#529: Modeling Discourse Shifts in Critical Machine Learning: How Black Girls Reimagine Surveillance through Design

This study examines how the discourse of surveillance shifted among middle school Black girls through their participation in a Critical Machine Learning (CML) project grounded in sociocultural learning theories and Critical Race Technology Theory. Using a Quantitative Ethnography research design, this study collected data through students' interaction in their groups over two consecutive sessions within a twelve-session program implementation. Ordered network analysis (ONA) reveals that students initially positioned surveillance in a binary category, either for care or control, with a weak connection between the two. Eventually, students began to position care as a primary purpose of surveillance and justified why care was important. These findings underscore the importance of integrating critical STEM education in schools, providing students with opportunities to engage and question the technologies they design.