Artificial Intelligence (AI) is now deeply embedded in sectors such as healthcare, banking, education, recruitment and public administration. As AI systems take on more decision-making responsibilities, concerns about fairness and discrimination have become increasingly important. This research explores the issue of algorithmic bias, a phenomenon in which AI models produce unequal or unfair outcomes due to imbalanced datasets, flawed model architecture or socio-technical limitations. The primary purpose of this study is to understand how algorithmic bias emerges, how it affects individuals and communities, and what strategies can be adopted to promote fairness in AI systems. The study uses a qualitative approach supported by case studies, research papers, and real-world examples to analyze bias in areas such as facial recognition, hiring algorithms, credit scoring and predictive policing. It also examines existing fairness frameworks and responsible AI guidelines proposed by global organizations. The findings indicate that technical solutions alone such as data balancing, algorithm auditing or explainable AI cannot fully eliminate bias without strong ethical oversight and inclusive decision-making. The research highlights the importance of transparency, accountability, diverse datasets and multidisciplinary collaboration to ensure equitable AI deployment. The study concludes that creating fair AI requires more than improving algorithms; it requires building systems that reflect societal values of justice and equality. Sustainable fairness in AI depends on continuous monitoring, community participation, and updated policy frameworks that protect human rights in an increasingly automated world.
Ansar et al. (2026) studied this question.