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x1Gray et al. (2022)Aagaard, J., Knudsen, M. E. C., Bækgaard, P., & Doherty, K. (2022). A Game of Dark Patterns: Designing Healthy, Highly-Engaging Mobile Games. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–8. https://doi.org/10.1145/3491101.3519837A Game of Dark Patterns: Designing Healthy, Highly-Engaging Mobile Games2022https://doi.org/10.1145/3491101.3519837Gaming is a more accessible, engaging and popular past-time than ever before. Recent research highlights games as strikingly effective means of capturing and holding our attention ‚Äî so effective, some argue, to the point of deleterious effect. An impassioned CHI2021 panel discussion directed these efforts towards the ethics and adoption of dark patterns. And yet, we know little as to how dark patterns are perceived and arise in the design, development and use of games. This paper seeks to address this knowledge gap by recounting findings from a design-led inquiry comprising interviews and workshops conducted with mobile game players, designers, developers, and business developers. We contribute an understanding of how dark patterns arise in the development, use and commercialisation of mobile games, their effects on players and industry professionals, and means for the consideration, negotiation and navigation of these strategies for gamer-engagement by design ‚Äî in support of healthier, highly-engaging game experiences.CoreFramework/TaxonomyApp Dark Design (ADD) Framework for Considering Dark Design Aspects in Free-To-Play AppsCategories of DPs gathered from practitioners, "theory" from previous research, and a user study with preteen-aged girls: Temporal, Monetary, Social, Disguised Ads, Sneaky Ads, InappropriateDan Fitton and Janet C. Read. 2019. Creating a Framework to Support the Critical Consideration of Dark Design Aspects in Free-to-Play Apps. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (IDC '19). Association for Computing Machinery, New York, NY, USA, 407–418. https://doi.org/10.1145/3311927.3323136https://dl.acm.org/doi/10.1145/3311927.3323136
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x2Gray et al. (2022)Avolicino, S., Di Gregorio, M., Palomba, F., Romano, M., Sebillo, M., & Vitiello, G. (2022). AI-Based Emotion Recognition to Study Users’ Perception of Dark Patterns. HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction, 185–203. https://doi.org/10.1007/978-3-031-17615-9_13AI-based emotion recognition to study users' perception of dark patterns2022https://doi.org/10.1007/978-3-031-17615-9_13Dark Patterns are design patterns used to trick users into acting against their real interest. The web provides an infinite number of services ac- cessible to anyone, which do not always promote a good user experience and are often structured with the aim of leading the user to perform unwanted ac- tions or discourage him from making decisions that could damage the company. This is a very common practice, especially in neuromarketing. Human behav- ioral and perceptual patterns are cleverly exploited to achieve a specific goal. For this reason, dark pattern developers try to create an environment that invites as much purchase as possible by stimulating the customer's unconscious. Among the areas in which these strategies are adopted is tourism: online travel agency websites promote "fake discounts" for the products/services they are selling, display inaccurate pricing information leading to incorrect pricing as- sumptions, thus misleading consumers. One of the goals of this work is to identify which dark patterns are most used in online travel agencies and once identified, they will be used to run scenarios that will simulate booking a vacation online. During the execution of the tests, users will be filmed via webcam tracking their expressions and emotions through AI-based facial recognition. Finally, the data obtained from the tests will be analyzed to study the emotions and feelings that a user feels when he/she is confronted with dark patterns, to understand which users are more at risk and which are the types of dark patterns to which they are more vulnerable.
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✅3Gray et al. (2022)Baroni, L. A., Puska, A. A., de Castro Salgado, L. C., & Pereira, R. (2021). Dark Patterns: Towards a Socio-technical Approach. Proceedings of the XX Brazilian Symposium on Human Factors in Computing Systems, 1–7. https://doi.org/10.1145/3472301.3484336Dark Patterns: Towards a Socio-Technical Approach2021https://doi.org/10.1145/3472301.3484336The term Dark Pattern has been used to define design patterns intentionally created to deceive users or favor the interests of parties other than users. Dark Patterns present socio-technical characteristics as something that happens when humans interact with technical artifacts in a given environment, causing annoyance, frustration, anger, and other emotions in users. Comprehending Dark Patterns require us to look at them from different perspectives, in a multifocal way, considering social and technical aspects in an intertwined way. In this paper, we apply the Semiotic Framework to analyze and discuss Dark Patterns from a socio-technical perspective. We use a popular Dark Pattern called Roach Motel to exemplify our discussion, unraveling it with the Semiotic Framework. Results show the complexity of the topic and exemplify the potential of a socio-technical approach to understand, characterize and advance our knowledge on it.CoreFrameworkSemiotic p. 3: "considers both the Technical Platform and the Human Information Functions involved in the informational process ... The framework comprises six layers: Social World, Pragmatics, Semantics, Syntactics, Empirics, and the Physical World, representing specific parts of the informational process between humans and technical artifacts"Ronald Stamper. 1993. A semiotic theory of information and information systems. In Invited papers for the ICL/University of Newcastle Seminar on Informationhttps://research.utwente.nl/en/publications/a-semiotic-theory-of-information-and-information-systemsCursoryMechanismFraming Effectp. 3: "the tendency users have of drawing different conclu-sions from the same information depending on how that information is presented. In dark patterns, information may be presented in a way that reduces users’ autonomy of choice"Amos Tversky and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice. science 211, 4481 (1981), 453–458https://www.science.org/doi/10.1126/science.7455683Christine Utz, Martin Degeling, Sascha Fahl, Florian Schaub, and Thorsten Holz. 2019. (un) informed consent: Studying gdpr consent notices in the field. In Proceedings of the 2019 acm sigsac conference on computer and communications security. 973–990.https://dl.acm.org/doi/10.1145/3319535.3354212
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✅4Gray et al. (2022)Berens, B. M., Dietmann, H., Krisam, C., Kulyk, O., & Volkamer, M. (2022). Cookie Disclaimers: Impact of Design and Users’ Attitude. Proceedings of the 17th International Conference on Availability, Reliability and Security, 1–20. https://doi.org/10.1145/3538969.3539008Cookie Disclaimers: Impact of Design and Users' Attitude2022https://doi.org/10.1145/3538969.3539008Dark patterns in cookie disclaimers are factors that are used to lead users to accept more cookies than needed and more than they are aware of. The contributions of this paper are (1) evaluating the efficacy of several of these factors while measuring actual behavior; (2) identifying users‚Äô attitude towards cookie disclaimers including how they decide which cookies to accept or reject. We show that different visual representation of the reject/accept option have a significant impact on users‚Äô decision. We also found that the labeling of the reject option has a significant impact. In addition, we confirm previous research regarding biasing text (which has no significant impact on users‚Äô decision). Our results on users‚Äô attitude towards cookie disclaimers indicate that for several user groups the design of the disclaimer only plays a secondary role when it comes to decision making. We provide recommendations on how to improve the situation for the different user groups.CoreTheoryNudgep. 2: "using specific patterns to increase the likelihood of a specific behavior [22], such as getting people to stop smoking or to save water due to environmental concerns"Richard H Thaler and Cass R Sunstein. 2008. Nudge: improving decisions about health. Wealth, and Happiness 6 (2008), 14–38
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x5Gray et al. (2022)Bergesen, A., Gullaksen, J., Hanssen, M., & Karlsson, A. (2021). Dark patterns in cookie consent notices -Norway’s 50 most visited websites. EReMCIS 2021, the Fifth Student Symposium on Empirical Research Methods in Computer Sciences and Information Systems https://folk.idi.ntnu.no/baf/eremcis/2021/Group07.pdfDark patterns in cookie consent notices: Norway's 50 most visited websites2021https://folk.idi.ntnu.no/baf/eremcis/2021/Group07.pdfCookie consent notices are widely spread among websites in the EU after the General Data Protection Regulation (GDPR) was implemented in 2018. Personal data handling affects and is relevant for every citizen of the EU, and is largely shaped by the way different websites conduct their data privacy control.Norwegian sites are subject to these, yet limited literature is presenting Norwegian websites’ implementation of the different requirements. Existing literature on international websites’ practice shows that the use of dark patterns in design is present in the manner they display cookie consents. With the use of quantitative data gathering based on surveys and quantitative analysis of the data, the team has identified the widespread achievement of the GDPR requirements and uncovered certain types of dark patterns found in Norway’s top 50 visited websites. 48% of the websites reviewed did not allow users to manage their cookie preference, or made it more difficult to reject cookies, being in favor of certain companies’ profit, but not of the user’s privacy.No theory but theory
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✅6Gray et al. (2022)Bermejo Fernandez, C., Chatzopoulos, D., Papadopoulos, D., & Hui, P. (2021). This Website Uses Nudging: MTurk Workers’ Behaviour on Cookie Consent Notices. Proc. ACM Hum.-Comput. Interact., 5(CSCW2), 1–22. https://doi.org/10.1145/3476087This Website Uses Nudging: MTurk Workers' Behaviour on Cookie Consent Notices2021https://doi.org/10.1145/3476087Data protection regulatory policies, such as the European Union's General Data Protection Regulation (GDPR), force website operators to request users' consent before collecting any personal information revealed through their web browsing. Website operators, motivated by the potential value of the collected personal data, employ various methods when designing consent notices (e.g., dark patterns) in order to convince users to allow the collection of as much of their personal data as possible. In this paper, we design and conduct a user study where 1100 MTurk workers interact with eight different designs of cookie consent notices. We show that the nudging designs used in the different cookie consent notices have a large effect on the choices user make. Our results show that color-based nudging bars can significantly impact the participants' decisions to change the default cookie settings, despite using dark patterns. Also, in contrast to previous works, we report that users who do not use ad-blocking software are less likely to modify default cookie settings. Our findings demonstrate the importance of nudged interfaces and the effects orthogonal nudging techniques can have on users' choices.CoreTheoryNudgep. 2: "Another technique that website designers widely use in order to influence users’ decisions while browsing is nudging [2, 11, 43]. For example, in a cookie consent display, the accept-essential-only setting may be highlighted in color in order to nudge users towards a more privacy-aware choice" ... p. 3: "In this paper, we use the term nudging to refer to subtle changes in the cookie consent design that guide users towards privacy-aware decision" ... p. 4: "Nudging is any aspect of a choice architecture that alters the behaviour of the individuals towards more beneficial decisions for individual"Richard H Thaler and Cass R Sunstein. 2009. Nudge: Improving decisions about health, wealth, and happiness. Penguin
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✅7Gray et al. (2022)Bhoot, A., A. Shinde, M., & P. Mishra, W. (2020). Towards the Identification of Dark Patterns: An Analysis Based on End-User Reactions. IndiaHCI ’20: Proceedings of the 11th Indian Conference on Human-Computer Interaction, 24–33. https://doi.org/10.1145/3429290.3429293Towards the Identification of Dark Patterns: An Analysis Based on End-User Reactions2020https://doi.org/10.1145/3429290.3429293A dark pattern is a user interface that purposefully deceives users for the benefit of the business by influencing their decision making process. The objectives of this research paper are three-fold. The first objective is to determine the difference in the susceptibility of the users to the different types of dark patterns. The second is to identify the underlying factors that make users victims of the different types of dark patterns. The third objective is to identify the difference in the impact on the users, caused by the least identified and the most identified dark pattern. This paper presents five elements that play an important role in the identification of dark patterns by the users, even if they are not completely aware of the unethical intentions behind the design. In addition to that, a taxonomy is formed with the factors that trigger the users towards dark patterns. Strong correlations and associations between these five elements and the user's ability to identify dark patterns are found. It was also found that the correlations between the elements differ from the type of dark pattern in consideration. This paper helps in understanding the factors that influence the users to become victims of dark patterns and the difference in the impact of the different types of dark patterns on the user. The variables and factors of identification determined in this research can benefit the HCI community to understand the adverse effects of dark patterns on usability.CoreModelFogg’s Behavioral Modep. 24: "Fogg’s Behavioral Model states the factors that can be effectively used to nudge a user in order to perform the intended task. This may have many potential benefits but may also adversely affect the user [11]"Maximilian Maier. 2019. Dark patterns – An end user perspective. Retrieved 2020 from http://umu.diva-portal.org/smash/get/diva2:1330920/FULLTEXT01.pdfhttp://umu.diva-portal.org/smash/get/diva2:1330920/FULLTEXT01.pdfBj Fogg. 2009. A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology - Persuasive ’09. http://doi.org/10.1145/1541948.1541999https://dl.acm.org/doi/10.1145/1541948.1541999B.j. Fogg. 2003. Computers as persuasive social actors. Persuasive Technology: 89–120. http://doi.org/10.1016/b978-155860643-2/50007-xhttp://doi.org/10.1016/b978-155860643-2/50007-xCursoryTheoryDual Process p. 25: "Two different cognitive systems are operating when users make decisions, System 1 and System 2. System 1 is the unconscious part and works effortlessly, whereas System 2, has the ability to consciously “construct thoughts in an orderly series of steps” [10 , 15]. Since the conscious mind is unable to process all of the overwhelming amounts of data that it faces, unconscious decision-making takes up the biggest portion of mental processing: about 95% of our cognitive activities are made in a non-conscious manner [11]"Daniel Kahneman. 2015. Thinking, fast and slow. Farrar, Straus and Giroux, New York.CursoryTheoryNudgep. 25: "Nudges alter the environment in a way that triggers the fast and automatic cognitive decision-making processes of System 1 to promote the desired outcome [12 , 14]. This is partly responsible for users being victims of dark patterns."Richard Thaler and Cass Sunstein. 2009. Nudge: improving decisions about health, wealth, and happiness. Penguin Books, New York, NY.
