Examining Potential Gender Bias in Automated Recruitment Systems in Canada and Its Impact on Immigrant Women

This major research paper examines the potential gender bias in automated recruitment systems, also known as applicant tracking systems (ATS), and how it impacts immigrant women in Canada. It explores the relationship, interactions, and intersections between gender and artificial intelligence from an anthropological perspective to assess the impact of this technological trend on the process of acquiring the right job roles based purely on skills, particularly for immigrant women. Additionally, it studies the social, economic, and diachronic hardships that immigrant women may experience that artificial intelligence systems may ignore when considering skills data sets. Using studies on gender, artificial intelligence, technological fetishism, data feminism, digital discrimination, and AI recruitment, this paper focuses on questions such as whether AI is biased or if the people creating it are, who produces AI, and whether AI has agency. The research addresses these questions to help reimagine AI to make it useful for all groups without discrimination. For this research, the author, an immigrant woman in Canada, relied on autoethnography to collect and analyze qualitative data of her job search on LinkedIn. The insights drawn from this have been presented in the paper.