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

Year: 2024

Type: article

Abstract: Enabled by ubiquitous dataveillance practices, corporations try to construct accurate algorithmic profiles about their users for 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, individuals who perceive their algorithmic profiles to be more accurate also tend to cope by thinking of the benefits of dataveillance (self-empowering) and taking the corporations’ perspectives (sympathizing), whereas these relationships are not explained by perceived surveillance. These findings suggest that subjective evaluations of algorithmic profiling can contribute to feelings of surveillance and individual coping responses.

Source: Computers in Human Behavior

DOI: 10.1016/j.chb.2024.108536

Authors: Dong Zhang, Joanna Strycharz, Sophie C. Boerman, Theo Araujo, Hilde Voorveld

Institutions: University of Amsterdam, Wageningen University & Research

Language: English

Topic: Privacy, Security, and Data Protection

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,820

Funders: Universiteit van Amsterdam, Amsterdam School of Communication Research, University of Amsterdam