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✅8Gray et al. (2022)Bongard-Blanchy, K., Rossi, A., Rivas, S., Doublet, S., Koenig, V., & Lenzini, G. (2021). ”I am Definitely Manipulated, Even When I am Aware of it. It’s Ridiculous!” - Dark Patterns from the End-User Perspective. Designing Interactive Systems Conference 2021, 1, 763–776. https://doi.org/10.1145/3461778.3462086"I Am Definitely Manipulated, Even When I Am Aware of It. It's Ridiculous!" - Dark Patterns from the End-User Perspective2021https://doi.org/10.1145/3461778.3462086Online services pervasively employ manipulative designs (i.e., dark patterns) to influence users to purchase goods and subscriptions, spend more time on-site, or mindlessly accept the harvesting of their personal data. To protect users from the lure of such designs, we asked: are users aware of the presence of dark patterns? If so, are they able to resist them? By surveying 406 individuals, we found that they are generally aware of the influence that manipulative designs can exert on their online behaviour. However, being aware does not equip users with the ability to oppose such influence. We further find that respondents, especially younger ones, often recognise the ‚Äùdarkness‚Äù of certain designs, but remain unsure of the actual harm they may suffer. Finally, we discuss a set of interventions (e.g., bright patterns, design frictions, training games, applications to expedite legal enforcement) in the light of our findings.CursoryMechanismCounterfactual Thinkingp. 772: "attributes trigger users’ scepticism towards interfaces and activate a more elaborate mode of thought (i.e., counterfactual thinking [ 6]) that disposes them to recognise potential manipulation attempts."David M. Boush, Marian Friestad, and Peter Wright. 2009. Deception In The Marketplace: The Psychology of Deceptive Persuasion and consumer self-protection (first edition ed.). Rouledge.CursoryMechanismHyperbolic Discountingp. 764: "hyperbolic discounting causes people to overvalue current rewards (e.g., accomplish a task), while they inadequately discount the cost of future risks [73] (e.g., privacy invasion)"Rick Wash. 2010. Folk Models of Home Computer Security. In Proceedings of the Sixth Symposium on Usable Privacy and Security (Redmond, Washington, USA) (SOUPS ’10). Association for Computing Machinery, New York, NY, USA, Article 11, 16 pages. https://doi.org/10.1145/1837110.1837125https://doi.org/10.1145/1837110.1837125CursoryMechanismOptimism Biasp.764: "The optimism bias [ 63 ] might make individuals underestimate their disposition to online manipulation."Tali Sharot. 2011. The optimism bias. Current biology 21, 23 (2011), R941–R945.
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✅9Gray et al. (2022)Borberg, I., Hougaard, R., Rafnsson, W., & Kulyk, O. (2022). “So I Sold My Soul”: Effects of Dark Patterns in Cookie Notices on End-User Behavior and Perceptions. Usable Security and Privacy (USEC) Symposium 2022. https://dx.doi.org/10.14722/usec.2022.23026"So I Sold My Soul": Effects of Dark Patterns in Cookie Notices on End-User Behavior and Perceptions2022https://dx.doi.org/10.14722/usec.2022.23026Cookies are widely acknowledged as a potential privacy issue, due to their prevalence and use for tracking users across the web. To address this issue, multiple regulations have been enacted which mandate informing users about data collection via. so-called cookie notices. Unfortunately, these notices have been shown to be ineffective; they are largely ignored, and are generally not understood by end-users. One main source of this ineffectiveness is the presence of dark patterns in notice designs, i.e. user interface design elements that nudge users into performing an action they may not otherwise do, e.g. consent to data collection. In this paper, we investigate the mental models and behavior of users when confronted with dark patterns in cookie notices. We do this by performing a mixed-method study (on Danes in their late-20s) which integrates quantitative and qualitative insights. Our quantitative findings confirm that the design of a cookie notice does influence the decisions of users on whether or not to consent to data collection, as well as whether they recall seeing the notice at all. Our qualitative findings reveal that users do in fact recognize the presence of dark patterns in cookie notice designs, and that they are very uncomfortable with standard practices in data collection. However, they seldom take action to protect their privacy, being overall resigned due to decision fatigue. We conclude that website maintainers need to reconsider how they request consent lest they alienate their users, and that end-users need better solutions that alleviate their burden wrt. protecting their privacy whilst visiting websites that collect data.CoreModelMental Modelp. 1: "mechanisms whereby humans generate descriptionsof system purpose and form, explanations of system functioning and system states, and predictions of future system states”, see [19], [29]"W. B. Rouse and N. M. Morris, “On looking into the black box: Prospects and limits in the search for mental models.” Psychological bulletin, vol.100, no. 3, p. 349, 1986.https://psycnet.apa.org/record/1987-09358-001M. Volkamer and K. Renaud, “Mental models–general introduction and review of their application to human-centred security,” in Number Theory and Cryptography. Springer, 2013, pp. 255–280.https://www.researchgate.net/publication/262933754_Mental_Models_-_General_Introduction_and_Review_of_Their_Application_to_Human-Centred_Security
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✅10Gray et al. (2022)Bösch, C., Erb, B., Kargl, F., Kopp, H., & Pfattheicher, S. (2016). Tales from the Dark Side: Privacy Dark Strategies and Privacy Dark Patterns. Proceedings on Privacy Enhancing Technologies, 2016(4). https://doi.org/10.1515/popets-2016-0038Tales from the Dark Side: Privacy Dark Strategies and Privacy Dark Patterns2016https://doi.org/10.1515/popets-2016-0038Privacy strategies and privacy patterns are fundamental concepts of the privacy-by-design engineering approach. While they support a privacy-aware development process for IT systems, the concepts used by malicious, privacy-threatening parties are generally less understood and known. We argue that understanding the “dark side”, namely how personal data is abused, is of equal importance. In this paper, we introduce the concept of privacy dark strategies and privacy dark patterns and present a framework that collects, documents, and analyzes such malicious concepts. In addition, we investigate from a psychological perspective why privacy dark strategies are effective. The resulting framework allows for a better understanding of these dark concepts, fosters awareness, and supports the development of countermeasures. We aim to contribute to an easier detection and successive removal of such approaches from the Internet to the benefit of its users.CoreMechanismCognitive Dissonancep. 247: "Cognitive dissonance [17] is a state of discomfort caused by contradictory beliefs and actions. According to the theory of cognitive dissonance, the experience of inconsistency triggers a reduction of dissonance and a potential modification of the conflicting cognition. In terms of privacy dark patterns, this process can be exploited by inconspicuously providing justification arguments for sugarcoating user decisions that have negatively affected their privacy. For instance, after asking users for inappropriate amounts of personal data, a service provider would later remind the users of the high data protection standards they comply with."L. Festinger, A theory of cognitive dissonance. Stanford university press, 1962, vol. 2.CoreTheoryDual Process p. 244: "There is widespread agreement in the field of psychological research that two different cognitive systems underlie thinking and reasoning processes [29, 43, 44]."Daniel Kahneman. 2015. Thinking, fast and slow. Farrar, Straus and Giroux, New York.K. E. Stanovich and R. F. West, “Advancing the rationality debate,” Behavioral and Brain Sciences, vol. 23, no. 05, pp.701–717, 2000.F. Strack and R. Deutsch, “Reflective and impulsive determinants of social behavior,” Personality and Social Psychology Review, vol. 8, no. 3, pp. 220–247, 2004.https://d-nb.info/1101586389/34CursoryTheoryNeed to Belongp. 246: "Humans pos-sess basic needs, e.g., safety and security needs, concerns about physical well-being, the need for self-esteem, and the need to belong to significant others [23]. We identified the need to belong as particularly important for why some privacy dark strategies work well."E. T. Higgins, Beyond pleasure and pain: How motivation works. Oxford University Press, 2011.CursoryTheoryNudgep. 247: "First, we focus on nudging, a concept for influencing decision making based on positive reinforcement and non-forced compliance [45]. Nudging has already been applied to decision making in the domain of privacy protection [3]."R. Thaler, Nudge : improving decisions about health, wealth, and happiness. New York: Penguin Books, 2009.CursoryTheoryPersuasion p. 247: "A stronger form of manipulation is achieved by ap-plying traditional persuasion techniques [13]. For instance, the so-called “door in the face” technique takes advantage of the principle of reciprocity. In this technique, the refusal of a large initial request increases the likelihood of agreement to a second, smaller request.This technique has already been studied in the context of private information disclosure [4] and privacy user settings [30]. Applied to privacy dark strategies, a service provider might intentionally ask users for disproportionate amounts of personal data. By providing an option to skip the first form and then only asking for a very limited set of personal data in the second form (e.g., mail address only), users may be more willing to comply and to provide that information after all."R. Cialdini, Influence : the psychology of persuasion. New York: Morrow, 1993.CoreMechanismHyperbolic Discountingp. 764: "hyperbolic discounting causes people to overvalue current rewards (e.g., accomplish a task), while they inadequately discount the cost of future risks [73] (e.g., privacy invasion)"D. Laibson, “Golden eggs and hyperbolic discounting,” The Quarterly Journal of Economics, vol. 112, no. 2, pp. 443–478, 1997.https://dash.harvard.edu/bitstream/handle/1/4481499/Laibson_GoldenEggs.pdf
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✅12Gray et al. (2022)Chaudhary, A., Saroha, J., & Monteiro, K. (n.d.). “Are You Still Watching?”: Exploring Unintended User Behaviors and Dark Patterns on Video Streaming Platforms. https://doi.org/10.1145/3532106.3533562Are You Still Watching?: Exploring Unintended User Behaviors and Dark Patterns on Video Streaming Platforms2022https://doi.org/10.1145/3532106.3533562Dark patterns in UI promote addictive behaviors. We explore how the effects of dark patterns in video streaming applications can be exacerbated by a range of temporal and contextual factors. Previous work has shown that excessive watching is potentially detrimental to physical and mental health. We conduct a diary study with 22 viewers over 228 sessions to gain insight into users‚Äô states of mind and to identify users‚Äô emotions while interacting with 4 popular streaming platforms. We analyze users during both the selection phase and the completion phase, finding meaningful correlations between user mood and contextual behaviors that highlight how particular individual characteristics and viewing situations can lead to negative behaviors. We discuss the implications of our findings, highlighting important UI design considerations to enhance digital wellbeing. Furthermore, we collect artifacts of problematic UIs, and present a novel taxonomy of dark patterns found in popular video streaming platforms from a user-centric perspective.CoreTheoryDual Process p. 780: "Dual Process Theory acts as our framework to investigate thinking patterns and user perceptions in video watching as it is intended to reveal both conscious (controlled, mindful) and unconscious (automatic, mindless) thinking patterns [30 ], as manifested in user viewing behaviors when interacting with UI features to navigate to the next video in a session. This is done by providing options that display an equal number of mindful/conscious states of mind, mindless/unconscious states of mind, and an option in between them for each UI interaction. In general, participants were encouraged to use additional comments for any diary prompt using the “others (please specify)” option, wherever necessary."Daniel Kahneman. 2011. Thinking, fast and slow. Macmillan.
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x13Gray et al. (2022)Chivukula, S. S., Watkins, C., McKay, L., & Gray, C. M. (05/2019). “Nothing Comes Before Profit”: Asshole Design in the Wild. CHI EA ’19: CHI’19 Extended Abstracts on Human Factors in Computing Systems, LBW1314. https://doi.org/10.1145/3290607.3312863Nothing Comes Before Profit: Asshole Design In the Wild 2019https://doi.org/10.1145/3290607.3312863Researchers in HCI and STS are increasingly interested in describing ethics and values relevant for design practice, including the formulation of methods to guide value application. However, little work has addressed ethical considerations as they emerge in everyday conversations about ethics in venues such as social media. In this late breaking work, we describe online conversations about a concept known as "asshole design" on Reddit, and the relationship of this concept to another practitioner-focused concept known as "dark patterns." We analyzed 1002 posts from the subreddit '/r/assholedesign' to identify the types of artifact being shared and the interaction purposes that were perceived to be manipulative or unethical as a type of "asshole design." We identified a subset of these posts relating to dark patterns, quantifying their occurrences using an existing dark patterns typology.
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✅14Gray et al. (2022)Forbrukerrådet (Norwegian Consumer Council). (2018). Deceived by Design: How tech companies use dark patterns to discourage us from exercising our rights to privacy.Deceived By Design: How tech companies use dark patterns to discourage us from exercising our rights to privacy2018https://storage02.forbrukerradet.no/media/2018/06/2018-06-27-deceived-by-design-final.pdfIn this report, we analyze a sample of settings in Facebook, Google andWindows 10, and show how default settings and dark patterns, techniques and features of interface design meant to manipulate users, are used to nudge users towards privacy intrusive options. The findings include privacy intrusive default settings, misleading wording, giving users an illusion of control, hiding away privacy-friendly choices, take-it-or-leave-it choices, and choice architectures where choosing the privacy friendly option requires more effort for the users.CoreTheoryNudgep. 6: "The concept of nudging comes from the fields of behavioural economy and psychology, and describes how users can be lead toward making certain choices by appealing to psychological biases. Rather than making decisions based on rationality, individuals have a tendency to be influenced by a variety of cognitive biases, often without being aware of it"“Privacy’s Blueprint: The Battle to Control the Design of New Technologies”, page 36 https://books.google.no/books?id=YERMDwAAQBAJ&lpg=PA35&dq=nudging%20aw ay%20from%20privacy&hl=no&pg=PA36#v=onepage&q=nudging%20away%20from%20privacy&f=falsehttps://books.google.no/books?id=YERMDwAAQBAJ&lpg=PA35&dq=nudging%20aw ay%20from%20privacy&hl=no&pg=PA36#v=onepage&q=nudging%20away%20from%20privacy&f=false
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x15Gray et al. (2022)Cranor, L. F. (2022). Cookie monster. Communications of the ACM, 65(7), 30–32. https://doi.org/10.1145/3538639Cookie Monster2022https://doi.org/10.1145/3538639Inscrutable cookie banners torment users while failing to inform consent.
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x16Gray et al. (2022)Di Geronimo, L., Braz, L., Fregnan, E., Palomba, F., & Bacchelli, A. (2020). UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376600UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception2020https://doi.org/10.1145/3313831.3376600A Dark Pattern (DP) is an interface maliciously crafted to deceive users into performing actions they did not mean to do. In this work, we analyze Dark Patterns in 240 popular mobile apps and conduct an online experiment with 589 users on how they perceive Dark Patterns in such apps. The results of the analysis show that 95% of the analyzed apps contain one or more forms of Dark Patterns and, on average, popular applications include at least seven different types of deceiving interfaces. The online experiment shows that most users do not recognize Dark Patterns, but can perform better in recognizing malicious designs if informed on the issue. We discuss the impact of our work and what measures could be applied to alleviate the issue.
