Biometric Bordering and Automatic Gender Recognition: Challenging Binary Gender Norms in Everyday Biometric Technologies

Abstract

Abstract With the rise of advanced biometric technologies, the surveilling of populations who do not match racial and gender norms has increased. Modern-day biometrics make assumptions about gender and race based on skin color, facial structure, body type, and body parts, which are encoded in predictive algorithms and other AI-driven systems. Growing empirical evidence points to the obstacles this poses for trans and non-binary individuals in several spheres, including border security, healthcare, and social media. Drawing on autoethnographic vignettes, semi-structured interviews, and survey responses, we look to the increased use of binary-based biometric technologies and automatic gender recognition (AGR), which rely on outmoded understandings of gender as static, measurable, and physiological. Our ethnographic data demonstrate how trans and non-binary bodies are forced to bend to these systems; meanwhile these technologies and algorithms increasingly extract data on trans and non-binary users, which may then be used as challenge sets to refine their accuracy.