Google knows me too well! Coping with perceived surveillance in an algorithmic profiling context

Abstract

Enabled by ubiquitous dataveillance practices, corporations try to construct accurate algorithmic profiles of their users for various purposes, such as personalized advertising. In this study, we confront users with their personal algorithmic profiles and employ a cross-sectional survey (N = 685) to investigate how perceived accuracy of algorithmic profiling relates to perceived surveillance and subsequent coping strategies. Our findings reveal that the more accurate individuals perceive their algorithmic profiles to be, the more they feel surveilled. Subsequently, they experience more privacy cynicism, are less likely to downplay the harm of dataveillance, and have stronger intentions to adjust ad settings. Furthermore, whereas individuals with lower online privacy literacy have higher privacy cynicism regardless of their level of perceived surveillance, those with higher literacy are more likely to experience privacy cynicism as they feel more surveilled. These findings suggest that subjective evaluations of algorithmic profiling can contribute to feelings of surveillance and individual coping responses.