Cameras in the Classroom: Facial Recognition Technology in Schools

Abstract

Facial recognition (FR) technology was long considered science fiction, but it is now part of everyday life for people all over the world. FR systems identify or verify an individual’s identity based on a digitized image alone, and are commonly used for identity verification, security, and surveillance in a variety of settings including law enforcement, commerce, and transportation. Schools have also begun to use it to track students and visitors for a range of uses, from automating attendance to school security. FR can be used to identify people in photos, videos, and in real time, and is usually framed as more efficient and accurate than other forms of identity verification. However, a growing body of evidence suggests that it will erode individual privacy and disproportionately burden people of color, women, people with disabilities, and trans and gender non-conforming people. In this report, we focus on the use of FR in schools because it is not yet widespread and because it will impact particularly vulnerable populations. We analyze FR’s implications using an analogical case comparison method. Through an iterative process, we developed historical case studies of similar technologies, and analyzed their social, economic, and political impacts, and the moral questions that they raised. This method enables us to anticipate the consequences of using FR in schools; our analysis reveals that FR will likely have five types of implications: exacerbating racism, normalizing surveillance and eroding privacy, narrowing the definition of the “acceptable” student, commodifying data, and institutionalizing inaccuracy. Because FR is automated, it will extend these effects to more students than any manual system could. On the basis of this analysis, we strongly recommend that use of FR be banned in schools. However, we have offered some recommendations for its development, deployment, and regulation if schools proceed to use the technology.