AI and Gender Biases: A Critical study of Ken Liu’s The Perfect Match
Published Date: 10-01-2026 Issue: Vol. 3 No. 1 (2026): January 2026 Published Paper PDF: Download
Abstract: Gender studies analyses social, political, economic and power relations which work to achieve women empowerment. It focuses on creating a framework to construct policies by which the goal of equality and sustainable development for all genders can be achieved. Recent studies have showcased the increasing dominance of gendered artificial intelligence (AI) in our daily lives. In this digital world, the use of artificial intelligence has become part and partial of life because of its ability to increase efficiency and advanced data analysis. Rather, we can say that AI has touched every aspect of our personal and professional life. When we consider the use of artificial intelligence in the field of gender studies, we find that AI has a dual relationship with gender equality. It can advance gender equality as well as hinder it also. Through the use of AI women entrepreneurs are empowered by tools such as microfinance and information resources. At the same time, AI eternalizes existing societal biases. The lack of women in AI development roles leads towards the gender biases and stereotypes. In the field of literature, Ken Liu, an American author of science fiction and fantasy in his shortstory, The Perfect Match,first published in Light Speed Magazine, 2012 and collected in The Paper Menagerie and Other Stories, has explored the themes of gender bias in artificial intelligence. This story tells about co-dependence on AI and its impact on human decisions and relationships. But, any relation based on machine and data can create only sterile connection, which moves the protagonist, Sai towards rebellion against the AI. With the help of this short- story, the present paper will try to focus on exploring the facts which explain gender biases in artificial intelligence and promote gender inequality in society.
Keywords: Empowerment, Sustainable Development, Efficiency, Microfinance, Gender biases.