Privacy Discrimination: What it is and why it matters

dc.contributor.author Hillebrand, Luis
dc.contributor.author Hermes, Sebastian
dc.contributor.author Böhm, Markus
dc.date.accessioned 2021-12-24T18:04:35Z
dc.date.available 2021-12-24T18:04:35Z
dc.date.issued 2022-01-04
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.606
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79943
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject The Dark Sides of AI
dc.subject artificial intelligence
dc.subject privacy
dc.subject privacy concerns
dc.subject privacy discrimination
dc.title Privacy Discrimination: What it is and why it matters
dc.type.dcmi text
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