Acceptance of AI-powered facial recognition technology in surveillance scenarios: Role of trust, security, and privacy perceptions

Hyesun Choung, Prabu David and Tsai-Wei Ling

Technology in Society, 2024, vol. 79, issue C

Abstract: The study examines the roles of various layers of trust, as well as privacy and security concerns, in shaping the acceptance of AI-powered facial recognition technology (FRT) in three surveillance scenarios—public spaces, hospitals, and schools. Based on survey data from 575 U S. participants, we found that the context in which FRT is deployed shapes people's perceptions and acceptance of the technology. People perceived greater safety gains in schools and greater privacy risks in public spaces. Trust in officials, familiarity with FRT, and perceived security benefits positively predicted acceptance, while distrust and perceived privacy risks negatively predicted acceptance. These findings offer insights for stakeholders of FRT, policymakers, and organizations that seek to implement AI-powered surveillance, emphasizing the need to address public trust and privacy concerns.

Keywords: Artificial intelligence; Facial recognition technology (FRT); Surveillance technology; Public perceptions; Security and privacy; Trust; Contextual approach (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:79:y:2024:i:c:s0160791x24002690

DOI: 10.1016/j.techsoc.2024.102721

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