Artificial Intelligence, Gender Bias and Sustainable Development: A Critical Analysis of Algorithmic Inequality in Emerging Economies

Abstract

Through a PRISMA-guided systematic review of seventeen studies (2015–2025), this paper identifies three mechanisms driving algorithmic inequality in emerging economies: data-driven bias from skewed datasets, algorithmic design bias prioritising accuracy over subgroup fairness, and structural feedback loops. Women face disproportionate exposure to AI-driven automation, reduced access to AI-enabled financial and health services, and exclusion from AI development. Combining intersectional feminism, critical algorithm studies and socio-technical systems theory, the paper argues purely technical fixes are insufficient and calls for inclusive data governance, fairness-aware design, regulatory oversight and meaningful participation of marginalised communities to align AI with SDG 5.