Dark Patterns: Past, Present, and Future

Narayanan, Arvind; Mathur, Arunesh; Chetty, Marshini; Kshirsagar, Mihir

doi:10.1145/3400899.3400901

Cited by 89 publications

(46 citation statements)

References 14 publications

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“…Some common dark patterns are (1) nagging: when an interface interrupts user workfow consistently and asks for a certain action from them, (2) preselection: values set to defaults that are in the best interest of the provider prior to user interaction, (3) aesthetic manipulation: graphical elements used to deceive the user into taking an action that may be in favour of service provider rather than the user, (4) forced action: users are given only one option to follow even if that is not what they prefer to do, (5) toying with emotion: elements, colours, or language to provoke user's emotion to get user make a decision that is in favour of the service provider, and (6) false hierarchy: options that are in the best interest of the service provider are in higher positions [17]. As such patterns become prevalent in the digital world [5,6,10,12,13,17,27,31,32,51], we are keen to explore the presence of them in tools and libraries that developers use, specifcally, in ad networks.…”

Section: Introductionmentioning

confidence: 99%

“Developers Are Responsible”: What Ad Networks Tell Developers About Privacy

Tahaei

Vaniea

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Advertising networks enable developers to create revenue, but using them potentially impacts user privacy and requires developers to make legal decisions. To understand what privacy information ad networks give developers, we did a walkthrough of four popular ad network guidance pages with a senior Android developer by looking at the privacy-related information presented to developers. We found that information is focused on complying with legal regulations, and puts the responsibility for such decisions on the developer. Also, sample code and settings often have privacyunfriendly defaults laced with dark patterns to nudge developers' decisions towards privacy-unfriendly options such as sharing sensitive data to increase revenue. We conclude by discussing future research around empowering developers and minimising the negative impacts of dark patterns. CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy; • Software and its engineering → Software creation and management; • Information systems → Online advertising.

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Section: Introductionmentioning

confidence: 99%

“Developers Are Responsible”: What Ad Networks Tell Developers About Privacy

Tahaei

Vaniea

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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“…Or consider retail websites that nudge shoppers into making quicker (and thus less deliberative) decisions by displaying countdown timers or misleading stock reports, like "Only 1 left!" Dark patterns emerged, Narayanan et al argue, from the integration of nudge research with long-standing deceptive retail practices, and their adoption has been driven by the technology industry's imperative to achieve market growth at all costs [66]. Although there is only limited empirical research on dark patterns, existing findings are illuminating.…”

Section: Darkmentioning

confidence: 99%

Measuring Automated Influence: Between Empirical Evidence and Ethical Values

Susser

Grimaldi

2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

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Automated influence, delivered by digital targeting technologies such as targeted advertising, digital nudges, and recommender systems, has attracted significant interest from both empirical researchers, on one hand, and critical scholars and policymakers on the other. In this paper, we argue for closer integration of these efforts. Critical scholars and policymakers, who focus primarily on the social, ethical, and political effects of these technologies, need empirical evidence to substantiate and motivate their concerns. However, existing empirical research investigating the effectiveness of these technologies (or lack thereof), neglects other morally relevant effects-which can be felt regardless of whether or not the technologies "work" in the sense of fulfilling the promises of their designers. Drawing from the ethics and policy literature, we enumerate a range of questions begging for empirical analysis-the outline of a research agenda bridging these fields-and issue a call to action for more empirical research that takes these urgent ethics and policy questions as their starting point. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; • Human-centered computing → HCI design and evaluation methods.

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“…Regardless of the adoption of privacy preference signals, there is little basis to trust that expressed preferences will be respected. In terms of what we can observe: vendors ignoring the DNT signal was public policy [4], P3P was intentionally misconfigured by websites [1], TCF consent signals misreport the user's expressed preferences [8], tracking remains ubiquitous in a post-GDPR world [78] and there is growing evidence firms use dark patterns to manipulate users' expressed preferences [94][95][96]. More fundamentally, there is no way of auditing whether AdTech vendors respect expressed signals.…”

Section: Futurementioning

confidence: 99%

Privacy Preference Signals: Past, Present and Future

Hils

Woods

Böhme

2021

Proceedings on Privacy Enhancing Technologies

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Privacy preference signals are digital representations of how users want their personal data to be processed. Such signals must be adopted by both the sender (users) and intended recipients (data processors). Adoption represents a coordination problem that remains unsolved despite efforts dating back to the 1990s. Browsers implemented standards like the Platform for Privacy Preferences (P3P) and Do Not Track (DNT), but vendors profiting from personal data faced few incentives to receive and respect the expressed wishes of data subjects. In the wake of recent privacy laws, a coalition of AdTech firms published the Transparency and Consent Framework (TCF), which defines an optin consent signal. This paper integrates post-GDPR developments into the wider history of privacy preference signals. Our main contribution is a high-frequency longitudinal study describing how TCF signal gained dominance as of February 2021. We explore which factors correlate with adoption at the website level. Both the number of third parties on a website and the presence of Google Ads are associated with higher adoption of TCF. Further, we show that vendors acted as early adopters of TCF 2.0 and provide two case-studies describing how Consent Management Providers shifted existing customers to TCF 2.0. We sketch ways forward for a pro-privacy signal.

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Dark Patterns: Past, Present, and Future

Cited by 89 publications

References 14 publications

“Developers Are Responsible”: What Ad Networks Tell Developers About Privacy

“Developers Are Responsible”: What Ad Networks Tell Developers About Privacy

Measuring Automated Influence: Between Empirical Evidence and Ethical Values

Privacy Preference Signals: Past, Present and Future

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