The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
Open accessAbstract
New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet our minimal requirements based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22-23 percentage points; and providing more granular controls on the first page decreases consent by 8-20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance.
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A behavior model for persuasive design
Fogg, B. (2009) · Proceedings of the 4th International Conference on Persuasive Technology · doi:10.1145/1541948.1541999 · Cited by 2,328
Privacy and rationality in individual decision making
Acquisti, A., & Grossklags, J. (2005) · IEEE Security and Privacy Magazine · doi:10.1109/msp.2005.22 · Cited by 1,123
A Contextual Approach to Privacy Online
Nissenbaum, H. (2011) · Daedalus · doi:10.1162/daed_a_00113 · Cited by 649
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Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2020) · Psychological Science in the Public Interest · doi:10.1177/1529100620946707 · Cited by 479
How behavioural sciences can promote truth, autonomy and democratic discourse online
Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0889-7 · Cited by 223
Gray, C. M., Santos, C. T., Bielova, N., & Mildner, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642436 · Cited by 121
Yes: Affirmative Consent as a Theoretical Framework for Understanding and Imagining Social Platforms
Im, J., Dimond, J., Berton, M., Lee, U., Mustelier, K., Ackerman, M. S., & Gilbert, E. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445778 · Cited by 119
Fosch-Villaronga, E., van der Hof, S., Lutz, C., & Tamò-Larrieux, A. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01295-w · Cited by 54
“Specifically, companies have used “dark patterns” or misleading designs to obfuscate information retrieval (Gray et al. 2018; Nouwens et al. 2020).”
BackgroundAsthana, S., Im, J., Chen, Z., & Banovic, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642180 · Cited by 34
Metadata
- Authors
- Nouwens, M., Liccardi, I., Veale, M., Karger, D., & Kagal, L.
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W3000190958
- Cited by
- 494
- References
- 3
- FWCI
- 146.45
- Type
- conference paper
- Language
- en
- Open access
- green
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