Justice-Centered Pathways to Combat Surveillance Realism in Informal CS Learning

Abstract

Teenagers today are coming of age under unprecedented, pervasive networks of institutional mass surveillance technologies. Surveillance technologies spreading in youth spaces are impacting young people’s prospects today and are expected to continue in the future. Additionally, today’s teenagers will be tomorrow’s decision-makers, tasked with managing and shaping future surveillance systems. Concerns that young people are being normalized to surveillance culture carry serious political implications for just and democratic futures with technology. How do teenagers view these systems? How can educators, researchers, and fellow learners support them to develop their own perspectives and sense of agency with these technologies? This dissertation examines how teenagers learn about surveillance technologies and opportunities for supporting their learning in line with justice-centered goals. Specifically, teenagers’ views on institutional privacy and surveillance through an interview study, and learning about a specific institutional surveillance technology in Drag vs AI workshops through observational data analyses. Drag vs AI is a drag queen-led workshop created by the Algorithmic Justice League which engages teenagers in makeup use to subvert AI-powered facial recognition technologies (FRTs). We organized and studied four workshop enactments with two community partners, Out Boulder County and the Longmont Public Library. Article I, Expectations vs reality: teenager views of institutional privacy, reveals how teenagers’ complex views of institutional surveillance coexist with attitudes of surveillance realism, identifying implications for justice-centered surveillance education. The second article, Taking Play and Tinkering Seriously in AI Education: Cases from Drag vs AI Teen Workshops, examines tinkering and play as modes of interaction with FRTs in Drag vs AI workshops, evidencing an alternative learning methodology termed embodied algorithmic tinkering. Finally, Drag Pedagogy for CS Educational Work: Reflections from Drag vs AI Workshops, explores how drag pedagogical practices embodied in the design and facilitation of workshops supported playful and critical CS learning around FRTs.

In my concluding chapter, I reflect on the relationship between privacy and criticism of surveillance, and examine how the limitations identified in the first study emerged in studies of Drag vs AI using a historical action analysis, synthesizing actionable takeaways for practitioners and possible directions for future designs and research.