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✅18Gray et al. (2022)Dmitry, N., & Yerkebulan, B. (2022). Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce). Science in China, Series A: Mathematics, 10(18), 3219. https://doi.org/10.3390/math10183219Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce)2022https://doi.org/10.3390/math10183219dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using two methods for clustering many dark patterns in application interfaces: hierarchical and k-means. The complexity of the implementation lies in the lack of datasets that formalize dark patterns in user interfaces. The authors conducted a study and identified signs of dark patterns based on the use of Nelsen’s antisymmetric principles. The article proposes a technique for assessing dark patterns using linguistic variables and their further interval numerical assessment for implementing cluster data analysis. The last part of the article contains an analysis of two clustering algorithms and an analysis of the methods and procedures for applying them to clustering data according to previously selected features in the RStudio environment. We also gave a characteristic for each resulting cluster.insufficient refererrd from oringinal theoryCorePrincipleUsability Heuristicspara. 3: "Since we associate the use of dark patterns with user interface design, we can name two Danish engineers Jakob Nielsen and Rolf Molich as the founders of this area of activity. In 1990, they planned 10 principles that a user-friendly interface should comply with [1,2,3,4,5]. From this point of view, “dark patterns” are a vivid example of ignoring these principles, “dark patterns” are interfaces created based on Nielsen’s antisymmetric principles."NielsenCursoryTheoryProspect para. 6: "Economics Nobel Prize winners Daniel Kahneman and Amos Tversky indirectly looked at dark patterns in terms of how they affect people’s behavior; they identified and formalized the psychological principles and criteria for decision-making and the risks associated with them [17]."Tversky, A.; Kahneman, D. Prospect theory: An analysis of decision under risk. Econometrica 1979, 47, 263–291.
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✅19Gray et al. (2022)Dupont, B., & Malliet, S. (2021). Contextualizing Dark Patterns with the Ludeme Theory: A New Path for Digital Game Literacy? Acta Ludologica, 4(1), 4–22. https://www.ceeol.com/search/article-detail?id=959439Contextualizing Dark Patterns with the Ludeme Theory: A New Path for Digital Game Literacy?2021https://www.ceeol.com/search/article-detail?id=959439So-called dark patterns are widely discussed in game design. This phenomenon raises concerns for gaming education because numerous dark patterns trick players into real money transactions or gambling. A major obstacle to the practical assessment of the severity of a ‘dark’ pattern is the very definition of ‘game patterns’, basing solely on ac- tion-oriented structures. In order to take into account not only abstract expressions of the game system, but also the experience of the player, as well as the diverse contexts in which games are played, this article proposes to use the semiotic model of the ‘ludeme’. A ludeme is a minimal element in game design consisting of a grapheme, an acousteme, and a motifeme. We begin by explaining and situating the conceptual framework of the ludeme theory, with a specific interest in its application to repetitions of the same game element over time and through different digital games. Then, the theoretical framework is applied to SimCity BuildIt and particularly to the ‘dark patterns’ in it. In the last part, paths for further developments of the model of ludemic analysis are discussed, with regard to its relevance for media education and digital game literacy.CoreTheoryLudeme p. 10-11: "Basing on similar premises, 34 D. Hansen comes up with a proposition of a digital game grammar with the ludeme as the ‘basic video game unit’. 35 His work draws on heterogeneous sources from analogue and digital game studies and mitigates diverse views on minimal design elements, 36 in an attempt to link together ‘being and doing, formalization and use, game and player’. 37 The ludeme, as the videoludic equivalent of F. de Saussure’s morpheme, is constituted of a “grapheme (graphic unit), a sound, or even an acousteme if one wants to continue the structuralist tradition, and of mechanical properties or mecanemes”. 38 Our decision to analyse dark game content in terms of ludemes, rather than patterns, reflects a deeper discussion on the processes of meaning creation that take place during game play" ... "On the other hand, there is the position popularized by M. Sicart41 that such ACTA LUDOLOGICAa mechanic is only activated by a significant effort of a player, and as such, that player agency should be always considered an indistinguishable part of a game’s building blocks.In proposing to investigate game content in terms of ludemes, rather than patterns, we indirectly align to this second position."HANSEN, D.: Morphologie du médium vidéoludique : Le ludème envisagé comme unité minimale fonctionnelle du jeu vidéo. [Master’s Thesis]. Liège : Département de Langues, Lettres et Traductologie, University of Liège, 2019, p. 40.https://matheo.uliege.be/handle/2268.2/8274
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✅20Gray et al. (2022)Fansher, M., Chivukula, S. S., & Gray, C. M. (2018). #darkpatterns: UX Practitioner Conversations About Ethical Design. Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, LBW082. https://doi.org/10.1145/3170427.3188553#darkpatterns: UX Practitioner Conversations About Ethical Design2018https://doi.org/10.1145/3170427.3188553There is increasing interest in the role that ethics plays in UX practice, however current guidance is largely driven by formalized frameworks and does not adequately describe "on the ground" practitioner conversations regarding ethics. In this late-breaking work, we identified and described conversations about a specific ethical phenomenon on Twitter using the hashtag #darkpatterns. We then determined the authors of these tweets and analyzed the types of artifacts or links they shared. We found that UX practitioners were most likely to share tweets with this hashtag, and that a majority of tweets either mentioned an artifact or "shames" an organization that engages in manipulative UX practices. We identify implications for building an enhanced understanding of pragmatist ethics from a practitioner perspective.CursoryMechanismStrategies of Persuasionp. 2: "Although the concept of dark patterns has evolved in primarily within design practice, with limited reference in the academic literature [9], it seems to have resonance with academic knowledge that is focused on higher-level ethical theories and methods such as strategies of persuasion [6]"BJ Fogg. 2003. Persuasive Technology: Using Computers to Change What We Think and Do, Morgan Kaufmann. https://doi.org/10.1016/B9781-55860-643-2.X5000-8
21
x21Gray et al. (2022)Fitton, D., Bell, B. T., & Read, J. C. (2021). Integrating Dark Patterns into the 4Cs of Online Risk in the Context of Young People and Mobile Gaming Apps. Human-Computer Interaction – INTERACT 2021, 701–711. https://doi.org/10.1007/978-3-030-85610-6_40Integrating Dark Patterns into the 4Cs of Online Risk in the Context of Young People and Mobile Gaming Apps2021https://doi.org/10.1007/978-3-030-85610-6_40Mobile technologies potentially expose children and adolescents to increasing online risk. These risks take many forms and are widely categorized using the 4Cs: Content, Conduct, Contact, and Commerce. Commerce is the least developed category and, while it has significant overlap with what is known as Dark Design within the field of UX, amalgamation of Dark Design and the 4Cs has not yet been considered. Within this paper we integrate Dark Design into the 4Cs to provide a set of questions we call RIGA (Risk In Games Assessment) and use RIGA to identify potential risks to children and adolescents in free-to- play mobile gaming apps. The key contribution of this paper is the integration of contemporary understandings of Dark Design into the 4Cs framework, through the RIGA question set, which can support research and practitioner communities in identifying potential risk to young people present in mobile gaming apps.
22
✅22Gray et al. (2022)Fitton, D., & Read, J. C. (2019). Creating a Framework to Support the Critical Consideration of Dark Design Aspects in Free-to-Play Apps. Proceedings of the 18th ACM International Conference on Interaction Design and Children, 407–418. https://doi.org/10.1145/3311927.3323136Creating a Framework to Support the Critical Consideration of Dark Design Aspects in Free-to-Play Apps2019https://doi.org/10.1145/3311927.3323136The majority of mobile apps are free-to-play and so include a variety of forms of advertising and other mechanisms for monetization. These monetization mechanisms often have deceptive elements and closely resemble what designers know as Dark Patterns. In-app advertising and purchasing have been studied with adults but, to-date, younger users have received comparatively little consideration despite their increased susceptibility to manipulation. This paper addresses the gap in research by creating the ADD (App Dark Design) framework which brings together insights from practitioners, theory from existing related research, and the findings from a user study which gathered qualitative data from 39 girls aged 12-13 years. We also derive a set of emerging issues and identify future research questions. This work is the first of its kind to create a framework to support the critical consideration of the design of free-to-play apps. We have identified a set of problematic Dark Design aspects that young people across the world are encountering in their apps every day and we hope this paper will both raise awareness and stimulate further research work on this important topic.insufficient refererrd from oringinal theoryCursoryMechanismMental Accounting p. 409: "In 2016 Kim et al. [24] explored app purchasing decisions using Mental Accounting theory as an underlying theoretical framework; this theory considers risk/uncertainly in relation to purchasing decisions and maximizing the acquisition utility (perceived value of app compared to actual cost) along with transaction utility (perceived merits/absence of risk or uncertainty inherent in the purchase). Again, the complexity of purchasing decisions in this context is apparent, with factors identified by Kim et al. including perceptions of word of mouth, usefulness, monetary value, trialability (ability to trial the app prior to purchase), and enjoyment."Hee-Woong Kim, Atreyi Kankanhalli, and Hyun Lyung Lee. 2016. Investigating decision factors in mobile application purchase: A mixed-methods approach. Information & Management 53, 6: 727–739.CursoryModelAffect–Behavior–Cognition modelp. 409: "In 2016 Hsu and Lin published research considering influences on in-app purchasing intention, again with adults, using a model based on the general Affect–Behavior–Cognition understanding of attitudes, with strength of intention to perform a behavior being driven by associated cognitive responses (beliefs) and affective responses (attitudes towards and perceptions of potential satisfaction) [20]."Chin-Lung Hsu and Judy Chuan-Chuan Lin. 2016. Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological Forecasting and Social Change 108: 42–53. https://doi.org/10.1016/j.techfore.2016.04.012https://doi.org/10.1016/j.techfore.2016.04.012CursoryMechanismDomain-Specific Innovativeness (DSI)p. 409: "In [23] Ladeira et al. studied Freemium games in the context of children aged 9-12, focusing on experiential value and its influence on Domain-Specific Innovativeness (DSI), along with the influence of DSI on child wellbeing and life satisfaction. Experiential value is the value the user perceives from the experience of playing a game; constructs considered included visual appeal, entertainment, escapism, enjoyment, efficiency, and economic value. DSI measures predisposition to purchasing new and different products and includes comparison with peers [30]. A key finding was that experiential value was a predictor of DSI, and for children with a high level of parental materialism or high levels of communication with peers there were positive relationships between DSI and well-being."Gilles Roehrich. 2004. Consumer innovativeness: Concepts and measurements. Journal of Business Research 57, 6: 671–677. https://doi.org/10.1016/S0148-2963(02)00311-9https://doi.org/10.1016/S0148-2963(02)00311-9
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x23Gray et al. (2022)Fritsch, L. (2017). Privacy dark patterns in identity management. Open Identity Summit (OID), 5-6 October 2017, Karlstad, Sweden., 93–104. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1141673Privacy dark patterns in identity management2017https://dl.gi.de/bitstream/handle/20.500.12116/3583/proceedings-07.pdf?sequence=1This article presents three privacy dark patterns observed in identity management. Dark patterns are software design patterns that intentionally violate requirements, in the given case privacy requirements for identity management. First, the theoretical background is presented, and then next, the observed patterns are documented, described and formalized. The resulting dark patterns show how security is used as obfuscation of data collection, how the seemingly harmless collection of additional data is advertised to end users, and how the use of anonymization technology is actively discouraged by service providers.insufficient refererrd from oringinal theoryCoreTheory?Privacy theoryp. 2 section 1.1: In privacy theory, a digital identity is often defined as an identifier with related identity attributes attached [Cl05]. Pfitzmann and Hansen [PH10] defined: An identity is any subset of attribute values of an individual person which sufficiently identifies this individual person within any set of persons. They point out that there rarely is a single identity for a person, but many combinations and permutations of identity attributes that are used in various sets. Therefore, they introduce the concept of a partial identity by defining: A partial identity is a subset of attribute values of a complete identity, where a complete identity is the union of all attribute values of all identities of this person.Pfitzmann, A.; Hansen, M.: Anonymity, unlinkability, unobservability, pseudonymity, and identity management-a consolidated proposal for terminology. Technische Universität Dresden, Dresden, 2010.
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✅24Gray et al. (2022)Graßl, P., Schraffenberger, H., Borgesius, F. Z., & Buijzen, M. (2021). Dark and Bright Patterns in Cookie Consent Requests. Journal of Digital Social Research, 3(1), 1–38. https://doi.org/10.33621/jdsr.v3i1.54Dark and bright patterns in cookie consent requests2021https://doi.org/10.33621/jdsr.v3i1.54Dark patterns are (evil) design nudges that steer people’s behaviour through persuasive interface design. Increasingly found in cookie consent requests, they possibly undermine principles of EU privacy law. In two preregistered online experiments we investigated the effects of three common design nudges (default, aesthetic manipulation, obstruction) on users’ consent decisions and their perception of control over their personal data in these situations. In the first experiment (N = 228) we explored the effects of design nudges towards the privacy-unfriendly option (dark patterns). The experiment revealed that most participants agreed to all consent requests regardless of dark design nudges. Unexpectedly, despite generally low levels of perceived control, obstructing the privacy-friendly option led to more rather than less perceived control. In the second experiment (N = 255) we reversed the direction of the design nudges towards the privacy-friendly option, which we title “bright patterns”. This time the obstruction and default nudges swayed people effectively towards the privacy-friendly option, while the result regarding perceived control stayed the same compared to Experiment 1. Overall, our findings suggest that many current implementations of cookie consent requests do not enable meaningful choices by internet users, and are thus not in line with the intention of the EU policymakers. We also explore how policymakers could address the problem.CoreTheoryPrivacy CalculusP.2 "This assumption corresponds to a prominent model of privacy decision making, the privacy calculus theory, which presumes people’s behaviour to be fundamentally rational and privacy decisions to be made through conscious weighing of the costs and benefits of each choice option (Laufer & Wolfe, 1977). "Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.xCursoryTheoryNudgeOriginally, nudging means influencing the decisions of individuals or groups towards good choices (as judged bythemselves) through minor changes in the choice environment without compromising freedom of choice (a prominent example is a fly painted on a urinal in a public men’s toilet to prevent urine spillage; Thaler & Sunstein, 2009)Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness(Rev. and expanded ed). New York: Penguin Books.
