Redefining the Privacy Paradox: The Role of Market-Driven Interaction Rituals in Algorithmic Data Disclosure

Abstract

The privacy paradox suggests there is a discrepancy between people’s expressed concerns about privacy and their actual actions online. Traditionally, this paradox is supported most significantly by the cost-benefit analysis where the perceived benefits of certain web platforms outweigh the perceived risks to their privacy. Additionally, in sociology, the violation of privacy norms is often understood as the inappropriate flow of information across social contexts, known as the contextual integrity theory. This study aims to challenge this theory and redefine our understanding around the privacy paradox, specifically in algorithmic engagement. Through empirical and qualitative research, this thesis asks: how do individuals across generations navigate privacy concerns in the age of Artificial Intelligence? Specifically, it explores how these algorithms challenge conventional understandings around privacy by obscuring the flow of information and enforcing strong user behaviors, known as interaction rituals. By analyzing 20 interviews (10 subjects ages 18-24 years old and 10 subjects ages 47-53 years old), I work to disprove the traditional theories behind privacy behaviors. Instead, I find that social interactions, particularly market-driven interaction rituals and complex contagions, are normalizing the erosion of personal privacy in algorithms. These findings suggest that individuals interact in networks that socially reinforce constant engagement with digital technology, which makes opting out and privacy-focused behaviors increasingly difficult.