Privacy Discrimination: What it is and why it matters

Date
2022-01-04
Authors
Hillebrand, Luis
Hermes, Sebastian
Böhm, Markus
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Abstract
We argue that online companies are able to exploit users’ varying levels of privacy needs. We show that by employing data analytics methods on a comparatively small amount of data it is possible to predict how high information privacy concerns of specific users are. We argue that online companies might be able to introduce “privacy discrimination”, in the sense that they might apply varying levels of privacy protection to users, based on their privacy concerns. Users indifferent about privacy could be presented with limited privacy options, adjusted terms and conditions or might be driven to disclose more personal information.
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Keywords
The Dark Sides of AI, artificial intelligence, privacy, privacy concerns, privacy discrimination
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