• Super Admin
• 06 Mar, 2026

Sira Dibbassey examines how women are often overlooked and systematically excluded from AI development and governance in Africa. She argues that closing these gaps does not depend on new strategies, but on using existing ones more deliberately. Artificial intelligence (AI) in Africa could contribute up to $1.5 trillion to Africa's economy by 2030, with a growing impact across sectors such as agriculture, healthcare, finance, and public services. Yet persistent infrastructural deficits, limited sustainable investments, skills gaps, and widening digital divides hinder this potential. Equally critical but often overlooked is the systematic exclusion and underrepresentation of women in AI development and governance. Without meaningful participation of women in the digital and AI economy, the African continent risks undermining innovation, reinforcing bias in AI systems, and falling short of the inclusive growth envisioned under Agenda 2063. African women are excluded from the AI ecosystem at multiple structural levels, from education and funding opportunities to governance. This creates a cycle of marginalisation that limits their participation and benefit. Statistics of 2024 show that only 31% of African women used the internet, compared to 43% of men. This connectivity gap limits women's ability to access digital tools, build technical skills, and pursue opportunities in the digital economy. Although women make up 47% of STEM graduates, the highest globally, they represent only 23-30% of the workforce in tech. Barriers within the technology sector continue to shape their participation, including gender stereotypes that frame technology as a 'masculine field' and promote workplace bias. As a result, many women struggle to build the digital experience and AI-related skills needed to access jobs and progress within the sector, putting them at a disadvantage. These factors limit women's contribution in AI research and labour markets despite a strong presence in STEM education. For the few women who manage to build tech companies, reports show that female tech firms still receive minimal early-stage investments. Lack of capital limits women-led tech companies from scaling ideas, pushing them toward smaller, less resilient ventures, thereby hindering their influence on innovation and control over future market platforms and intellectual property. AI researchers have also found that AI development often overlooks women's lived realities, creating design gaps that turn into real-world exclusion. Such biased AI systems are increasingly used in decision-making that affects life opportunities, from job screening, to credit assessment systems that determine access to loans and public services. Africa is beginning to see many of these biased AI systems being rolled out. For example, an audit of 10 credit-scoring algorithms in Nigeria, Kenya, and South Africa revealed that women-led SMEs faced a 37% underfunding penalty compared to equivalent male-led firms. By excluding women from acquiring AI skills and using AI, Africa places itself under economic constraints, limiting talent and innovation. If women are not digitally skilled, they will miss out on future opportunities. For instance, in sectors like agriculture, where women constitute 60% of the rural agriculture workforce, AI's benefits are often captured by men. By ignoring the realities of women who drive food production, these systems scale inefficiency rather than progress, weakening the entire sector's growth. Beyond visible economic losses, AI systems that ignore women's realities perform poorly as development tools. Once these gaps are built in, they grow, resulting in fewer women in the AI economy and weaker overall AI-driven development outcomes. This raises a crucial question for policymakers and development partners: how can AI-driven growth be pursued when half the population is structurally excluded from its benefits and key decision-making spaces? The African Union adopted a 10-year Strategy for Gender Equality and Women's Empowerment to advance Agenda 2063 through stronger gender-responsive laws and promoting women's economic empowerment, including in digital development. However, implementation remains siloed, as gender mainstreaming is binding across sectors and lacks sufficient institutional capacity and resources. UN Women and the AU Commission report that gender integration across AU institutions is hindered by limited staff training, insufficient financing, and weak gender data and monitoring systems. Other AU policies like the Digital Transformation Strategy, Continental AI Strategy and Data Policy Framework promote inclusive growth but do not adequately embed gender equality into their operational design. While they acknowledge gender risks, they lack binding measures such as mandatory gender impact assessments, representative data standards, or gender-responsive monitoring indicators, thereby assuming that gender inclusion will follow naturally from AI governance. Despite gender equality commitments by the AU, the AfCFTA Digital Trade Protocol is largely gender-neutral. By prioritising scale and interoperability, it overlooks structural constraints for women-owned MSMEs, such as limited access to finance, high transaction costs, and the digital divide that hinders their participation, risking the deepening of existing inequalities. Furthermore, national AI and digital policies often lack consistency and capacity, mentioning gender without integrating it into implementation. Studies in countries like Egypt, Rwanda and Mauritius show that advanced national AI strategies rarely manifest continental gender commitments into tangible action. African countries and their development partners should pay particular attention to: Source: https://ecdpm.org/work/why-gender-inclusive-ai-matters-africa