Abstract

Artificial Intelligence (AI) has become one of the most transformative technologies of the twenty-first century influencing decision-making processes in sectors such as healthcare education employment finance criminal justice and public administration. While AI promises efficiency accuracy and innovation growing concerns have emerged regarding algorithmic bias and its contribution to social inequality. Algorithmic bias occurs when AI systems produce unfair discriminatory or prejudiced outcomes due to biased training data flawed design choices or unequal social structures embedded within technological systems. Such biases often disproportionately affect marginalized communities based on race gender socioeconomic status ethnicity and other social identities. The increasing dependence on automated decision-making systems raises critical ethical and social questions regarding fairness accountability transparency and inclusiveness. This study explores the relationship between algorithmic bi