Algorithmic Bias, Justice and Social Inequality: A Critical Review of AI Regulation

Abstract

Artificial Intelligence (AI) systems are increasingly embedded in decision-making processes across critical social sectors, including criminal justice, healthcare, employment, education, finance, and public administration. While these technologies promise efficiency, consistency, and scalability, growing evidence indicates that algorithmic systems can reproduce and amplify existing social inequalities through biased data, opaque decision-making mechanisms, and unequal distributions of technological benefits and harms. This critical review examines the interrelationship between algorithmic bias, justice, and social inequality, with particular emphasis on contemporary approaches to AI regulation. Drawing upon interdisciplinary literature from law, sociology, computer science, ethics, and public policy, the study synthesizes current debates surrounding the origins, manifestations, and consequences of algorithmic discrimination. The review identifies multiple sources of bias, including historical data inequities, flawed model design, inadequate representation of marginalized populations, and institutional power asymmetries that become embedded within automated systems. The study further analyzes how algorithmic decisions disproportionately affect vulnerable groups, exacerbating disparities related to race, gender, socioeconomic status, disability, and geographic location. Additionally, the review critically evaluates emerging regulatory frameworks developed by governments and international organizations, highlighting their strengths, limitations, and implementation challenges. Particular attention is given to principles of transparency, accountability, fairness, explainability, human oversight, and participatory governance as foundational pillars of responsible AI governance. The findings reveal that existing regulatory approaches often struggle to keep pace with rapid technological advancements and frequently prioritize innovation over social justice considerations. Furthermore, fragmented global regulatory landscapes create inconsistencies that hinder effective oversight and enforcement. The study argues that addressing algorithmic injustice requires moving beyond purely technical solutions toward comprehensive socio-legal frameworks that incorporate ethical accountability, inclusive policymaking, and multidisciplinary collaboration. Ultimately, this review contributes to ongoing scholarly and policy discussions by proposing a justice-centered approach to AI regulation that prioritizes human rights, democratic values, and equitable technological development. Such an approach is essential for ensuring that AI systems serve as instruments of social progress rather than mechanisms that perpetuate or deepen existing inequalities.