Queering Artificial Intelligence: The Implications of Gender Bias in AI-Driven Systems

Abstract

Artificial Intelligence (AI) has become an integral part of modern society, shaping decision-making in various domains, including healthcare, finance, recruitment, and social media. However, the development and deployment of AI systems often reflect and reinforce existing social biases, particularly concerning gender. This study explores the implications of gender bias in AI-driven systems through the lens of queer theory, emphasizing how these biases contribute to the marginalization of non-binary and gender-diverse identities. While AI models are trained on vast datasets, these datasets frequently encode heteronormative and patriarchal structures, leading to discriminatory outcomes in applications such as facial recognition, natural language processing, and automated decision-making. This research investigates the systemic exclusion of queer identities in AI development, highlighting the urgent need for more inclusive datasets, ethical AI design principles, and interdisciplinary collaboration between technologists and gender scholars. By critically examining case studies of biased AI outcomes, the paper underscores the potential harm of gendered algorithmic decision-making and advocates for a queering of AI—a radical rethinking of AI design that challenges binary gender norms and fosters inclusivity. The findings suggest that integrating queer perspectives into AI ethics frameworks can mitigate gender biases and create more equitable technological landscapes. This study contributes to the broader discourse on AI ethics, advocating for structural reforms in AI governance to ensure that emerging technologies respect and uphold diverse gender identities.