APQ - APPON PHILOSOPHICAL QUARTERLY

Artificial intelligence (AI) systems are influencing human lives but with greater potential to perpetuate or challenge gender stereotypes as a pressing concern. The ontological ambivalence of AI gender stereotyping refers to the inherent contradictions and tensions within these AI systems, which can both reflect and shape societal norms. Despite the growing recognition of Al's impact on gender equality, the ontological ambivalence of AI gender stereotyping remains understudied. This study aims to critically examine the ontological ambivalence of AI gender stereotyping and its implications for individuals and society. The fundamental research question is: "How do AI systems perpetuate gender stereotypes, and what are the implications for individuals and society?" To achieve this aim, the study analyse existing research on AI, gender stereotyping, and ontological ambivalence and examines AI-generated content and algorithms to identify patterns and contradictions in gender stereotyping. The findings suggest that AI systems exhibit ontological ambivalence, reflecting both traditional and progressive values, and that these systems can perpetuate and challenge gender stereotypes in complex ways. It concluded that understanding the ontological ambivalence of AI gender stereotyping is important for developing effective strategies for challenging and subverting limiting gender norms by developers of AI systems. The study recommended that developers prioritize fairness, accountability, and transparency in AI systems, and that policymakers implement regulations to prevent AI- generated gender stereotypes from perpetuating harm. Keywords: Artificial Intelligence, Gender Stereotyping, Ontological Ambivalence, Societal Norms, Fairness, Accountability.

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