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✅25Gray et al. (2022)Gray, C. M., Chen, J., Chivukula, S. S., & Qu, L. (2021). End User Accounts of Dark Patterns as Felt Manipulation. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), Article 372. https://doi.org/10.1145/3479516End User Accounts of Dark Patterns as Felt Manipulation2021https://doi.org/10.1145/3479516Manipulation defines many of our experiences as a consumer, including subtle nudges and overt advertising campaigns that seek to gain our attention and money. With the advent of digital services that can continuously optimize online experiences to favor stakeholder requirements, increasingly designers and developers make use of "dark patterns"-forms of manipulation that prey on human psychology-to encourage certain behaviors and discourage others in ways that present unequal value to the end user. In this paper, we provide an account of end user perceptions of manipulation that builds on and extends notions of dark patterns. We report on the results of a survey of users conducted in English and Mandarin Chinese (n=169), including follow-up interviews from nine survey respondents. We used a card sorting method to support thematic analysis of responses from each cultural context, identifying both qualitatively-supported insights to describe end users' felt experiences of manipulative products and a continuum of manipulation. We further support this analysis through a descriptive analysis of survey results and the presentation of examples from the interviews. We conclude with implications for future research, considerations for public policy, and guidance on how to further empower and give users autonomy in their experiences with digital services.CursoryTheoryNudgeP.4 "In contrast, the marketing literature has often described the manipulation of emotion in relatively positive terms (e.g., [48]), with even recent popular texts such as Thaler and Sunstein’s Nudge [56] focusing almost solely on the positive aspects of persuasive technologies, while largely ignoring instances where nudges turn into overtly manipulative experiences."Richard H Thaler and Cass R Sunstein. 2009. Nudge: Improving Decisions about Health, Wealth, and Happiness. Penguin. https://play.google.com/store/books/details?id=NGA9DwAAQBAJCursoryMechanismStrategies of Persuasion"In contrast, other scholars such as Fogg [25] have actively called for the identification and harnessing of principles of persuasion, with the contention that these principles can be used to promote user experiences where behavior modification is desirable (e.g., increasing motivation, reducing addictive behaviors)."B J Fogg. 2003. Persuasive Technology: Using Computers to Change What We Think and Do. 1–282 pages. https://doi.org/10.1016/B978-1-55860-643-2.X5000-8
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x26Gray et al. (2022)Gray, C. M., Chivukula, S. S., & Lee, A. (2020). What Kind of Work Do “Asshole Designers” Create? Describing Properties of Ethical Concern on Reddit. Proceedings of the 2020 ACM Designing Interactive Systems Conference, 61–73. https://doi.org/10.1145/3357236.3395486What Kind of Work Do "Asshole Designers" Create? Describing Properties of Ethical Concern on Reddit2020https://doi.org/10.1145/3357236.3395486Design practitioners are increasingly engaged in describing ethical complexity in their everyday work, exemplified by concepts such as "dark patterns" and "dark UX." In parallel, researchers have shown how interactions and discourses in online communities allow access to the various dimensions of design complexity in practice. In this paper, we conducted a content analysis of the subreddit "/r/assholedesign," identifying how users on Reddit engage in conversation about ethical concerns. We identify what types of artifacts are shared, and the salient ethical concerns that community members link with "asshole" behaviors. Based on our analysis, we propose properties that describe "asshole designers," both distinct and in relation to dark patterns, and point towards an anthropomorphization of ethics that foregrounds the inscription of designer's values into designed outcomes. We conclude with opportunities for further engagement with ethical complexity in online and offline contexts, stimulating ethics-focused conversations among social media users and design practitioners.
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x27Gray et al. (2022)Gray, C. M., Chivukula, S. S., Melkey, K., & Manocha, R. (2021, August 16). Understanding “dark” design roles in computing education. Proceedings of the 17th ACM Conference on International Computing Education Research. ICER 2021: ACM Conference on International Computing Education Research, Virtual Event USA. https://doi.org/10.1145/3446871.3469754Understanding ‚"Dark" Design Roles in Computing Education2021https://doi.org/10.1145/3446871.3469754In conjunction with the increasing ubiquity of technology, computing educators have identified the need for pedagogical engagement with ethical awareness and moral reasoning. Typical approaches to incorporating ethics in computing curricula have focused primarily on abstract methods, principles, or paradigms of ethical reasoning, with relatively little focus on examining and developing students‚Äô pragmatic awareness of ethics as grounded in their everyday work practices. In this paper, we identify and describe computing students‚Äô negotiation of values as they engage in authentic design problems through a lab protocol study. We collected data from four groups of three students each, with each group including participants from either undergraduate User Experience Design students, Industrial Engineering students, or a mix of both. We used a thematic analysis approach to identify the roles that students took on to address the design prompt. Through our analysis, we found that the students took on a variety of ‚Äúdark‚Äù roles that resulted in manipulation of the user and prioritization of stakeholder needs over user needs, with a focus either on building solutions or building rationale for design decisions. We found these roles to actively propagate through design discourses, impacting other designers in ways that frequently reinforced unethical decision making. Even when students were aware of ethical concerns based on their educational training, this awareness did not consistently result in ethically-sound decisions. These findings indicate the need for additional ethical supports to inform everyday computing practice, including means of actively identifying and balancing negative societal impacts of design decisions. The roles we have identified may productively support the development of pragmatically-focused ethical training in computing education, while adding more precision to future analysis of computing student discourses and outputs.
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x28Gray et al. (2022)Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., & Toombs, A. L. (2018). The Dark (Patterns) Side of UX Design. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 534:1–534:14. https://doi.org/10.1145/3173574.3174108The Dark (Patterns) Side of UX Design2018https://doi.org/10.1145/3173574.3174108Interest in critical scholarship that engages with the complexity of user experience (UX) practice is rapidly expanding, yet the vocabulary for describing and assessing criticality in practice is currently lacking. In this paper, we outline and explore the limits of a specific ethical phenomenon known as "dark patterns," where user value is supplanted in favor of shareholder value. We assembled a corpus of examples of practitioner-identified dark patterns and performed a content analysis to determine the ethical concerns contained in these examples. This analysis revealed a wide range of ethical issues raised by practitioners that were frequently conflated under the umbrella term of dark patterns, while also underscoring a shared concern that UX designers could easily become complicit in manipulative or unreasonably persuasive practices. We conclude with implications for the education and practice of UX designers, and a proposal for broadening research on the ethics of user experience.insufficient refererrd from oringinal theoryCursoryModelnot mentioned" The model of van Wynsberghe
et al. [70] provides one such model of ethics in design practice, creating a space for ethical theory to be taken up in a
pragmatic and generative, rather than objective and static way."		Aimee van Wynsberghe and Scott Robbins. 2014. Ethicist
as designer: a pragmatic approach to ethics in the lab. Sci
Eng Ethics 20, 4 (2014), 947–961.		DOI:
https://doi.org/10.1007/s11948-013-9498-4		CursoryFramework?Value-Sensitive MethodsValueSensitive Design (VSD) has been one of the most comprehensive frameworks developed to address the question of values in
design, described by its creators as "a theoretically grounded
approach to the design of technology that accounts for human
values in a principled and comprehensive manner throughout
the design process"		Batya Friedman, Peter Kahn, and Alan Borning. 2002.
Value sensitive design: Theory and methods. University
of Washington technical report December (2002), 2–12.		DOI:https://doi.org/10.1016/j.neuropharm.2007.08.009
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x29Gray et al. (2022)Gray, C. M., Santos, C., Bielova, N., Toth, M., & Clifford, D. (2021, May). Dark Patterns and the Legal Requirements of Consent Banners: An Interaction Criticism Perspective. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445779Dark Patterns and the Legal Requirements of Consent Banners: An Interaction Criticism Perspective2021https://doi.org/10.1145/3411764.3445779User engagement with data privacy and security through consent banners has become a ubiquitous part of interacting with internet services. While previous work has addressed consent banners from either interaction design, legal, and ethics-focused perspectives, little research addresses the connections among multiple disciplinary approaches, including tensions and opportunities that transcend disciplinary boundaries. In this paper, we draw together perspectives and commentary from HCI, design, privacy and data protection, and legal research communities, using the language and strategies of “dark patterns” to perform an interaction criticism reading of three different types of consent banners. Our analysis builds upon designer, interface, user, and social context lenses to raise tensions and synergies that arise together in complex, contingent, and conflicting ways in the act of designing consent banners. We conclude with opportunities for transdisciplinary dialogue across legal, ethical, computer science, and interactive systems scholarship to translate matters of ethical concern into public policy.
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✅31Gray et al. (2022)Gunawan, J., Pradeep, A., Choffnes, D., Hartzog, W., & Wilson, C. (2021). A Comparative Study of Dark Patterns Across Web and Mobile Modalities. Proc. ACM Hum.-Comput. Interact., 5(CSCW2), 1–29. https://doi.org/10.1145/3479521A Comparative Study of Dark Patterns Across Web and Mobile Modalities2021https://doi.org/10.1145/3479521Dark patterns are user interface elements that can influence a person's behavior against their intentions or best interests. Prior work identified these patterns in websites and mobile apps, but little is known about how the design of platforms might impact dark pattern manifestations and related human vulnerabilities. In this paper, we conduct a comparative study of mobile application, mobile browser, and web browser versions of 105 popular services to investigate variations in dark patterns across modalities. We perform manual tests, identify dark patterns in each service, and examine how they persist or differ by modality. Our findings show that while services can employ some dark patterns equally across modalities, many dark patterns vary between platforms, and that these differences saddle people with inconsistent experiences of autonomy, privacy, and control. We conclude by discussing broader implications for policymakers and practitioners, and provide suggestions for furthering dark patterns research.insufficient refererrd from oringinal theoryCursoryTheoryChoice Architecture"Thaler and Sunstein took these concepts a step further in their discussion of how intentionally designed choice architectures can nudge people into taking actions or making choices that are favored by the designer"RichardH.ThalerandCassR.Sunstein.2008.Nudge:ImprovingDecisionsAboutHealth,Wealth,andHappiness.YaleUniversityPress.CursoryTheorySludge"Richard Thaler refers to dark patterns as “sludge” [61] since they weaponize peoples’ mental heuristics and cognitive biases against them [7, 50, 66]."Richard H. Thaler. 2018. Nudge, not Sludge. Science 361, 6401 (Aug. 2018).
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x32Gray et al. (2022)Habib, H., Li, M., Young, E., & Cranor, L. (2022). “Okay, whatever”: An Evaluation of Cookie Consent Interfaces. CHI Conference on Human Factors in Computing Systems, 1–27. https://doi.org/10.1145/3491102.3501985Okay, Whatever: An Evaluation of Cookie Consent Interfaces2022https://doi.org/10.1145/3491102.3501985Many websites have added cookie consent interfaces to meet regulatory consent requirements. While prior work has demonstrated that they often use dark patterns ‚Äî design techniques that lead users to less privacy-protective options ‚Äî other usability aspects of these interfaces have been less explored. This study contributes a comprehensive, two-stage usability assessment of cookie consent interfaces. We first inspected 191 consent interfaces against five dark pattern heuristics and identified design choices that may impact usability. We then conducted a 1,109-participant online between-subjects experiment exploring the usability impact of seven design parameters. Participants were exposed to one of 12 consent interface variants during a shopping task on a prototype e-commerce website and answered a survey about their experience. Our findings suggest that a fully-blocking consent interface with in-line cookie options accompanied by a persistent button enabling users to later change their consent decision best meets several design objectives.CorePrinciples?usability" Nielsen defnes usability through fve “quality components” that assess how easy interfaces are to use [28]."Jakob Nielsen. 2012. Usability 101: Introduction to Usability. https://www. nngroup.com/articles/usability- 101- introduction- to- usability/.CorePrinciples? Six qualities of the user experience"Morville’s UX Honeycomb is commonly referred to in web design and explains six qualities of the user experience that must be addressed"Peter Morville. 2004. User Experience Design. http://semanticstudios.com/user_ experience_design/.
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x33Gray et al. (2022)Habib, H., Pearman, S., Young, E., Saxena, I., Zhang, R., & Cranor, L. F. (2022). Identifying User Needs for Advertising Controls on Facebook. Proc. ACM Hum.-Comput. Interact., 6(CSCW1), 1–42. https://doi.org/10.1145/3512906Identifying User Needs for Advertising Controls on Facebook2022https://doi.org/10.1145/3512906We conducted an online survey and remote usability study to explore user needs related to advertising controls on Facebook and determine how well existing controls align with these needs. Our survey results highlight a range of user objectives related to controlling Facebook ads, including being able to select what ad topics are shown or what personal information is used in ad targeting. Some objectives are achievable with Facebook's existing controls, but participants seemed to be unaware of them, suggesting issues of discoverability. In our remote usability study, participants noted areas in which the usability of Facebook's advertising controls could be improved, including the location, layout, and explanation of controls. Additionally, we found that users could be categorized into four groups based on their privacy concerns related to Facebook's data collection practices, objectives for controlling their ad experience, and willingness to engage with advertising controls. Our findings provide a set of user requirements for advertising controls, applicable to Facebook as well as other platforms, that would better align such controls with users' needs and expectations.
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x34Gray et al. (2022)Hogan, M., Barry, C., & Lang, M. (2022). Dissecting Optional Micro-Decisions in Online Transactions: Perceptions, Deceptions and Errors. ACM Trans. Comput.-Hum. Interact. https://doi.org/10.1145/3531005Dissecting Optional Micro-Decisions in Online Transactions: Perceptions, Deceptions and Errors2022https://doi.org/10.1145/3531005Online firms frequently increase profit by selling optional extras. However, opt-in rates tend to be low. In response, questionable design practices have emerged to nudge consumers into inadvertent choices. Many of these design constructs are presented using an opt-out design. Using eye tracking and think-aloud data techniques, this research investigates the impact of the framing and optionality of micro-decisions on user perceptions and error rates. Focusing on opt-out decisions, the study found: up to one in three users make errors in decision-making; there is a higher error rate for rejection-framed opt-out decisions; users widely misinterpret decision framing; and failure to read decision text results in rushed and unsighted decisions, even leading users to automatically construe un-ticked checkboxes as opt-in decisions. In talking afterwards about their experiences, users expressed strong negative emotions, feeling confused, manipulated and resentful. Many suggested they would, in practice, steer away from similar encounters toward more unambiguous and honest sites. These findings might alert managers and developers, tempted to use dark patterns, that such a strategy might backfire over time.insufficient refererrd from oringinal theoryCursoryFramework (no detail bur reference)effects in privacy decision-making"Studies have shown acceptance framing in a decision construct results in more users opting in [2, 54–56, 84], and that framing, coupled with the default value (i.e., ticked or unticked), impact on user se- lection [2, 12]. " " It has been clearly demonstrated that question framing can affect user decisions [2, 54–56, 84], with positive framing resulting in more users opting-in."Anaraky Reza Ghaiumy, Nabizadeh Tahereh, Bart P. Knijnenburg, and Risius Marten. 2018. Reducing default and framing effects in privacy decision-making. In Proceedings of the SIGCHI 2018.Kuo Feng-Yang, Hsu Chiung-Wen, and Day Rong-Fuh. 2009. An exploratory study of cognitive effort involved in decision under framing—an application of the eye-tracking technology. Decision Support System 48, 1 (2009), 81–91.Lai Yee-Lin and Hui Kai-Lung. 2006. Internet opt-in and opt-out: Investigating the roles of frames, defaults and privacy concerns. In Proceedings of the 2006 ACM SIGMIS CPR Conference on Computer Personnel Research: Forty Four Years of Computer Personnel Research: Achievements, Challenges & the Future. ACM.Irwin P. Levin, Sandra L. Schneider, and Gary J. Gaeth. 1998. All frames are not created equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes 76, 2 (1998), 149–188.Tversky Amos and Kahneman Daniel. 1981. The framing of decisions and the psychology of choice. Science 211, 4481 (1981), 453–458.
