[en] Being asked to consent to data sharing is a ubiquitous experience in digital services - yet it is very rare to encounter a well designed consent experience. Considering the momentum and importance
of a European data space where personal information freely and easily flows across organizations, sectors and Member States, solving the long-discussed thorny issue of "how to get consent right"
cannot be postponed any further. In this paper, we describe the first findings from a study based on 24 semi-structured interviews investigating participants’ expectations and opinions toward consent in a data sharing scenario with a data trustee. We analyzed various dimensions of a consent form redesigned as a comic and an infographic, including language, information design, content and the writer-reader relationship. The results provide insights into the complexity of elements that should be considered when asking individuals to freely and mindfully disclose their data, especially sensitive information.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > IRiSC - Socio-Technical Cybersecurity
Disciplines :
Sciences informatiques
Auteur, co-auteur :
DOAN, Xengie Cheng ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
Selzer, Annika
ROSSI, Arianna ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
BOTES, Wilhelmina Maria ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
LENZINI, Gabriele ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Context, Prioritization, and Unexpectedness: Factors Influencing User Attitudes About Infographic and Comic Consent
Date de publication/diffusion :
26 avril 2022
Nom de la manifestation :
COnSeNT 2022 ~ 2nd International Workshop on Consent Management in Online Services, Networks and Things co-located with 31st WEB Conference
European Union’s Hori zon 2020 Innovative Training Networks Grant Agreement ID:956562 (Legality Attentive Data Scientists) and is partially supported by the Luxembourg National Research Fund (FNR) – IS/14717072 “De ceptive Patterns Online (Decepticon)”. Author’s 2 contribution has been partially funded by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE.
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