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A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions

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Abstract

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 models. It highlights the necessity of diverse representation in data, fairness-aware algorithms, and interpretability as key elements in creating bias-free AI systems. By synthesizing existing research and providing a holistic overview of bias in deep learning, this paper aims to contribute to the ongoing discourse on mitigating bias and fostering equity in artificial intelligence systems. The insights presented herein can serve as a foundation for future research and as a guide for practitioners, policymakers, and stakeholders to navigate the complex landscape of bias in deep learning.

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  1. Department of Computer Science & Engineering, Krishna School of Emerging Technology & Applied Research (KSET), Drs. Kiran & Pallavi Patel Global University (KPGU), Vadodara, Gujarat, India

    Milind Shah & Nitesh Sureja

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  1. Milind Shah
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Correspondence to Milind Shah.

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Shah, M., Sureja, N. A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10134-2

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