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How Artificial Intelligence is Transforming Africa’s Digital Landscape-and the Hidden Risks of Gender Bias
Artificial intelligence (AI) is revolutionizing access to financial services, education, healthcare, and digital content across Africa. While the narrative often highlights innovation and increased accessibility, a subtler challenge lurks beneath: the risk that AI systems may entrench existing social inequalities, particularly gender disparities, into the continent’s digital future.
The Economic Promise and Pitfalls of AI in Africa
AI is rapidly emerging as a key driver of economic growth in Africa. From bustling tech hubs in Lagos to Nairobi, venture capital is pouring into startups aiming to bypass traditional infrastructure constraints. Machine learning algorithms are now underwriting loans for previously unbanked populations, enabling remote medical diagnoses, and streamlining supply chains. The underlying belief is that data-driven technologies can unlock vast, underserved markets and accelerate development.
However, this promising engine is fueled by incomplete and biased data, which threatens to undermine its potential. In many African countries, the digital footprints that train AI models predominantly come from men. According to the GSMA’s 2024 report, women are approximately 15% less likely than men to own smartphones or engage with mobile money services, resulting in their underrepresentation in datasets that inform AI decision-making.
Gender Bias Embedded in AI: A Barrier to Inclusive Growth
This gender imbalance in data is not just a social justice issue-it represents a significant economic inefficiency. If AI-driven tools fail to accurately recognize or serve half the population, billions of dollars in potential economic activity remain untapped. This problem is especially pronounced in fintech, a sector often hailed as Africa’s digital crown jewel. Traditional banks have historically excluded low-income women due to collateral requirements, and fintechs sought to address this by using alternative data-such as mobile airtime purchases, geolocation, and app usage-to assess creditworthiness.
Yet, these algorithms often replicate past exclusions. Because AI models learn from historical data where women were marginalized, they tend to classify female borrowers as higher risk by default. Moreover, proxies used by AI can inadvertently disadvantage women. For example, if frequent travel is interpreted as a sign of economic activity, women who face mobility restrictions due to safety concerns or caregiving responsibilities may be unfairly penalized. Similarly, if time spent online is used as a measure of digital literacy, women burdened with unpaid labor may be undervalued.
The Financial Cost of Overlooking Women Entrepreneurs
Such biases carry substantial financial consequences. The International Finance Corporation (IFC) estimates a staggering $1.7 trillion annual financing gap for women-owned small and medium enterprises (SMEs) in emerging markets. These businesses are not inherently unviable; rather, they remain invisible to traditional and AI-driven financial systems alike.
Beyond Finance: Gender Data Gaps in Agriculture and Technology Development
The issue extends beyond fintech. In agriculture, AI-powered tools designed to optimize crop yields often rely on data collected from male heads of households, despite women contributing approximately 40% of crop production in sub-Saharan Africa, according to the World Bank. If agri-tech solutions fail to account for the specific crops women cultivate or the smaller plots they manage, these innovations risk limited adoption and impact.
Compounding these challenges is the underrepresentation of women in Africa’s tech workforce. UNESCO reports that women constitute only about 30% of professionals in the continent’s technology sector, with even fewer in specialized roles such as AI engineering and data science. This lack of diversity in the creators of AI systems perpetuates blind spots and biases in the technology itself.
Moving from Gender Neutrality to Gender Equity in AI Governance
For investors and policymakers shaping Africa’s digital future, simply aiming for gender neutrality is insufficient. Neutral approaches applied to unequal datasets often accelerate existing disparities. Addressing this requires proactive governance and regulatory frameworks tailored to Africa’s unique data environment, rather than importing models from the Global North.
The African Union’s Digital Transformation Strategy rightly emphasizes closing the digital gender divide as essential for sustainable growth. However, this vision must be translated into enforceable policies and compliance mechanisms. For instance, if a credit scoring algorithm systematically denies loans to creditworthy women at higher rates than men, it is not just biased-it is a flawed product that misprices risk and limits market potential.
A Crucial Juncture: Building an Inclusive Digital Future for Africa
Africa stands at a pivotal moment in its economic development. The continent has the opportunity to construct digital infrastructures that correct historical inequities rather than perpetuate them. By designing AI systems that inclusively represent and serve all demographics, Africa can unlock the full economic potential of its 1.4 billion people. The alternative is allowing flawed algorithms to shrink markets and reinforce exclusion before the digital economy fully matures.
Mission is an economist and researcher specializing in literacy, gender, development, and digital innovation. She examines how state and non-state actors influence recovery in conflict-affected regions and how economies respond to factors like oil markets, exchange rates, and policy shifts. Currently, she leads Mibe Consulting, transforming ideas into funded initiatives.
