Intersectional analysis of the barriers faced by university women accessing and using ai in the workplace

Abstract

The purpose of this study was to analyze the barriers faced by university women in accessing and using Artificial Intelligence (AI) in the workplace in Argentina, Chile, and Mexico from an intersectional perspective. We explored the complex interactions between gender, ethnicity, social class, and geographical location in shaping these barriers. The study's design and methodology followed an explanatory sequential mixed-methods approach. We collected data in the quantitative phase using an online survey on a sample of 812 university women working in three countries. In the qualitative phase, semi-structured interviews and focus groups were conducted with a subsample of participants. The findings revealed various barriers to accessing and using AI, such as lack of knowledge and skills, gender stereotypes, digital divides, and challenges in work-family balance. We also identified significant differences based on ethnicity and type of work. The qualitative analysis highlighted discrimination, lack of support, and mentoring, as well as the intersection of inequalities. The practical implications of this study underscore the importance of considering intersectionality when addressing the barriers faced by university women when interacting with AI. The findings have implications for the design of policies and programs that promote gender equity in AI and the workplace, taking into account the diversity of women's experiences.