(untitled) (Journal of responsible AI & ethics, 2026)
Artificial Intelligence (AI) algorithms are increasingly being deployed across various sectors, including healthcare, finance, criminal justice, and hiring processes. However, as AI systems become more integrated into society, a growing ethical concern has emerged regarding the bias inherent in these algorithms. AI systems are designed to make decisions based on data; however, if the data used to train these systems are biased, the resulting decisions will reflect and perpetuate these biases. This paper explores the origins and types of bias in AI algorithms, focusing on how biases are introduced during data collection, feature selection, and algorithmic design. It examines the consequences of these biases, particularly in sensitive areas like discriminatory practices, social justice, and economic disparities. The paper also delves into the ethical implications of biased AI decisions and provides recommendations for addressing these concerns through improved transparency, diverse data collection, and the development of ethical AI frameworks. By understanding the sources and impacts of AI bias, the paper aims to contribute to the ongoing conversation about responsible AI use and the need for fairness and accountability in AI decision-making systems.