Cookie Disclaimers: Impact of Design and Users’ Attitude

Berens, Benjamin; Dietmann, Heike; Krisam, Chiara; Kulyk, Oksana; Volkamer, Melanie

doi:10.1145/3538969.3539008

Cited by 9 publications

(7 citation statements)

References 21 publications

(36 reference statements)

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“…They reason that due to their ubiquitous nature and frequent exposure, users have become accustomed to this type of dark pattern. Berens et al [1] confirmed the findings of Graßl et al regarding color highlighting and additionally discovered that styling the decline option as a link instead of a button had a strong effect.…”

Section: The Efficacy Of Dark Patterns In Consent Noticessupporting

confidence: 61%

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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Section: The Efficacy Of Dark Patterns In Consent Noticessupporting

confidence: 61%

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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“…This practice should therefore be avoided as a practice by data controllers [28]. Berens et al also confirmed this finding, showing in their study that the phrasing for accepting or rejecting cookies can influence users' behaviours [6].…”

Section: Deceptive Design Beyond User Interfacesmentioning

confidence: 69%

“It doesn’t tell me anything about how my data is used”: User Perceptions of Data Collection Purposes

Kyi,

Mhaidli,

Santos

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Data collection purposes and their descriptions are presented on almost all privacy notices under the GDPR, yet there is a lack of research focusing on how effective they are at informing users about data practices. We fill this gap by investigating users' perceptions of data collection purposes and their descriptions, a crucial aspect of informed consent. We conducted 23 semi-structured interviews with European users to investigate user perceptions of six common purposes (Strictly Necessary, Statistics and Analytics, Performance and Functionality, Marketing and Advertising, Personalized Advertising, and Personalized Content) and identified elements of an effective purpose name and description.We found that most purpose descriptions do not contain the information users wish to know, and that participants preferred some purpose names over others due to their perceived transparency or ease of understanding. Based on these findings, we suggest how the framing of purposes can be improved toward meaningful informed consent. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; • Security and privacy → Usability in security and privacy; • Applied computing → Law.

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“…In interface design, cues that commonly occur include identity cues (e.g., giving a virtual agent a name or face), non-verbal cues (e.g., affective speech, gendered voices or pausing as if thinking), and verbal cues (e.g., humanlike mannerisms in responses) [29]. 5 Such cues help to make the system appear more like an intentional 'agent' speaking to the user, rather than a medium for people to communicate with each other [25], strengthening social/emotional responses.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This may include treating computers as if they have folk-psychological states (e.g., beliefs, intentions, or desires), or reacting to moving pictures on a screen as if the events were taking place in real life [49]. 5 Other (top-down) cues include personified descriptions of AI systems: beyond the countless AI personifications that populate the media (including the term AI itself), CUIs like social robots are often explicitly marketed as "friends" and "your next family member" that "can't wait to meet you" [23, p.60].…”

Section: Background and Related Workmentioning

confidence: 99%

“…In considering these ambiguities, we will use the term 'dark patterns' in a broad sense, regardless of intention, to refer to design patterns that involve inducing certain emotions (e.g., guilt, fear, shame) or misleading mental models (e.g., a misconception of the system or their actions) to manipulate user behaviour towards certain ends-involving mechanisms or goals that the user is unaware of or did not consent to. 6 As noted earlier, the vast majority of literature on dark patterns has focused on how information is phrased or presented in graphic user interfaces (GUIs), most prominently cookie consent banners [5,6,19], ads on online platforms [30,66], and e-commerce websites [44,47,58]. Recently, a handful of papers have started considering dark patterns in the ways that other modalities interact with people, i.e., embodied interactions with robots [33,57], auditory interactions with voice user interfaces (VUIs) [52], and proxemic interactions with movable platforms [28].…”

Section: Distinguishing Nudges Dark and Anti-patternsmentioning

confidence: 99%

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Computers as Bad Social Actors: Dark Patterns and Anti-Patterns in Interfaces that Act Socially

Alberts¹,

Kleek²

2023

Preprint

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Fig. 1. Mock examples of notification messages with manipulative, rude, or tactless framing The Computers Are Social Actors paradigm suggests people exhibit social/anthropomorphic biases in their treatment of technology.Such insights have encouraged the design of interfaces that interact with users in more social (chatty or even friend-like) ways.However, in typical 'dark pattern' fashion, social-emotional responses to systems as (seemingly sentient) agents can be harnessed to manipulate user behaviour. An increasingly common example is app notifications that assume person-like tones to persuade or pressure users into compliance. Regardless of being manipulative, difficulties meeting contextual social expectations can make automated social acting seem rude, invasive, tactless, and even disrespectful-constituting 'social' anti-patterns. This paper explores ways to improve how automated systems treat people in interactions. We mixed four qualitative methods to elicit user experiences and preferences regarding how automated systems "talk" to/at them. We identify an emerging 'social' class of dark and anti-patterns, and propose guidelines for helping (social) interfaces treat users in more respectful, tactful, and autonomy-supportive ways.CCS Concepts: • Human-centered computing → Natural language interfaces; Empirical studies in interaction design; User centered design; Contextual design.

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Cookie Disclaimers: Impact of Design and Users’ Attitude

Cited by 9 publications

References 21 publications

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

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

“It doesn’t tell me anything about how my data is used”: User Perceptions of Data Collection Purposes

Computers as Bad Social Actors: Dark Patterns and Anti-Patterns in Interfaces that Act Socially

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