December 2023
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People’s opinions and actions in everyday life are increasingly influenced by artificial intelligence. However, representation in the design of these technologies has the potential to undo decades of progress in gender and ethnicity. These biases threaten the strides toward equality in these areas, casting a shadow over our progress. The concerns surrounding gender and ethnicity biases have pervaded numerous fields, none more prominently than within artificial intelligence, especially in pre-trained deep learning models. These models, celebrated for their capacity to extract knowledge from extensive datasets, hold immense potential to revolutionize society and decision-making. However, they are not impervious to the biases embedded in the data upon which they are trained. It raises the possibility of unintentionally perpetuating and amplifying societal biases linked to gender or ethnicity. The issue of gender bias in deep learning models has gained significant traction in recent times. As these models have become increasingly ubiquitous across various applications, it has become evident that they often perpetuate and exacerbate long-standing gender biases inherent in the training data. This paper embarks on a methodical and empirically rigorous exploration, delving into the nuanced landscape of gender and ethnicity bias within a diverse array of pre-trained deep learning models. Through meticulous scrutiny of these models’ performance about gender and ethnicity-based predictions, we aim to unearth invaluable insights regarding the presence, intricacies, and magnitude of bias.This research paper offers a comprehensive and empirically grounded examination of gender and ethnicity bias within a diverse range of pre-trained deep-learning models. This investigation involves a meticulous analysis of how these models perform when making predictions related to gender and ethnicity. By scrutinizing their predictions, the aim is to unearth valuable insights into the presence, nuances, and extent of these biases within AI systems. Furthermore, this work introduces an innovative, holistic solution to mitigate gender and ethnicity bias. We present CNN models strategically crafted to address and rectify biases about gender and ethnicity effectively. This model represents a pioneering step towards combating bias on multiple fronts within AI systems. This research thus contributes significantly to the broader understanding of bias within AI technologies. Simultaneously addressing gender and ethnicity bias and proposing a practical remedy and the way for more equitable and unbiased advancements in artificial intelligence. Through rigorous analysis and innovative solutions, we seek to ensure that AI systems respect and uphold the principles of fairness, inclusively, and diversity, thereby fostering a more just technological landscape for all.

Computer Science