Dark Patterns in the Interaction with Cookie Banners

Hausner, Philip; Gertz, Michael

doi:10.48550/arxiv.2103.14956

Cited by 5 publications

(9 citation statements)

References 4 publications

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“…Dark patterns are tricky because "It is rarely possible to foresee which new patterns are going to emerge, and as a result, detection measures are always reactive, and rely on practitioners that constantly update the existing pattern databases as well as engaged consumers that point out new occurrences". (Hausner and Gertz, 2021). Thus, guidelines may need to be continuously updated and limited to a particular context of use.…”

Section: Discussionmentioning

confidence: 99%

“…Text, compared to image, is harder to automatically scrap from online applications, particularly if the interface interaction invokes other interfaces. Cookie banners are observed to employ legal and technical jargon (Strycharz et al, 2021;, vague and ambiguous language (Santos et al, 2017), and positive or negative framing (Hausner and Gertz, 2021) which hampers automatic detection of textual expressions. An open question is whether the text of the interface is sufficient to identify a dark pattern.…”

Section: Representation Challengesmentioning

confidence: 99%

“…used semiautomatic methods and only made the content of the cookie banner notifications available to the users via a script and human labour is used to identify four GPDR violations, as depicted in Table 7: consent was stored before the user made the choice,whether a cookie banner offers a way to opt out, whether there were pre-selected choices, if the choice that the user had made was respected at all. Hausner and Gertz (2021) presented ongoing work in the direction of automatic detection of dark patterns on cookie banners. They use the feature representation for cookie banners and automatically extract the feature values.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way

Soe¹,

Santos²,

Slavkovik³

2022

Preprint

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Cookie banners, the pop ups that appear to collect your consent for data collection, are a tempting ground for dark patterns. Dark patterns are design elements that are used to influence the user's choice towards an option that is not in their interest. The use of dark patterns renders consent elicitation meaningless and voids the attempts to improve a fair collection and use of data. Can machine learning be used to automatically detect the presence of dark patterns in cookie banners? In this work, a dataset of cookie banners of 300 news websites was used to train a prediction model that does exactly that. The machine learning pipeline we used includes feature engineering, parameter search, training a Gradient Boosted Tree classifier and evaluation. The accuracy of the trained model is promising, but allows a lot of room for improvement. We provide an in-depth analysis of the interdisciplinary challenges that automated dark pattern detection poses to artificial intelligence. The dataset and all the code created using machine learning is available at the url to repository removed for review.

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Section: Discussionmentioning

confidence: 99%

Section: Representation Challengesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way

Soe¹,

Santos²,

Slavkovik³

2022

Preprint

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“…Indeed, it has been shown that users tend to ignore privacy-related notices [22,32,54]. Considering GDPR Privacy Banners, users tend to accept privacy policies when offered a default button via intrusive banners that nudge users [18,27], which is often the case [33] with websites offering large pop-ups or wall-style banners that cover most of the webpage as seen in Figure 1.…”

Section: Related Work and Toolsmentioning

confidence: 99%

“…We aim at a practical and effective approach to accept privacy policies through the offered button. As previously said, most users will indeed be nudged in this direction, being the opt-out options often made cumbersome on purpose [18,27,33].…”

Section: Priv-accept Design and Testingmentioning

confidence: 99%

The Internet with Privacy Policies: Measuring The Web Upon Consent

Jha¹,

Trevisan²,

Vassio³

et al. 2021

Preprint

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To protect users' privacy, legislators have regulated the usage of tracking technologies, mandating the acquisition of users' consent before collecting data. Consequently, websites started showing more and more consent management modules -i.e., Privacy Banners -the visitors have to interact with to access the website content. They challenge the automatic collection of Web measurements, primarily to monitor the extensiveness of tracking technologies but also to measure Web performance in the wild. Privacy Banners in fact limit crawlers from observing the actual website content.In this paper, we present a thorough measurement campaign focusing on popular websites in Europe and the US, visiting both landing and internal pages from different countries around the world. We engineer Priv-Accept, a Web crawler able to accept the privacy policies, as most users would do in practice. This let us compare how webpages change before and after. Our results show that all measurements performed not dealing with the Privacy Banners offer a very biased and partial view of the Web. After accepting the privacy policies, we observe an increase of up to 70 trackers, which in turn slows down the webpage load time by a factor of 2x-3x.

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Cookiescanner: An Automated Tool for Detecting and Evaluating GDPR Consent Notices on Websites

Gundelach,

Herrmann

2023

Proceedings of the 18th International Conference on Availability, Reliability and Security

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The enforcement of the GDPR led to the widespread adoption of consent notices, colloquially known as cookie banners. Studies have shown that many website operators do not comply with the law and track users prior to any interaction with the consent notice, or attempt to trick users into giving consent through dark patterns. Previous research has relied on manually curated filter lists or automated detection methods limited to a subset of websites, making research on GDPR compliance of consent notices tedious or limited. We present cookiescanner, an automated scanning tool that detects and extracts consent notices via various methods and checks if they offer a decline option or use color diversion. We evaluated cookiescanner on a random sample of the top 10,000 websites listed by Tranco. We found that manually curated filter lists have the highest precision but recall fewer consent notices than our keyword-based methods. Our BERT model achieves high precision for English notices, which is in line with previous work, but suffers from low recall due to insufficient candidate extraction. While the automated detection of decline options proved to be challenging due to the dynamic nature of many sites, detecting instances of different colors of the buttons was successful in most cases. Besides systematically evaluating our various detection techniques, we have manually annotated 1,000 websites to provide a ground-truth baseline, which has not existed previously. Furthermore, we release our code and the annotated dataset in the interest of reproducibility and repeatability. CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy; • General and reference → Measurement; • Information systems → World Wide Web.

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Dark Patterns in the Interaction with Cookie Banners

Cited by 5 publications

References 4 publications

Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way

Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way

The Internet with Privacy Policies: Measuring The Web Upon Consent

Cookiescanner: An Automated Tool for Detecting and Evaluating GDPR Consent Notices on Websites

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