User Expectations of Facial Recognition in Schools and Universities: Mixed Methods Analysis
Authors: Aimee Roundtree
Abstract: This project explores the relationship between community perception, expectations, and experiences with privacy risk and facial recognition technology used in schools and universities. The methodology includes a meta-analysis of current literature and content analysis of social media content on the subject matter. The meta-analysis revealed that positive attitudes about facial recognition technology used in schools only reflect a portion of the total surveyed. A sentiment analysis of tweets about facial recognition technology used in schools and universities revealed that concerns skyrocketed in 2020, probably caused by the pandemic forcing courses and academic activity online, thereby heightening awareness about facial recognition technology and its implications. Tweets expressed concern about privacy, ethics, and data management. Negative emotion spiked in discussions about unrest and conflicts, possibly due to news about facial recognition used in crowd control. Concerns about power differentials spiked in conversations about how facial recognition would affect academics and education. The trends in attitudes directly pertain to current and projected problems and negative implications of facial recognition on vulnerable populations, including children, seniors, ethnic minorities, and transgender populations. The heterogeneity of the U.S. market requires sensitivity to issues of diversity, equity, and inclusion. Recommendations include operationalizing lessons learned from user experience research. Future studies should investigate trade-offs between privacy, safety, and autonomy.
Keywords: Facial Recognition Technology, Schools, Universities, Integrative Review, Meta, Analysis, Social Media
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