Fairness in Artificial Intelligence: Understanding and Mitigating Algorithmic Bias

Artificial Intelligence (AI) systems are increasingly used in decision-making processes that significantly affect individuals and society, including areas such as employment, healthcare, finance, and education. While these technologies promise efficiency and objectivity, growing evidence shows that AI systems can produce biased and unfair outcomes. This phenomenon, known as algorithmic bias, occurs when AI systems reflect or amplify existing social inequalities due to biased data, design choices, or deployment contexts. Such bias raises serious ethical, social, and legal concerns, particularly when automated decisions impact human rights, equality, and access to opportunities. This paper examines the concept of algorithmic bias and explores the meaning of fairness in AI systems. It analyzes how bias enters the AI lifecycle, including data collection, model development, and deployment, and discusses why fairness cannot be treated as a purely technical issue. The study highlights the importance of transparency, accountability, and human oversight in mitigating bias and promoting responsible AI use. By synthesizing existing research and governance frameworks, the paper emphasizes the need for ethical and institutional approaches that ensure AI systems support social justice, public trust, and sustainable technological innovation.