Proceedings of the 2nd World Conference on Gender Equality
Year: 2024
DOI:
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Examining Algorithmic Bias and Gender in Artificial Intelligence
Atefeh Namdari, and Mohammad Hossein Hashemi Asl
ABSTRACT:
Artificial intelligence (AI) is increasingly shaping various aspects of our lives. However, research suggests that AI algorithms can be riddled with gender bias, potentially harming women and exacerbating gender inequality. This study explores the two main types of gender bias in AI algorithms: statistical bias arising from skewed training data and technical bias inherent in the algorithm design. We discuss how these biases can lead to discrimination against women in employment, healthcare, and education, further amplifying existing societal inequalities. The paper then examines potential mitigation strategies, including utilizing fair and unbiased data sets, designing algorithms to minimize bias, and raising awareness about the issue. We conclude by emphasizing the need for a multifaceted approach to address gender bias in AI. This includes establishing strong regulations and ethical guidelines, fostering diversity within the field of AI development, and prioritizing fairness and inclusivity throughout the AI development process.
keywords: AI and Gender Inequality, Gender and Algorithmic Power, AI and Gender Inequality, Algorithmic Gender Bias, Feminist Technoscience