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x39Gray et al. (2022)Kim, W. G., Pillai, S. G., Haldorai, K., & Ahmad, W. (2021). Dark patterns used by online travel agency websites. Annals Of Tourism Research, 88(C). https://doi.org/10.1016/j.annals.2020.103055Dark patterns used by online travel agency websites2021https://doi.org/10.1016/j.annals.2020.103055The “Deceptive Experiences To Online Users Reduction” Act is legislation that was introduced by U.S. senators, and it deals with digital companies' unethical dark pattern practices. Recent findings show that consumers are cheated out of almost $6 billion annually by online travel agency booking scams, where dark patterns influence 45% of customers (American Hotel and Lodging Association, 2018). This deceptive practice is coined dark pattern by Harry Brignull (Brignull, 2013). Dark patterns are user inter- faces that trick online consumers to perform unintended purchase actions by exploiting consumers' cognitive biases (Waldman, 2020). Cognitive biases are mental shortcuts and tendencies; they cause distortions in perception, interpretation, and judgment, thereby leading to systematic deviations from rational decision-making (Kahneman, 1973; Kahneman, 2011). The purpose of this research note is to shed light on how the various types of dark patterns found in online travel agencies' websites influence the cognitive biases that lead to irrational decision-making. This paper serves as a pebble in creating a comprehensive link be- tween cognitive psychology and tourism consumer behavior.
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✅40Gray et al. (2022)Kitkowska, A., Högberg, J., & Wästlund, E. (2022). Barriers to a well-functioning digital market: Exploring dark patterns and how to overcome them. Proceedings of the 55th Hawaii International Conference on System Sciences. https://scholarspace.manoa.hawaii.edu/items/24a63982-4927-488a-b5c3-c9dfca54fb39/fullBarriers to a well-functioning digital market: Exploring dark patterns and how to overcome them2022https://scholarspace.manoa.hawaii.edu/items/24a63982-4927-488a-b5c3-c9dfca54fb39/fullIn a well-functioning digital economy, consumers should be able to make autonomous and informed choices, and companies compete fairly. One of the barriers preventing such well-functioning is dark patterns—designs that mislead users into making specific purchase-related choices. In this research, through a qualitative inquiry (expert interviews), we classify dark patterns based on the harmful ways such designs affect the digital market. Moreover, we analyze data using the behavior change framework and illustrate ways to prevent dark patterns and grant consumers greater protection and autonomy. Our exploratory results outline potential solutions policymakers might apply to improve digital market well-functioning.CoreTheoryDual Process "One way to explain it is through the dual-process theories, implying that people make decisions using the two different information processing modes working in parallel. The first mode is Type 1, based on automatic, fast, and uncomplicated information processing. "D.Kahneman,“APerspectiveonJudgmentandChoice,” American Psychologist, vol. 3, no. 4, pp. 7–18, 2003.J. S. B. Evans and K. E. Stanovich, “Dual-Process Theories of Higher Cognition: Advancing the Debate,” Perspectives on Psychological Science, vol. 8, no. 3, pp. 223–241, 2013.CoreTheoryNudge"Thaler & Sunstein described how such cognitive shortcomings could be applied in choice architecture to improve human well-being, introducing the concept of nudging [18]."R.ThalerandC.Sunstein,Nudge.ImprovingDecisions About Health, Wealth, and Happiness. 2008.CoreFrameworkCOM-B (Capability, Opportunity, Motivation-Behavior) framework"Taking into account online companies and their influence on consumer behavior, in this research, we applied the COM-B (Capability, Opportunity, Motivation-Behavior) framework [15]." S.Michie,M.M.vanStralen,andR.West,“Thebehavior change wheel: A new method for characterising and designing behavior change interventions,” Implementation Science, vol. 6, no. 42, 2011.CursoryFrameworkBehavior Change Wheel "The framework’s authors further proposed the behavior change wheel (BCW), offering possible interventions that might steer changes [15]."S.Michie,M.M.vanStralen,andR.West,“Thebehavior change wheel: A new method for characterising and designing behavior change interventions,” Implementation Science, vol. 6, no. 42, 2011.
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x41Gray et al. (2022)Kollnig, K., Datta, S., & Van Kleek, M. (2021). I Want My App That Way: Reclaiming Sovereignty Over Personal Devices. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1–8. https://doi.org/10.1145/3411763.3451632I Want My App That Way: Reclaiming Sovereignty Over Personal Devices2021https://doi.org/10.1145/3411763.3451632Dark patterns in mobile apps take advantage of cognitive biases of end-users and can have detrimental effects on people‚Äôs lives. Despite growing research in identifying remedies for dark patterns and established solutions for desktop browsers, there exists no established methodology to reduce dark patterns in mobile apps. Our work introduces GreaseDroid, a community-driven app modification framework enabling non-expert users to disable dark patterns in apps selectively.
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x42Gray et al. (2022)Krisam, C., Dietmann, H., Volkamer, M., & Kulyk, O. (2021). Dark Patterns in the Wild: Review of Cookie Disclaimer Designs on Top 500 German Websites. European Symposium on Usable Security 2021, 1–8. https://doi.org/10.1145/3481357.3481516Dark Patterns in the Wild: Review of Cookie Disclaimer Designs on Top 500 German Websites2021https://doi.org/10.1145/3481357.3481516Cookie disclaimers are these days an indispensable part of surfing and working on the Internet. In this work, we report on examining and classifying the cookie disclaimers on the 500 most popular websites in Germany, based on the presented information about data collection via cookies and the provided choices at the cookie disclaimer. Our analysis results in 13 categories of cookie disclaimers, consisting of six main categories and additional subcategories. Our findings include that dark pattern based categories were prevalent among the cookie disclaimers: e.g. (1) more than 85% of the investigated websites providing a cookie disclaimer and giving the option to reject cookies are visually nudging users towards accepting all cookies; (2) Only 21.5% of those providing a cookie disclaimer offer a reject-all option with a single click. We discuss our results and conclude that both raising user awareness as well as addressing dark patterns from a legal point of view is needed.CursoryFramework?Transparency and Consent Framework (TCF)"One way to explain it is through the dual-process theories, implying that people make decisions using the two different information processing modes working in parallel. The first mode is Type 1, based on automatic, fast, and uncomplicated information processing. "Célestin Matte, Nataliia Bielova, and Cristiana Santos. 2020. Do Cookie Banners Respect my Choice?: Measuring Legal Compliance of Banners from IAB Europe’s Transparency and Consent Framework. (2020), 791–809.
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x43Gray et al. (2022)Lacey, C., & Caudwell, C. (2019). Cuteness as a “Dark Pattern” in Home Robots. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 374–381. https://doi.org/10.1109/HRI.2019.8673274Cuteness as a 'dark Pattern' in Home Robots2019https://doi.org/10.1109/HRI.2019.8673274Dark patterns are a recent phenomenon in the field of interaction design, where design patterns and behavioral psychology are deployed in ways that deceive the user. However, the current corpus of dark patterns literature focuses largely on screen-based digital interactions and should be expanded to include home robots. In this paper, we apply the concept of dark patterns to the 'cute' aesthetic of home robots and suggest that their design constitutes a dark pattern in HRI by (1) emphasizing short-term gains over long-term decisions; (2) depriving users of some degree of conscious agency at the site of interaction; and (3) creating an affective response in the user for the purpose of collecting emotional data. This exploratory paper expands the current library of dark patterns and their application to new technological interfaces into the domain of home robotics in order to establish the grounds for an ethical design practice in HRI.insufficient refererrd from oringinal theory
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✅44Gray et al. (2022)Luguri, J., & Strahilevitz, L. J. (2021). Shining a Light on Dark Patterns. Journal of Legal Analysis, 13(1), 43–109. https://doi.org/10.1093/jla/laaa006Shining a light on dark patterns2021https://academic.oup.com/jla/article/13/1/43/6180579Dark patterns are user interfaces whose designers knowingly confuse users, make it difficult for users to express their actual preferences, or manipulate users into taking certain actions. They typically exploit cognitive biases and prompt online consumers to purchase goods and services that they do not want or to reveal personal information they would prefer not to disclose. This article provides the first public evidence of the power of dark patterns. It discusses the results of the authors’ two large-scale experi- ments in which representative samples of American consumers were exposed to dark patterns. In the first study, users exposed to mild dark patterns were more than twice as likely to sign up for a dubious service as those assigned to the control group, and users in the aggressive dark pattern condition were almost four times as likely to subscribe. Moreover, whereas aggressive dark patterns generated a powerful backlash among consumers, mild dark patterns did not. Less educated subjects were significant- ly more susceptible to mild dark patterns than their well-educated counterparts. The second study identified the dark patterns that seem most likely to nudge consumers into making decisions that they are likely to regret or misunderstand. Hidden informa- tion, trick question, and obstruction strategies were particularly likely to manipulate consumers successfully. Other strategies employing loaded language or generating bandwagon effects worked moderately well, while still others such as “must act now” messages did not make consumers more likely to purchase a costly service. Our second study also replicated a striking result in the first experiment, which is that where dark patterns were employed the cost of the service offered to consumers became imma- terial. Decision architecture, not price, drove consumer purchasing decisions. The art- icle concludes by examining legal frameworks for addressing dark patterns. Many dark patterns appear to violate federal and state laws restricting the use of unfair and de- ceptive practices in trade. Moreover, in those instances where consumers enter into contracts after being exposed to dark patterns, their consent could be deemed void- able under contract law principles. The article also proposes that dark pattern audits become part of the Federal Trade Commission (FTC)’s consent decree process. Dark patterns are presumably proliferating because firms’ proprietary A-B testing has revealed them to be profit maximizing. We show how similar A-B testing can be used to identify those dark patterns that are so manipulative that they ought to be deemed unlawful.CoreTheoryNudge"This latter feature helps differentiate dark pat- terns from nudges, which may use similar techniques to foster prosocial behavior like organ donation or retirement saving (Thaler & Sunstein 2009)."Thaler, Richard H. & Sunstein Cass. R. 2009. Nudge: Improving Decisions about Health, Wealth, and Happiness. New York: Penguin Books.CoreTheorySludge"Everybody has seen them before and found them frustrating, but most con- sumers don’t know what to call them. They are what computer scientists and user-experience (UX) designers have (for the last decade) described as dark patterns,1 and they are a proliferating species of sludge (to use a term preferred by behavioral economists) (Sunstein 2019, p. 1843; Thaler 2018, p. 431) "Sunstein, Cass R. 2019. Sludge and Ordeals. 68 Duke L.J. 1843–1883.Thaler, Richard H. 2018. Nudge, Not Sludge. 361 Science 431.CursoryTheoryInformation Fiduciary "Even to skeptics of Balkin and Zittrain’s information fiduciary theory (Khan & Pozen 2019, p. 497)"Khan, Lina & Pozen David. 2019. A Skeptical View of Information Fiduciaries. 133 Harv. L. Rev. 497–541.
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✅45Gray et al. (2022)Lukoff, K., Lyngs, U., Zade, H., Liao, J. V., Choi, J., Fan, K., Munson, S. A., & Hiniker, A. (2021). How the Design of YouTube Influences User Sense of Agency. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3411764.3445467How the Design of YouTube Influences User Sense of Agency2021https://doi.org/10.1145/3411764.3445467In the attention economy, video apps employ design mechanisms like autoplay that exploit psychological vulnerabilities to maximize watch time. Consequently, many people feel a lack of agency over their app use, which is linked to negative life effects such as loss of sleep. Prior design research has innovated external mechanisms that police multiple apps, such as lockout timers. In this work, we shift the focus to how the internal mechanisms of an app can support user agency, taking the popular YouTube mobile app as a test case. From a survey of 120 U.S. users, we find that autoplay and recommendations primarily undermine sense of agency, while playlists and search support it. From 13 co-design sessions, we find that when users have a specific intention for how they want to use YouTube they prefer interfaces that support greater agency. We discuss implications for how designers can help users reclaim a sense of agency over their media use.CursoryTheorySelf-Determination " Feeling in control of one’s actions is integral to autonomy, one of the three basic human needs outlined in self- determination theory [95]."Richard M Ryan and Edward L Deci. 2006. Self-regulation and the problem of human autonomy: Does psychology need choice, self- determination, and will? Journal of personality 74, 6 (2006), 1557– 1586. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-6494.2006. 00420.x?casa_token=MZuzC4Br_U4AAAAA:ErU7WbByAWUFcoh2N_ 5TIRqe7jhVXe6V8Z0- pWB8gbb- ZZ3I8xz_qrdAtePASmiTFBWb2COF6sX4BQ
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✅46Gray et al. (2022)Maier, M., & Harr, R. (2020). Dark Design Patterns: An End-User Perspective. Human Technology, 16(2), 170–199. https://doi.org/10.17011/ht/urn.202008245641Dark design patterns: An end-user perspective2020https://doi.org/10.17011/ht/urn.202008245641The number of websites and mobile applications available is growing continually, as are the persuasive approaches to influence human behavior and decision making. Although designing for persuasion offers several potential benefits, recent developments expose various deceptive designs, that is, dark patterns, that utilize psychological factors to nudge people toward, from someone else’s perspective, desired directions. This paper contributes to an increased awareness of the phenomenon of dark patterns through our exploring how users perceive and experience these patterns. Hence, we chose a qualitative research approach, with focus groups and interviews, for our exploration. Our analysis shows that participants were moderately aware of these deceptive techniques, several of which were perceived as sneaky and dishonest. Respondents further expressed a resigned attitude toward such techniques and primarily blamed businesses for their occurrence. Users considered their dependency on services employing these practices, thus making it difficult to avoid fully dark patterns.CoreTheoryNudge"Nudges, “changes in choice architecture that predictably influence decisions without restricting freedom of choice” (Peer et al., 2019, p. 2), are effective tools for influencing people’s behavior and have been applied successfully in areas such as finance, education, and health."Peer, E., Egelman, S., Harbach, M., Malkin, N., Mathur, A., & Frik, A. (2019, January 29). Nudge me right: Personalizing online nudges to people’s decision-making styles. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3324907https://doi.org/10.2139/ssrn.3324907CoreTheoryDual Process " Cognitive psychology research starts from the premise that two different systems are active in human decision making, System 1 and System 2 (Kahneman, 2012). System 1 works unconsciously and effortlessly, and it relies on emotions and simplifying heuristics to make decisions, causing predictable biases. The slower and conscious System 2, on the other hand, “construct[s] thoughts in an orderly series of steps” (Kahneman, 2012, p. 21) "Kahneman, D. (2012). Thinking, fast and slow. London, UK: Penguin Books.Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich (Ed.), Heuristics and biases: The psychology of intuitive judgment (pp. 49–81). Cambridge, UK: Cambridge University Press. https://psycnet.apa.org/doi/10.1017/CBO9780511808098.004 https://psycnet.apa.org/doi/10.1017/CBO9780511808098.004CoreMechanismStrategies of Persuasion"Persuasive technology is defined broadly as “any interactive computing system designed to change people’s attitudes or behaviors” (Fogg, 2003, p. 1). Fogg, B. J. (2003). Persuasive technology: Using computers to change what we think and do. Burlington, MA, USA:
Morgan Kaufmann Publishers.		CoreMechanismStrategies of Persuasion"Fogg coined the term captology to describe “computers as persuasive technologies,” a perspective that includes “design, research, analysis, and ethics of interactive computing products created for the purpose of changing
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people’s attitudes or behaviors” (Fogg, 2003, p. 5)"		Fogg, B. J. (2003). Persuasive technology: Using computers to change what we think and do. Burlington, MA, USA:
Morgan Kaufmann Publishers.
