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Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence

Nouwens, M; Liccardi, I; Veale, M; Karger, D; Kagal, L; (2020) Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence. In: CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. (pp. p. 194). ACM Green open access

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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.

Type: Proceedings paper
Title: Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
Event: ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2020)
Location: Honolulu, HI
Dates: 24 April 2020 - 30 April 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3313831.3376321
Publisher version: https://doi.org/10.1145/3313831.3376321
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Notice and Consent; Dark patterns; Consent Management Platforms; GDPR; Web scraper; Controlled experiment
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Laws
URI: https://discovery.ucl.ac.uk/id/eprint/10088400
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