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This comprehensive review and analysis delve into the intricate facets of bias within the realm of deep learning. As artificial intelligence and machine learning technologies become increasingly integrated into our lives, understanding and mitigating bias in these systems is of paramount importance. This paper scrutinizes the multifaceted nature of bias, encompassing data bias, algorithmic bias, and societal bias, and explores the interconnectedness among these dimensions. Through an exploration of existing literature and recent advancements in the field, this paper offers a critical assessment of various bias mitigation techniques. It examines the challenges faced in addressing bias and emphasizes the need for an intersectional and inclusive approach to effectively rectify disparities. Furthermore, this review underscores the importance of ethical considerations in the development and deployment of deep learning …