Artificial Intelligence is rapidly scaling opportunity — but without intentional guardrails, it risks widening existing gender gaps in access, skills, agency, and economic power.
Executive Summary
Artificial Intelligence is reshaping labor markets, public services, and information systems — and with it, the contours of the gender digital divide. According to UN Women and the ITU, women remain 20% less likely than men to access mobile internet, with the largest gaps in South Asia and Sub-Saharan Africa. As AI-driven services become embedded in financial systems, health diagnostics, agriculture extension, and public administration, unequal access risks compounding structural exclusions.
Evidence from the World Bank’s World Development Report 2024, UNESCO’s Science Report, and the OECD AI Observatory shows two reinforcing dynamics:
- Skills inequality — women are underrepresented in STEM fields, advanced digital skills, and AI research pipelines.
- Bias inheritance — algorithms trained on historically skewed data (e.g., labor markets, credit scoring, hiring) risk amplifying discriminatory patterns.
But when gender-responsive design is applied, AI can close gaps: the ITU’s Women in Tech Initiatives, UNDP’s digital public infrastructure pilots, and AfDB’s digital entrepreneurship programs show strong gains in financial inclusion, employment matching, and safety-net delivery.
Useful institutional repositories:
- UN iLibrary (https://www.un-ilibrary.org/)
- World Bank Open Knowledge Repository (https://openknowledge.worldbank.org/)
- AfDB Knowledge Publications (https://www.afdb.org/en/knowledge/publications)
Think About It This Way
The gender digital divide is not simply about devices — it’s about power. AI accelerates whatever social patterns it meets. If those patterns are unequal, AI becomes a multiplier of inequality; if systems are intentionally inclusive, AI becomes a multiplier of possibility.
Implications (What This Means in Practice)
- Bias in → bias out
AI systems inherit the structure of the data they’re trained on. In countries with gender-segmented labor markets, algorithms risk institutionalizing discrimination in hiring, lending, or public service targeting. - Skills stratification compounds market exclusion
Where girls have weaker STEM pathways or limited access to tertiary digital training, AI-driven labor markets push women into lower-wage, lower-growth occupations — reinforcing long-term income inequality. - Access gaps become governance gaps
Women with limited connectivity face exclusion from AI-enabled public services (ID systems, digital social protection, e-health), shifting inequality from markets to state–citizen relations. - Platformization can reinforce informal precarity
AI-mediated gig work offers entry points but often without protections; women in the informal economy may be more exposed to algorithmic management without bargaining power. - Localization matters more than ever
AI tools trained on Global North data often misfire in multilingual, low-connectivity, rural, or informal contexts — particularly affecting women whose economic lives are underrepresented in data systems. - Safety and agency shape adoption
Online harassment, surveillance, and digital safety risks remain major deterrents for women; without trust, even well-designed AI systems go unused.
Further Reading
| Report / Study | What It Covers / Why Useful | Official Link |
|---|---|---|
| WDR 2024: Digital Technologies and Development (World Bank) | Strong evidence on how digital/AI shifts labor markets & public services; includes gender analysis. | World Bank OKR |
| Progress on the Sustainable Development Goals: Gender Snapshot (UN Women, 2023) | Data on global gender disparities in digital access & skills. | UN iLibrary |
| OECD AI Observatory – Gender & AI | Comparative analysis of women’s participation in AI and risks of algorithmic bias. | OECD.AI |
| UNESCO Science Report (2021/2023) | Tracks women in STEM, research systems, and AI innovation pipelines. | UNESCO Publications |
| AfDB Jobs for Youth & Digital Economy Reports | African context on digital entrepreneurship, inclusion, and gendered employment impacts. | AfDB Knowledge |
Explore With VoD
Here are prompts to dig deeper:
“Explain how political economy factors shape gender-responsive AI design.”
“Break down how AI affects women differently in low-income vs. middle-income countries.”
“Show me a systems map of AI, skills, and gender inequality.”
“What questions should a government ask before deploying AI in social protection?”
About the Author
