unchecked deployment of AI threatens to deepen societal divides in the continent and exclude the most vulnerable. The technology’s potential for harm makes the call for “Responsible AI,” a framework built on fairness, accountability, and transparency, an urgent necessity for equitable development across the continent.
VIDEO: Leading tech companies’ commercial AI systems significantly mis-gender women and darker skinned individuals, according to Gender Shades, a research project by Joy Buolamwini and colleagues at MIT.
The problem of AI bias is particularly acute in underserved communities, where historical inequalities can be amplified by poorly designed algorithms. In one widely cited study by Joy Buolamwini and colleagues at the Massachussetts Institute of Technology, leading facial recognition systems showed error rates of up to 34.7 percent for dark-skinned women, compared to just 0.8 percent for light-skinned men. The real-world impact of such failures is profound, potentially locking individuals out of essential digital services, financial applications, and even personal devices, creating new barriers to participation in the digital economy.
This isn’t a hypothetical risk; it’s a reality playing out across the continent in various sectors.
- In Finance: AI-driven fintech platforms in key markets like Kenya and Nigeria have shown bias against rural populations and women. Because these systems are often trained on data that reflects historical lending patterns, they can inadvertently learn to penalize applicants who lack a formal credit history, thereby limiting their access to capital and reinforcing existing economic disparities.
- In Hiring: Recruitment AI, frequently trained on data from Western contexts, has been found to favor graduates from certain universities while excluding candidates from rural and ethnic minority backgrounds. This not only shrinks the talent pool for employers but also perpetuates cycles of employment inequality.
- In Communication: Automated translation tools, while useful, frequently fail to capture the nuance and cultural context of African languages like Yoruba and Swahili. This can lead to significant misrepresentation, stripping away cultural identity and, in some cases, spreading harmful misinformation.
The cost of inaction is staggering. When AI systems fail, they don’t just harm individuals; they erode public trust and undermine the promise of technology as a force for good. Global data shows that 73 percent of consumers lose trust in a brand after a single negative AI interaction. This loss of faith has tangible economic consequences, with 60 percent of C-level executives reporting that AI scandals have led to a direct reduction in investment. The global economic cost of AI bias is projected to reach a staggering $2.9 trillion by 2030.
For Africa, this means that failing to address AI ethics could jeopardize the continent’s $61-103 billion potential economic windfall from generative AI. If people do not trust the systems being deployed, widespread adoption will falter, and the promised benefits of efficiency and growth will never fully materialize.
So, who is responsible for fixing this? The answer is everyone. A successful framework for Responsible AI requires a collaborative, multi-stakeholder effort. It cannot be delegated to a single entity.
- Governments and Regulators must move beyond rhetoric to establish clear, enforceable governance frameworks and ethical guidelines, such as the African Union’s Continental AI Strategy, to protect citizen rights and privacy.
- The Private Sector and Tech Companies must embed ethics into the design process, conducting rigorous bias testing and ensuring their decision-making processes are transparent and explainable.
- Academia and Research Institutions have a duty to conduct independent ethics research, train a new generation of ethical AI practitioners, and champion the development of Africa-centric solutions that address local needs.
- Civil Society and Citizens play a crucial role as watchdogs, advocating for responsible AI, holding institutions accountable, and reporting instances of bias and harm.
The journey toward an ethical AI future is a collective one. The question isn’t whether AI will transform Africa, it’s whether we’ll shape that transformation responsibly.
For readers who wish to see the full presentation, a video can be found on my Google Drive.
About the Author
Esther Oyiyechi Abel is a 2025 Engineering for Change Fellow, a Mastercard Foundation Scholar, a leader in Artificial Intelligence in Business and a strategic program lead working toward more ethical, inclusive technology. She brings hands-on experience to the global conversation on AI as an AI Acceleration Analyst at Arizona State University (ASU), where she served on the university’s AI Acceleration Team. At ASU she contributed to the ethical evaluation, deployment, and integration of AI tools across enterprise systems within a rigorous ethical framework grounded in principles such as transparency, safety, and fairness.
Ms. Abel has founded two organizations: Opportunities Unlock, which empowers more than 5,000 African youth through free certification courses, webinars, scholarships, and digital skills training; and ShopQnB, an AI company registered in the USA.
