Gender Bias in Artificial Intelligence: Empowering Women Through Digital Literacy
Syed Sibghatullah Shah
Abstract
Purpose This narrative review investigates the interplay between gender bias in artificial intelligence (AI) systems and the potential of digital literacy to empower women in technology. By synthesising research from 2010 to 2024, the study examines how gender bias manifests in AI, its impact on women’s participation in technology, and the effectiveness of digital literacy initiatives in addressing these disparities. Purpose A systematic literature search was conducted across major academic databases, including Web of Science, Scopus, IEEE Xplore, and Google Scholar. The review focused on peer-reviewed articles, reports, and case studies published between 2010 and 2024 that addressed gender bias in AI, women’s participation in technology, and digital literacy initiatives. A thematic analysis framework was employed to identify and synthesise recurring themes and patterns. Purpose The findings reveal systemic gender biases embedded in AI applications across diverse domains, such as recruitment, healthcare, and financial services. These biases stem from factors including the under-representation of women in AI development teams, biased training datasets, and algorithmic design choices. Digital literacy programs emerge as a promising intervention, fostering a critical awareness of AI bias, encouraging women to pursue AI careers, and catalysing growth in women-led AI projects. Purpose Although gender bias in AI poses significant challenges, this review highlights digital literacy as a transformative tool for achieving gender equity in AI development and application. The study highlights the importance of inclusive AI design, gender-responsive education policies, and sustained research efforts to mitigate bias and promote equity.