The rapid advancement of machine learning and artificial intelligence (AI) has led to transformative changes across various domains that involve Computer Vision, including healthcare, finance, and autonomous systems. Despite their potential, these technologies raise significant concerns related to privacy, fairness, transparency, reliability, and ethics, especially when dealing with biased data. In recent years, the computer vision community has shown an increasing interest in model debiasing, with strategies designed to identify and mitigate the dependency of deep neural networks on shortcuts corresponding to bias. This survey aims to bring order to this field by providing a comprehensive review of model debiasing methods, with a particular focus on their application in Computer Vision. After defining model bias and discussing its implications, we describe the typical evaluation benchmarks, serving as a foundation for understanding the complexities involved in this critical area of research. We further delve into debiasing methods, providing a categorization while discussing their effectiveness and limitations. Besides, contemporary research trends are explored, among which the growing interest in explainable AI techniques and the ethical implications of biased models are predominant. Throughout this survey, we highlight milestones and open challenges in model debiasing, in an attempt to provide a reference for both experts and researchers interested in this context.
Ciranni et al. (Tue,) studied this question.