Algorithmic Bias and Social Stratification: How AI Shapes Inequality in Digital Societies

Abstract

This paper examines the complex relationship between algorithmic bias and social stratification in contemporary digital societies. As artificial intelligence systems increasingly mediate access to employment, credit, healthcare, and education, their embedded biases risk perpetuating and amplifying existing social inequalities. Through analysis of case studies across multiple domains, this research demonstrates how algorithmic decision-making systems can systematically disadvantage marginalized groups, creating new forms of digital stratification. The paper proposes a framework for understanding algorithmic bias as both a technical and social phenomenon, arguing that addressing these issues requires interdisciplinary approaches combining technical solutions with policy interventions and social awareness. Findings suggest that without deliberate intervention, AI systems may entrench existing power structures while creating novel forms of discrimination that are harder to detect and challenge.