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x47Gray et al. (2022)Mathur, A. (2021). Identifying and Measuring Manipulative User Interfaces at Scale on the Web. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1–5. https://doi.org/10.1145/3411763.3457782Identifying and Measuring Manipulative User Interfaces at Scale on the Web2021https://doi.org/10.1145/3411763.3457782In this dissertation, I present measurement methods to automatically identify manipulative user interfaces‚Äîcolloquially known as ‚Äúdark patterns‚Äù‚Äîat scale on the web. Using these methods, I quantify the prevalence of dark patterns in three studies and show how dark patterns are rampant on the web, thus a pressing concern for society. First, I examine whether social media content creators, or ‚Äúinfluencers,‚Äù disclose their relationships with advertisers to their audience. Analyzing over 500K YouTube videos and 2.1M Pinterest pins, I find that only about 10% of all advertising content is disclosed to users. Second, I examine various types of dark patterns in shopping websites. Analyzing data from 11K shopping websites, I discover over 1,800 dark patterns on over 1,200 websites that mislead users into making more purchases or disclosing more information than they would otherwise. Third, I examine dark patterns in political emails from the 2020 U.S. election cycle. Through an analysis of over 100K emails, I find that over 40% of emails from the median sender contain dark patterns that nudge recipients to open emails or make donations they might otherwise not make. I further outlay the conceptual foundation of dark patterns and articulate a set of normative perspectives for analyzing the effects of dark patterns. I conclude with how the lessons learned from the studies can be used to build technical defenses and to lay out policy recommendations to mitigate the spread of these interfaces.
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✅48Gray et al. (2022)Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A. (2019). Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), Article No. 81. https://doi.org/10.1145/3359183Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites2019https://doi.org/10.1145/3359183Dark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions. We present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, we study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. Analyzing ~53K product pages from ~11K shopping websites, we discover 1,818 dark pattern instances, together representing 15 types and 7 broader categories. We examine these dark patterns for deceptive practices, and find 183 websites that engage in such practices. We also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, we develop a taxonomy of dark pattern characteristics that describes the underlying influence of the dark patterns and their potential harm on user decision-making. Based on our findings, we make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns.CoreTheoryDual Process "In another instance, Bösch et al. [31] used Kahneman’s Dual process theory [79] which describes how humans have two modes of thinking—‘System 1’ (unconscious, automatic, possibly less rational) and ‘System 2’ (conscious, rational)—and noted how ‘Dark Strategies’ exploit users’ System 1 thinking to get them to make a decision desired by the designer."Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. Science 185, 4157 (1974), 1124–1131.CursoryTheoryNudge"Some argue that because users are not always capable of acting in their own best interests, some forms of ‘paternalism’—a term referring to the regulation or curation of the user’s options—may be acceptable"Richard H. Thaler and Cass R. Sunstein. 2003. Libertarian Paternalism. American Economic Review 93, 2 (May 2003), 175–179. https://doi.org/10.1257/000282803321947001https://doi.org/10.1257/000282803321947001Mejtoft, T., Ristiniemi, C., Söderström, U., & Mårell-Olsson, E. (2019). User experience design and digital nudging in a decision making process. In 32nd Bled eConference Proceedings (pp. 427- 442). University of Maribor Press.Mejtoft, T., Hale, S., & Söderström, U. (2019). Design Friction: How intentionally added friction affect users level of satisfaction. In Proceedings of the 31st European Conference on Cognitive Ergonomics (pp. 41-44). New York, NY: ACM.CursoryModelFogg’s Behavioral Mode"This can be done by designing for a behavior as a product of motivation, ability and triggers (Fogg, 2009)."Fogg, B. J. (2009). A Behavior Model for Persuasive Design. In Proceedings of the 4th International Conference on Persuasive Technology (Persuasive ’09), Article 40. ACM.
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✅50Gray et al. (2022)Mejtoft, T., Frängsmyr, E., Söderström, U., & Norberg, O. (2021). Deceptive design : cookie consent and manipulative patterns. 34th Bled eConference - Digital Support from Crisis to Progressive Change, Online, June 27-30, 2021, 397–408. https://doi.org/10.18690/978-961-286-485-9.29Deceptive design: cookie consent and manipulative patterns2021https://doi.org/10.18690/978-961-286-485-9.29As a larger proportion of our lives moves onto the web, so does important and valuable information. This has led to an increase in different kinds of manipulative patterns (dark patterns) in web design with the sole purpose of being deceptive and tricking users. This paper discusses the comprehensive suite of deceptive design patterns on Internet services where the users are expected to comply with the use of cookies. This was done by analyzing 50 different home cooking recipe websites, regarding their appliance to GDPR and how they use different dark patterns in their design. Even though legislation tries to
move the choices from the website to the user, it is clear that by using deceptive design patterns it is possible to “bypass” the legislation and trick the user into making a favorable choice for
the owners behind the website. The results show that out of the websites that were GDPR approved, a majority still use two types
of deceptive design patterns - misdirection and sneak into basket.		CoreTheoryNudge"Unlike concepts like e.g. digital nudging, which is about creating solutions that help the user to make the choices in their best interest by altering the choice environment (Thaler & Sunstein, 2008; Mejtoft et al., 2019), deceptive design is about manipulating a user into doing something that is not in the user’s best interest but in the interest of the owner of the website.Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth and happiness. Penguin Putnam Inc.CursoryTheoryDual Process "One way of dealing with the automatic behavior that cookie consent has become is to purposely introduce more intentional friction into the design that encourage a reflective behavior. Consequently, it is possible to focus on the important elements at hand and make users do reflective choices (Mejtoft, Hale, & Söderström, 2019; Hansen & Jespersen, 2015; Kahneman, 2008)."
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x51Gray et al. (2022)Mildner, T., & Savino, G.-L. (2021). Ethical User Interfaces: Exploring the Effects of Dark Patterns on Facebook. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1–7. https://doi.org/10.1145/3411763.3451659Ethical User Interfaces: Exploring the Effects of Dark Patterns on Facebook2021https://doi.org/10.1145/3411763.3451659Many researchers have been concerned with whether social media has a negative impact on the well-being of their audience. With the popularity of social networking sites (SNS) steadily increasing, psychological and social sciences have shown great interest in their effects and consequences on humans. In this work, we investigate Facebook using the tools of HCI to find connections between interface features and the concerns raised by these domains. Using an empirical design analysis, we identify interface interferences impacting users‚Äô online privacy. Through a subsequent survey (n = 116), we find usage behaviour changes due to increased privacy concerns and report individual cases of addiction and mental health issues. These observations are the results of a rapidly changing SNS creating a gap of understanding between users‚Äô interactions with the platform and future consequences. We explore how HCI can help close this gap and work towards more ethical user interfaces in the future.
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x53Gray et al. (2022)Mohan, J., Wasserman, M., & Chidambaram, V. (2019). Analyzing GDPR Compliance Through the Lens of Privacy Policy. Heterogeneous Data Management, Polystores, and Analytics for Healthcare, 82–95. https://doi.org/10.1007/978-3-030-33752-0_6Analyzing GDPR compliance through the lens of privacy policy2019https://doi.org/10.1007/978-3-030-33752-0_6 With the arrival of the European Union’s General Data Protection Regulation (GDPR), several companies are making significant changes to their systems to achieve compliance. The changes range from modifying privacy policies to redesigning systems which process personal data. Privacy policy is the main medium of information dissemination between the data controller and the users. This work analyzes the privacy policies of large-scaled cloud services which seek to be GDPR compliant. We show that many services that claim compliance today do not have clear and concise privacy policies. We identify several points in the privacy policies which potentially indicate non-compliance; we term these GDPR dark patterns. We identify GDPR dark patterns in ten large-scale cloud services. Based on our analysis, we propose seven best practices for crafting GDPR privacy policies.
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✅54Gray et al. (2022)Monge Roffarello, A., & De Russis, L. (2022). Towards Understanding the Dark Patterns That Steal Our Attention. Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, 1–7. https://doi.org/10.1145/3491101.3519829Towards Understanding the Dark Patterns That Steal Our Attention2022https://doi.org/10.1145/3491101.3519829Contemporary digital services often adopt mechanisms, e.g., recommendations and infinite scrolling, that exploit users‚Äô psychological vulnerabilities to maximize time spent and daily visits. While these attention-capture dark patterns might contribute to technology overuse and problematic behaviors, they are relatively underexplored in the literature. In this paper, we first provide a definition of what are attention-capture dark patterns based on a review of recent works on digital wellbeing and dark patterns. Then, we describe a set 5 of attention-capture dark patterns extracted from a 1-week-long auto-ethnography during which we self-monitored our mobile and web interactions with Facebook and YouTube. Finally, we report on an initial study (N = 7) that explores whether and how a widespread mechanism, i.e., social investment, influence usage and users‚Äô perception of the Facebook website. We discuss the implications that our work may have on the design of technologies that better align with users‚Äô digital wellbeing.CursoryTheoryNudgep. 2: "Differently from traditional dark patterns, attention-capture mechanisms go beyond the manipulation of user interfaces, as they also include system functionality like autoplay and pull-to-refresh. They can be related to the concept of “negative nudges [12].” According to the original definition [32], nudging refers to any (subtle) changes in the “choice architecture” of a system that can alter people's behaviors in predictable ways. Traditionally, nudging envisions that our knowledge about the users’ systematic biases in decision making can be leveraged to support people in making optimal decisions. It is nowadays clear, however, that the same mechanisms and psychological vulnerabilities can be exploited against users [7]."Felicia Cordeiro, Daniel A. Epstein, Edison Thomaz, Elizabeth Bales, Arvind K. Jagannathan, Gregory D. Abowd, and James Fogarty. 2015. Barriers and Negative Nudges: Exploring Challenges in Food Journaling. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 1159–1162. https://doi.org/10.1145/2702123.2702155CursoryTheoryNudgep.2 "According to the original definition [32], nudging refers to any (subtle) changes in the “choice architecture” of a system that can alter people’s behaviors in predictable ways. "Cass Sunstein and Richard Thaler. 2008. Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.
