Date of Award
Honors Thesis (Open Access)
Colby College. Science, Technology and Society Program
The goal of this thesis is to present the current status and awareness of facial recognition technology and their use as part of video surveillance systems. Specifically, I intend to help readers develop a greater understanding of how facial recognition systems contain algorithms that perpetuate bias in their matching and recognition of faces. Current research demonstrates that algorithms differentially recognize faces from different races and genders. As a technology with substantive impacts for use and abuse, more scrutiny of facial recognition technology is necessary. This paper will also help readers understand the dangers of facial recognition as a biometric technology and how biometric data and privacy are large topics of discussion that affect individuals across the globe as society continues through the Information Age. This paper utilizes different critical lenses to address the issues and implications of facial recognition, including sociological and legal approaches in analyzing issues of algorithmic bias. Through the analysis of legal cases regarding the use of facial recognition, data on current algorithms used, and implications for privacy and surveillance, I present a critique of the technology is presented along with suggestions for its future uses.
facial recognition, algorithms, algorithmic bias, race, biometrics, privacy, surveillance
Recommended CitationVenditti, Lydia F.; Fleming, Jim; and Kugelmeyer, Kara, "Algorithmic Surveillance: A Hidden Danger in Recognizing Faces" (2019). Honors Theses. Paper 932.