ALGORITHMIC BIAS AND FAIRNESS IN AI SYSTEMS: SOCIETAL IMPLICATIONS AND ETHICAL GOVERNANCE IN AFRICAN CONTEXTS

Abstract

Algorithmic bias has emerged as one of the most critical ethical challenges associated with artificial intelligence (AI) systems deployed in societal decision-making. While extensive scholarship has examined bias and fairness in AI within Global North contexts, empirical evidence from Africa remains limited. This study investigates the nature, sources, and societal implications of algorithmic bias in AI systems used across African public and private sectors. Employing a mixed-methods approach, quantitative survey data were collected from 398 AI practitioners, policymakers, and civil society actors across Nigeria, Ghana, and Tanzania, complemented by qualitative interviews with 25 domain experts. Secondary analysis of AI case studies in finance, recruitment, and public service delivery further informed the research. The findings reveal widespread concern about algorithmic bias, particularly in relation to data representativeness, historical inequalities, and lack of contextual calibration. Quantitative analysis shows that perceived fairness significantly predicts public trust in AI systems, while qualitative insights highlight structural and institutional contributors to bias. The study argues that prevailing technical definitions of fairness are insufficient for African contexts and advocates for ethically grounded, context-sensitive governance frameworks. By centering societal values, historical inequalities, and participatory oversight, this research contributes to advancing ethical AI governance and mitigating algorithmic harm in African societies.