Date of Award
Doctor of Philosophy
Xiangming Mu, Margaret Kipp, Wonchan Choi, Shion Guha
Facial Recognition Technology (FRT) has become one of the most rapidly growing technologies. A statistical report expected that the global market size of FRT will record an increase by over 100% within five years, from $3.8 billion in 2020 to $8.5 billion in 2025 (“Facial Recognition Market Size, Share and Global Market Forecast to 2025 ,” n.d.). The proliferation of FRT is primarily related to the organizational desire to bridge integrity, credibility, and reliability vulnerabilities that are inherent in traditional identification mechanisms. The ambiguity of how information flows in this system has led to an increase in individuals' privacy concerns. Prior studies have statistically measured the volume of individuals' privacy concerns of FRT across various regions and contexts. However, the authors have failed to investigate the root of those concerns to provide a thorough framework illustrating the dimensions of FRT-related activities that breach privacy principles. This doctoral dissertation, therefore, bridged this gap by diving into user-generated text on the YouTube platform to develop a new framework of the most common FRT-related privacy concerns. The sequential exploratory mixed-method design was selected to evaluate user-generated text on 206 FRT-related YouTube videos. In the qualitative phase, user-generated text on five FRT-related videos was analyzed to explore different dimensions of FRT-related users' privacy concerns. In the quantitative phase, the supervised text classification was developed through SVM algorithms to apply the qualitative findings to a larger sample to achieve the external validity requirement. The sequential analysis of 206 video transcripts, 123301 top-level comments, and 75326 low-level comments revealed that what has motivated the users' privacy risk belief in FRT lies in nine dimensions divided into four main themes: information collection (surveillance, coercion), information processing (retention period, profiling, security, secondary use, exclusion) information dissemination (disclosure), and invasion (decisional interference). The findings should contribute to reconceptualizing privacy in the context of FRT as well as offering a comprehensive insight of current privacy laws flaws that are of interest to policymakers to enact new privacy laws or reform existing privacy laws to address organizations' abuses and protect the individuals' right to privacy in the era of FRT.
Alhumaidan, Yazeed, "A New Framework of Privacy Concerns Assessment in the Context of Facial Recognition Technology (FRT): Mixed-Methods Sequential Exploratory Analysis of YouTube Users." (2021). Theses and Dissertations. 2637.