معرفی
Sharla Alegria is an Associate Professor at the University of Toronto (Downtown Toronto campus), specializing in sociological research focused on race, gender, and technology. Her work intersects colonialism, racialization, and indigeneity with computational methods and organizational studies. She examines how systemic inequalities manifest in tech workforces and AI systems, emphasizing intersectional analyses of labor markets and workplace dynamics.
Her research interests include the sociology of artificial intelligence, racialized and gendered labor in tech industries, and the sociotechnical reproduction of inequality. She critiques structural barriers in STEM fields and explores pathways to enhance diversity and inclusion in science and engineering workforces through policy and organizational interventions.
Key themes in her recent publications (2024–2016) include algorithmic bias in machine learning, gender pay gaps in federal science agencies, and the invisibility of marginalized workers in global tech supply chains. Her work bridges sociological theory with empirical analyses of contemporary technological and organizational challenges.
Despite her prolific output, no specific awards or grants are explicitly listed in the provided materials. Her research often engages with interdisciplinary methods, combining quantitative and ethnographic approaches to study systemic inequities in high-tech environments.
