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Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
Name
2001.02479.pdf
Description
Submitted version
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
dcd0553f52f43d71922ed421ca23684d
Author(s)
Nouwens, Midas
Liccardi, Ilaria
Veale, Michael
Karger, David R
Kagal, Lalana
Date Issued
April 2020
Journal
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Publisher
Association for Computing Machinery (ACM)
Citation
Nouwens, Midas et al. "Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence." Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, April 2020, Honolulu, Hawaii, Association for Computing Machinery, April 2020. © 2020 ACM
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1145/3313831.3376321