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✅55Gray et al. (2022)Moser, C., Schoenebeck, S. Y., & Resnick, P. (2019, May 2). Impulse buying: Design practices and consumer needs. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. CHI ’19: CHI Conference on Human Factors in Computing Systems, Glasgow Scotland Uk. https://doi.org/10.1145/3290605.3300472Impulse Buying: Design Practices and Consumer Needs2019https://doi.org/10.1145/3290605.3300472E-commerce sites have an incentive to encourage impulse buying, even when not in the consumer's best interest. This study investigates what features e-commerce sites use to encourage impulse buying and what tools consumers desire to curb their online spending. We present two studies: (1) a systematic content analysis of 200 top e-commerce websites in the U.S. and (2) a survey of online impulse buyers (N=151). From Study 1, we find that e-commerce sites contain multiple features that encourage impulsive buying, including those that lower perceived risks, leverage social influence, and enhance perceived proximity to the product. Conversely, from Study 2 we find that online impulse buyers want tools that (a) encourage deliberation and avoidance, (b) enforce spending limits and postponement, (c) increase checkout effort, (d) make costs more salient, and (e) reduce product desire. These findings inform the design of "friction'' technologies that help users make more deliberative consumer choices.insufficient refererrd from oringinal theoryCoreModelModels of Self-Control"Deliberation. Models of self-control describe two systems: the reflective/planner/cool system and the impulsive/doer/hot system [57, 75, 77]."Janet Metcalfe and Walter Mischel. 1999. A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review 106, 1 (1999), 3–19. https://doi.org/10.1037/0033- 295X.106.1.3https://doi.org/10.1037/0033- 295X.106.1.3Fritz Strack, Lioba Werth, and Roland Deutsch. 2006. Reflective and Impulsive Determinants of Consumer Behavior. Journal of Con- sumer Psychology 16, 3 (Jan. 2006), 205–216. https://doi.org/10.1207/ s15327663jcp1603_2https://doi.org/10.1207/ s15327663jcp1603_2Richard H. Thaler and H. M. Shefrin. 1981. An Economic Theory of Self-Control. Journal of Political Economy 89, 2 (April 1981), 392–406. https://doi.org/10.1086/260971https://doi.org/10.1086/260971
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✅56Gray et al. (2022)Nelissen, L., & Funk, M. (2022). Rationalizing dark patterns: Examining the process of designing privacy UX through speculative enactments. International Journal of Design, 16(1), 77–94. http://ijdesign.org/index.php/IJDesign/article/view/4117Rationalizing dark patterns: Examining the process of designing privacy UX through speculative enactments2022https://ijdesign.org/index.php/IJDesign/article/view/4117Connected products and applications increasingly leverage users’ personal data in their core functions. Designing privacy-sensitive interfaces for such data-related applications is a delicate craft. There is often tension between designers and changing user perceptions of privacy, data monetization, legal requirements, and organizational power structures, often resulting in designer complicity in privacy violations. This work examines the process of designing privacy-oriented interfaces in terms of compliance, ethics, and creativity, and specifically how designers weigh competing interests in resolving an ethical conflict. We study this through a speculative enactment, ChoiceBox, in which 33 design students and professional designers explore UX design through a privacy lens with a series of fictional clients. The resulting interviews and wireframes are analyzed for Privacy UX insights. The results show a limited awareness of how legal principles affect design practice, and how some designers easily violated boundaries in terms of ethics—even their own. We show how designers are not immune to enacting and rationalizing dark patterns of Privacy UX, and how speculative enactments can be a tool to foreground crucial issues of friction and ambiguity regarding end-user privacy and data protection in design education and practice.CursoryMechanismStrategies of Persuasion"While dark patterns can be seen in design traditions of persuasive design (Fogg, 2009) "Fogg, B. (2009). A behavior model for persuasive design. In Proceedings of the 4th international conference on persuasive technology (Article no., 40). ACM. https://doi. org/10.1145/1541948.1541999https://doi. org/10.1145/1541948.1541999CursoryTheoryNudge"behavior change theories such as nudging (Thaler & Sunstein, 2008)"Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
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✅57Gray et al. (2022)Nouwens, M., Liccardi, I., Veale, M., Karger, D., & Kagal, L. (2020). 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, 1–13. https://doi.org/10.1145/3313831.3376321Dark Patterns after the GDPR: Scraping Consent Pop-Ups and Demonstrating Their Influence2020https://doi.org/10.1145/3313831.3376321New 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.CursoryTheoryNudge"As a phenomenon they are part of the larger research agenda around persuasive design [27] and nudging [1, 50]."Richard H Thaler and Cass R Sunstein. 2009. Nudge: Improving decisions about health, wealth, and happiness. Penguin.CursoryMechanismStrategies of Persuasion"As a phenomenon they are part of the larger research agenda around persuasive design [27] and nudging [1, 50]. "Brian J Fogg. 2009. A behavior model for persuasive design. In Proceedings of the 4th international Conference on Persuasive Technology. ACM, 40.
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x59Gray et al. (2022)Owens, K., Gunawan, J., Choffnes, D., Emami-Naeini, P., Kohno, T., & Roesner, F. (2022, September 29). Exploring deceptive design patterns in voice interfaces. 2022 European Symposium on Usable Security. EuroUSEC 2022: 2022 European Symposium on Usable Security, Karlsruhe Germany. https://doi.org/10.1145/3549015.3554213Exploring Deceptive Design Patterns in Voice Interfaces2022https://doi.org/10.1145/3549015.3554213Deceptive design patterns (sometimes called “dark patterns”) are user interface design elements that may trick, deceive, or mislead users into behaviors that often benefit the party implementing the design over the end user. Prior work has taxonomized, investigated, and measured the prevalence of such patterns primarily in visual user interfaces (e.g., on websites). However, as the ubiquity of voice assistants and other voice-assisted technologies increases, we must anticipate how deceptive designs will be (and indeed, are already) deployed in voice interactions. This paper makes two contributions towards characterizing and surfacing deceptive design patterns in voice interfaces. First, we make a conceptual contribution, identifying key characteristics of voice interfaces that may enable deceptive design patterns, and surfacing existing and theoretical examples of such patterns. Second, we present the findings from a scenario-based user survey with 93 participants, in which we investigate participants’ perceptions of voice interfaces that we consider to be both deceptive and non-deceptive.
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✅60Gray et al. (2022)Özdemir, Ş. (2020). Digital nudges and dark patterns: The angels and the archfiends of digital communication. Digital Scholarship in the Humanities, 35(2), 417–428. https://doi.org/10.1093/llc/fqz014Digital nudges and dark patterns: The angels and the archfiends of digital communication2020https://doi.org/10.1093/llc/fqz014Nudging is simply guiding people behaviors by the use of user-interface and design elements in digital environments. Today, many decisions are made in online environments. Gaining insights about digital nudging can greatly help communicators, policy makers, and designers lead users to make the most de- sirable choice for them and/or for the wealth of the society as well. Digital nudges can be used in many digital environments like e-mail, SMS, push notifications, mobile apps, social media, gamification, e-commerce, e-government, location services, corporate digital information systems, and many other digital interfaces that include any kind of decision-making processes. This study is a descriptive study and more of a qualitative nature and aims to identify the digital nudging concept, dark patterns, and usage of digital nudges in real-life applications. It also proposes a brief digital nudging process schema to be used for designing behav- ioral digital interventions.insufficient refererrd from oringinal theoryCoreTheoryProspect "Kahneman and Tversky (1979) claimed that decisions are not always optimal by putting forward ‘prospect theory’ which states human beings’ risk-taking willingness is highly context-dependent. People dislike losses more than they like gains (loss frame – gain frame); losses are much more painful than the same amount of gain."Kahneman, D. and Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2): 263–91.CoreTheoryNudge"‘A nudge, as we will use the term, is any aspect of the choice architecture that alters people’s behavior in a predictable way without forbid- ding any options or significantly changing their economic incentives. To count as a mere nudge, the intervention must be easy and cheap to avoid. Nudges are not mandates. Putting fruit at eye level counts as a nudge. Banning junk food does not’ (Thaler and Sunstein, 2009, s. 6)." ... "As they mentioned in their book Nudge, know- ing how people think helps us make/let them choose what is best for them, and the society (Thaler and Sunstein, 2009). The key point of nudging, as also stated by Thaler and Sunstein, is ‘knowing how people think’, which has long been searched by scholars and scientist."Thaler, R. and Sunstein, C. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. New York, NY: Penguin Books.CoreTheoryChoice Architecture"As Thaler, Sunstein, and Balz state: ‘Decision makers do not make choices in a vacuum. They make them in an environment where many features, noticed and unnoticed, can influence their decisions. The person who creates that environment is, in our termin- ology, a choice architect. The goal of Nudge is to show how choice architecture can be used to help nudge people to make better choices (as judged by themselves) without for- cing certain outcomes upon anyone, a phil- osophy we call libertarian paternalism. 　The　tools highlighted are: defaults, expecting error, understanding mappings, giving feed- back, structuring complex choices, and creat- ing incentives’ (Thaler et al., 2010).”Thaler, R., Sunstein, C., and Balz, J. (2010). Choice Architecture. https://papers.ssrn.com/sol3/papers. cfm?abstract_id1⁄41583509 (accessed 1 October 2018).https://papers.ssrn.com/sol3/papers. cfm?abstract_id1⁄41583509
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x61Gray et al. (2022)Runge, J., Wentzel, D., Huh, J. Y., & Chaney, A. (2022). “Dark patterns” in online services: a motivating study and agenda for future research. Marketing Letters. https://doi.org/10.1007/s11002-022-09629-4Dark patterns in online services: a motivating study and agenda for future research2022https://doi.org/10.1007/s11002-022-09629-4Some companies offering online services employ tactics that make it hard for cus- tomers to quit their accounts. These tactics are commonly referred to as “dark pat- terns” and may include hiding the cancelation procedure, asking customers to go through an excessive number of steps to complete the cancelation, or simply not letting customers quit their accounts straight away. Arguably, dark patterns are the result of misaligned incentives between companies and customers as companies can still benefit from their customers’ data even if they no longer use the companies’ ser- vices. Against this background, the authors conduct an observational survey of the state of current market practice and call for future research that enhances our under- standing of dark patterns, their organizational antecedents, customers’ psychological responses to these tactics, and the wider consequences of dark patterns for firms and markets.
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✅62Gray et al. (2022)Schaffner, B. (2022). Understanding account deletion and relevant dark patterns on social media. Proceedings of the ACM on Human-Computer InteractionVolume 6,Issue CSCW2 November 2022 Article No.: 417. https://airlab.cs.uchicago.edu/files/2022/06/PREPRINT_Understanding_Account_Deletion_CSCW2022-1.pdfUnderstanding Account Deletion and Relevant Dark Patterns On Social Media2022https://doi.org/10.1145/3555142Social media users may wish to delete their accounts, but it is unclear if this process is easy to complete or if users understand what happens to their account data after deletion. Furthermore, since platforms profit from users’ data and activity, they have incentives to maintain active users, possibly affecting what account deletion options are offered. To investigate these issues, we conducted a two-part study. In Study Part 1, we created and deleted accounts on the top 20 social media platforms in the United States and performed an analysis of 490 deletion-related screens across these platforms. In Study Part 2, informed by our interface analysis, we surveyed 200 social media users to understand how users perceive and experience social media account deletion. From these studies, we have four main findings. First, account deletion options vary considerably across platforms and the language used to describe these options is not always clear. Most platforms offer account deletion on desktop browsers but not all allow account deletion from mobile apps or browsers. Second, we found evidence of several dark patterns present in the account deletion interfaces and platform policies. Third, most participants had tried to delete at least one social media account, yet over one-third of deletion attempts were never completed. Fourth, users mostly agreed that they did not want platforms to have access to deleted account data. Based on these results, we recommend that platforms improve the terminology used in account deletion interfaces so the outcomes of account deletion are more clear to users. Additionally, we recommend that platforms allow users to delete their social media accounts from any device they use to access the platform. Finally, future work is needed to assess how users are affected by account deletion related dark patterns.CoreTheoryChoice Architecture"For instance, behavioral economics research shows that choice architects can control the environment to influence people’s decisions by using the idea that the automatic systems overpower the reflective systems of human decision-making [99]"Richard H Thaler, Cass R Sunstein, and John P Balz. 2013. Choice architecture. In The behavioral foundations of public policy. Princeton University Press, 428–439.
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✅64Gray et al. (2022)Sin, R., Harris, T., Nilsson, S., & Beck, T. (2022). Dark patterns in online shopping: do they work and can nudges help mitigate impulse buying? Behavioural Public Policy , 1–27. https://doi.org/10.1017/bpp.2022.11Dark patterns in online shopping: do they work and can nudges help mitigate impulse buying?2022https://doi.org/10.1017/bpp.2022.11Dark patterns – design interfaces or features that subtly manipulate people in making sub- optimal decisions – are ubiquitous especially in e-commerce websites. Yet, there is little research on the effectiveness of dark patterns, and even lesser studies on testing interven- tions that can help mitigate their influence on consumers. To that end, we conducted two experiments. The first experiment tests the effectiveness of different dark patterns within a hypothetical single product online shopping context. Results show that, indeed, dark pat- terns increase the purchase impulsivity across all dark patterns, relative to the control. The second experiment tests the effectiveness of three behaviorally informed interventions on four different dark patterns also in a hypothetical online shopping scenario, but this time offering multiple products instead of a single product. Between-subject analysis shows that not all interventions are equally effective, with uneven impact across dark patterns. However, within-subject results indicate that all interventions significantly reduce pur- chase impulsivity pre- versus post-intervention, indicating that any intervention is better than none when it comes to combating dark patterns. We then end by discussing the pol- icy implications of our results.insufficient refererrd from oringinal theoryCoreTheoryDual Process " From a behavioral science perspective, dark patterns are designed to prompt consumers to evoke System 1 thinking rather than a more deliberate and thoughtful System 2 think- ing by exploiting cognitive biases like scarcity bias or social proof (Stanovich & West, 2000; Kahneman, 2011). "Stanovich, K. E. and R. F. West (2000), ‘Individual differences in reasoning: Implications for the rationality debate?’ Behavioral and Brain Sciences, 23(5): 645–665. https://doi.org/10.1017/S0140525X00003435.https://doi.org/10.1017/S0140525X00003435.Kahneman, D. (2011). Thinking, fast and slow (1st ed.). NY: Farrar, Straus and Giroux.
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x65Gray et al. (2022)Singh, A., Arun, A., Malhotra, P., Desur, P., Jain, A., Chau, D. H., & Kumaraguru, P. (2022). Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play. Proceedings of the 33rd ACM Conference on Hypertext and Social Media, 186–191. https://doi.org/10.1145/3511095.3536368Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play2022https://doi.org/10.1145/3511095.3536368Google Play‚Äôs policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. However, there still exist apps that incentivize installs for other apps on the platform. To understand how install-incentivizing apps affect users, we examine their ecosystem through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We perform qualitative analysis of reviews to reveal various types of dark patterns that developers incorporate in install-incentivizing apps, highlighting their normative concerns at both user and platform levels. Permissions requested by these apps validate our discovery of dark patterns, with over 92% apps accessing sensitive user information. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers. Our proposed reconfiguration of a state-of-the-art microcluster anomaly detection algorithm yields promising preliminary results in detecting this fraud. We discover highly significant lockstep behaviors exhibited by reviews that aim to boost the overall rating of an install-incentivizing app. Upon evaluating the 50 most suspicious clusters of boosting reviews detected by the algorithm, we find (i) near-identical pairs of reviews across 94% (47 clusters), and (ii) over 35% (1,687 of 4,717 reviews) present in the same form near-identical pairs within their cluster. Finally, we conclude with a discussion on how fraud is intertwined with labor and poses a threat to the trust and transparency of Google Play.
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x66Gray et al. (2022)Soe, T. H., Nordberg, O. E., Guribye, F., & Slavkovik, M. (2020). Circumvention by design - dark patterns in cookie consent for online news outlets. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (pp. 1–12). Association for Computing Machinery. https://doi.org/10.1145/3419249.3420132Circumvention by Design - Dark Patterns in Cookie Consent for Online News Outlets2020https://doi.org/10.1145/3419249.3420132To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union‚Äôs General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up. We have manually analyzed 300 data collection consent notices from news outlets that are built to ensure compliance with GDPR. The analysis uncovered a variety of strategies or dark patterns that circumvent the intent of GDPR by design. We further study the presence and variety of these dark patterns in these ‚Äúcookie consents‚Äù and use our observations to specify the concept of dark pattern in the context of consent elicitation.
