Algorithmic Inequality in African Digital Labour Markets:The Economic Effects of AI-Based Selection on African Gig Workers

Abstract

The rapid diffusion of digital labour platforms across Sub-Saharan Africa has been accompanied by the growing use of artificial intelligence (AI) systems to select, rank, and allocate work among gig workers. While these systems are often promoted as neutral, efficiency-enhancing tools, emerging evidence suggests that algorithmic selection can reproduce and even amplify existing structural inequalities relating to gender, geography, digital access, and informal-economy disadvantage (Wood et al., 2019; Anwar & Graham, 2020). This paper examines the economic effects of AI-based selection mechanisms on gig workers in four African labour markets: Nigeria, Kenya, Ghana, and South Africa. Using a mixed-methods design combining a structured survey of 612 platform workers with 42 semistructured interviews, the study investigates how algorithmic rating, allocation, and deactivation systems shape worker earnings, task access, and job security. Findings indicate that workers positioned in the highest algorithmic rating tiers earn substantiallymore than those in lower tiers that female workers receive a disproportionately smaller share of high-value task allocations, and that data costs and infrastructural constraints independently depress algorithmically mediated earnings. The paper argues that algorithmic inequality in African gig work is not simply a technical artefact but a socioeconomic outcome shaped by pre-existing labour market disadvantage. It concludes with policy recommendations for algorithmic transparency, worker data rights, and regulatory oversight suited to African institutional contexts.