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x69Gray et al. (2022)Tahaei, M., & Vaniea, K. (2021). “Developers Are Responsible”: What Ad Networks Tell Developers About Privacy. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3411763.3451805Developers Are Responsible: What Ad Networks Tell Developers About Privacy2021https://doi.org/10.1145/3411763.3451805Advertising 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 privacy-unfriendly defaults laced with dark patterns to nudge developers‚Äô 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.
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x70Gray et al. (2022)Tiangpanich, P., & Nimkoompai, A. (2022). An Analysis of Differences between Dark Pattern and Anti-Pattern to Increase Efficiency Application Design. 2022 7th International Conference on Business and Industrial Research (ICBIR), 416–421. https://doi.org/10.1109/ICBIR54589.2022.9786470An Analysis of Differences between Dark Pattern and Anti-Pattern to Increase Efficiency Application Design2022https://doi.org/10.1109/ICBIR54589.2022.9786470As technology has grown faster than expected, marketing is starting to change from offline marketing to social media marketing. Therefore, marketers have begun to use mobile applications to represent their brands and products to customers. However, to make it attractive, the user experience (UX) needs to step in to create the communication between brands and customers smoothly and use the user interface (UI) to represent it in simple and understandable ways. Nowadays information is principal for marketing. Lead to an organization trying to extract users' personal information without their consent, resulting in damage to the property of users via the Dark pattern of User Experience (UX). However, when designers design the application, they might notice that the design is having trouble or not having a good solution to deal with the problem that users might find. The design experience that can solve this problem is called Anti-patterns, in which approaches to common issues might appear obvious but are less than optimal in practice. While the dark pattern of user experience (UX) is deceptive, UX/UI design or inter-actions created with psychological knowledge is designed to mislead users to do something they did not intend to create value for the service that employs them. This research aims to create a solution for designers to use the right tools to create compelling artwork. Anti-patterns and dark patterns to understand and apply appropriately.
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x71Gray et al. (2022)Toth, M., Bielova, N., & Roca, V. (2022). On dark patterns and manipulation of website publishers by CMPs. 22nd Privacy Enhancing Technologies Symposium (PETS 2022), Jul 2022, Sydney / Virtual, Australia. hal-03577024On dark patterns and manipulation of website publishers by CMPs2022https://hal.inria.fr/hal-03577024Web technologies and services widely rely on data collection via tracking users on websites. In the EU, the collection of such data requires user con- sent thanks to the ePrivacy Directive (ePD), and the General Data Protection Regulation (GDPR). To com- ply with these regulations and integrate consent collec- tion into their websites, website publishers often rely on third-party contractors, called Consent Management Providers (CMPs), that provide consent pop-ups as a service. Since the GDPR came in force in May 2018, the presence of CMPs continuously increased. In our work, we systematically study the installation and con- figuration process of consent pop-ups and their poten- tial effects on the decision making of the website pub- lishers. We make an in-depth analysis of the configu- ration process from ten services provided by five pop- ular CMP companies and identify common unethical design choices employed. By analysing CMP services on an empty experimental website, we identify manipula- tion of website publishers towards subscription to the CMPs paid plans and then determine that default con- sent pop-ups often violate the law. We also show that configuration options may lead to non-compliance, while tracking scanners offered by CMPs manipulate publish- ers. Our findings demonstrate the importance of CMPs and design space offered to website publishers, and we raise concerns around the privileged position of CMPs and their strategies influencing website publishers.
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x72Gray et al. (2022)Trice, M., & Potts, L. (2018). Building dark patterns into platforms: How GamerGate perturbed Twitter’s user experience. Present Tense: A Journal of Rhetoric in Society, 6(3). https://www.researchgate.net/profile/Michael-Trice/publication/337155548_Building_Dark_Patterns_into_Platforms_How_GamerGate_Perturbed_Twitter’s_User_Experience/links/5dc86bc692851c8180435536/Building-Dark-Patterns-into-Platforms-How-GamerGate-Perturbed-Twitters-User-Experience.pdfBuilding dark patterns into platforms: How GamerGate perturbed Twitter's user experience2018https://www.presenttensejournal.org/volume-6/building-dark-patterns-into-platforms-how-gamergate-perturbed-twitters-user-experience/n this article, we examine how GamerGate trapped both its unwilling targets and willing participants in an unending cycle of rhetorical invention through a mechanism of aggressive, hostile, mob-like activism (Ames 48; Massanari 334; Mortensen 4-5). Influential elements within GamerGate, we argue, specifically subverted the functionality of Twitter as a corporate media platform in order to test a variety of loosely connected arguments to see what would resonate within whatever aggrieved audience the GamerGate collective could find. We have selected GamerGate specifically due to the manner in which the activists subverted the use of Twitter, GitHub, and other online services. In fact, we argue that GamerGate subverted media platforms like Twitter, GitHub, Reddit, and others to become components within an activist supraplatform, a system of interconnected platforms where the higher level platform reshapes the rhetorical purpose and context of the component parts. It is the higher level abstraction of GamerGate as activist supraplatform across multiple media platforms that this article will focus upon.
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✅73Gray et al. (2022)Utz, C., Degeling, M., Fahl, S., Schaub, F., & Holz, T. (2019). (Un)informed Consent. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. https://doi.org/10.1145/3319535.3354212(Un)Informed Consent: Studying GDPR Consent Notices in the Field2019https://doi.org/10.1145/3319535.3354212Since the adoption of the General Data Protection Regulation (GDPR) in May 2018 more than 60 % of popular websites in Europe display cookie consent notices to their visitors. This has quickly led to users becoming fatigued with privacy notifications and contributed to the rise of both browser extensions that block these banners and demands for a solution that bundles consent across multiple websites or in the browser. In this work, we identify common properties of the graphical user interface of consent notices and conduct three experiments with more than 80,000 unique users on a German website to investigate the influence of notice position, type of choice, and content framing on consent. We find that users are more likely to interact with a notice shown in the lower (left) part of the screen. Given a binary choice, more users are willing to accept tracking compared to mechanisms that require them to allow cookie use for each category or company individually. We also show that the wide-spread practice of nudging has a large effect on the choices users make. Our experiments show that seemingly small implementation decisions can substantially impact whether and how people interact with consent notices. Our findings demonstrate the importance for regulation to not just require consent, but also provide clear requirements or guidance for how this consent has to be obtained in order to ensure that users can make free and informed choices.CursoryTheoryChoice Architecture"Prior work has shown that the design and architecture of choices heavily influences people’s de- cisions [43, 50]"Richard H. Thaler and Cass R. Sunstein. 2009. Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin Books, New York, NY, USA.Markus Weinmann, Christoph Schneider, and Jan vom Brocke. 2016. Digital Nudging. Business & Information Systems Engineering 58, 6 (Dec. 2016), 433–436. https://doi.org/10.1007/s12599- 016- 0453- 1
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✅74Gray et al. (2022)van Nimwegen, C., & de Wit, J. (2022). Shopping in the Dark. Human-Computer Interaction. User Experience and Behavior, 462–475. https://doi.org/10.1007/978-3-031-05412-9_32Shopping in the Dark2022https://doi.org/10.1007/978-3-031-05412-9_32Dark patterns are user interfaces designed to trick users into doing things they might not otherwise do. Human psychological insights are carefully exploited by designers to craft these patterns. This study investigates the rela- tion between dark pattern recognition and platform choice. An experiment was designed in which 54 participants performed a shopping task. In the website dif- ferent dark pattern types were implemented, such as “Sneak into Basket”, “Toying with emotions” and “Trick Questions”. Results showed that mobile users are twice as likely to fall for one of the patterns. In addition, a significant correlation was found between falling for that same dark pattern and the age of users. The older the user, the more chance of falling for that pattern. Lastly it showed that the higher the website’s “honesty” is rated, the higher the “navigability” is rated.CoreMechanismStrategies of Persuasion"In the context of interaction design, [9] views persuasive tech- nology as “designing for behavior as something we cause to occur and/or preventing a target behavior from happening”." ..."However, [9] warns that persuasive technology is a controversial topic and that there should be awareness of its negative applications"Fogg,B.:Computersaspersuasivesocialactors.Persuas.Technol.89–120(2003)
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x75Gray et al. (2022)Voigt, C., Schlögl, S., & Groth, A. (2021). Dark Patterns in Online Shopping: of Sneaky Tricks, Perceived Annoyance and Respective Brand Trust. HCI in Business, Government and Organizations, 143–155. https://doi.org/10.1007/978-3-030-77750-0_10Dark patterns in online shopping: Of sneaky tricks, perceived annoyance and respective brand trust2021https://doi.org/10.1007/978-3-030-77750-0_10Dark patterns utilize interface elements to trick users into performing unwanted actions. Online shopping websites often employ these manipulative mechanisms so as to increase their potential cus- tomer base, to boost their sales, or to optimize their advertising efforts. Although dark patterns are often successful, they clearly inhibit posi- tive user experiences. Particularly, with respect to customers’ perceived annoyance and trust put into a given brand, they may have negative effects. To investigate respective connections between the use of dark patterns, users’ perceived level of annoyance and their expressed brand trust, we conducted an experiment-based survey. We implemented two versions of a fictitious online shop; i.e. one which used five different types of dark patterns and a similar one without such manipulative user inter- face elements. A total of n = 204 participants were then forwarded to one of the two shops (approx. 2/3 to the shop which used the dark patterns) and asked to buy a specific product. Subsequently, we measured partic- ipants’ perceived annoyance level, their expressed brand trust and their affinity for technology. Results show a higher level of perceived annoy- ance with those who used the dark pattern version of the online shop. Also, we found a significant connection between perceived annoyance and participants’ expressed brand trust. A connection between participants’ affinity for technology and their ability to recognize and consequently counter dark patterns, however, is not supported by our data.
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x77Gray et al. (2022)Widdicks, K., Pargman, D., & Bjork, S. (2020). Backfiring and favouring: how design processes in HCI lead to anti-patterns and repentant designers. Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, 1–12. https://doi.org/10.1145/3419249.3420175Backfiring and Favouring: How Design Processes in HCI Lead to Anti-Patterns and Repentant Designers2020https://doi.org/10.1145/3419249.3420175Design is typically envisioned as aiming to improve situations for users, but this can fail. Failure can be the result of flawed design solutions, i.e. anti-patterns. Prior work in anti-patterns has largely focused on their characteristics. We instead concentrate on why they occur by outlining two processes that result in anti-patterns: 1) backfiring, and 2) favouring. The purpose of the paper is to help designers and researchers better understand how design processes can lead to negative impacts and to repentant designers by introducing a richer vocabulary for discussing such processes. We explore how anti-patterns evolve in HCI by specifically applying the vocabulary to examples of social media design. We believe that highlighting these processes will help the HCI community reflect on their own work and also raise awareness of the opportunities for avoiding anti-patterns. Our hope is that this will result in fewer negative experiences for designers and users alike.
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x78Gray et al. (2022)Zagal, J. P., Björk, S., & Lewis, C. (2013). Dark Patterns in the Design of Games. Foundations of Digital Games. http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1043332&dswid=1018Dark patterns in the design of games2013http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1043332&dswid=-6191Game designers are typically regarded as advocates for players. However, a game creator’s interests may not align with the players’. We examine some of the ways in which those opposed interests can manifest in a game’s design. In particular, we examine those elements of a game’s design whose purpose can be argued as questionable and perhaps even unethical. Building upon earlier work in design patterns, we call these abstracted elements Dark Game Design Patterns. In this paper, we develop the concept of dark design patterns in games, present examples of such patterns, explore some of the subtleties involved in identifying them, and provide questions that can be asked to help guide in the specification and identification of future Dark Patterns. Our goal is not to criticize creators but rather to contribute to an ongoing discussion regarding the values in games and the role that designers and creators have in this process.
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x79Gray et al. (2022)Zeng, E., Wei, M., Gregersen, T., Kohno, T., & Roesner, F. (2021). Polls, clickbait, and commemorative $2 bills: problematic political advertising on news and media websites around the 2020 U.S. elections. Proceedings of the 21st ACM Internet Measurement Conference, 507–525. https://doi.org/10.1145/3487552.3487850Polls, Clickbait, and Commemorative $2 Bills: Problematic Political Advertising on News and Media Websites around the 2020 U.S. Elections2021https://doi.org/10.1145/3487552.3487850Online advertising can be used to mislead, deceive, and manipulate Internet users, and political advertising is no exception. In this paper, we present a measurement study of online advertising around the 2020 United States elections, with a focus on identifying dark patterns and other potentially problematic content in political advertising. We scraped ad content on 745 news and media websites from six geographic locations in the U.S. from September 2020 to January 2021, collecting 1.4 million ads. We perform a systematic qualitative analysis of political content in these ads, as well as a quantitative analysis of the distribution of political ads on different types of websites. Our findings reveal the widespread use of problematic tactics in political ads, such as bait-and-switch ads formatted as opinion polls to entice users to click, the use of political controversy by content farms for clickbait, and the more frequent occurrence of political ads on highly partisan news websites. We make policy recommendations for online political advertising, including greater scrutiny of non-official political ads and comprehensive standards across advertising platforms.
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✅80ACMQunfang Wu, Yisi Sang, Dakuo Wang, and Zhicong Lu. 2023. Malicious Selling Strategies in Livestream E-commerce: A Case Study of Alibaba’s Taobao and ByteDance’s TikTok. ACM Trans. Comput.-Hum. Interact. 30, 3, Article 35 (June 2023), 29 pages. https://doi.org/10.1145/3577199Malicious Selling Strategies in Livestream E-Commerce: A Case Study of Alibaba’s Taobao and ByteDance’s TikTok2023https://doi.org/10.1145/3577199Due to the limitations imposed by the COVID-19 pandemic, customers have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, various streamers’ malicious selling behaviors have been reported. In this research, we sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao, and TikTok. We identified 16 malicious selling strategies that were used to deceive, coerce, or manipulate viewers and found that platform designs enhanced nine of the malicious selling strategies. Second, through an interview study with 13 viewers, we report three challenges of overcoming malicious selling in relation to imbalanced power between viewers, streamers, and the platforms. We conclude by discussing the policy and design implications of countering malicious selling.insufficient refererrd from oringinal theoryCursoryTheoryPersuasion "These selling strategies were based on persuasion theory [19]"Robert B Cialdini. 2009. Influence: Science and practice. Vol. 4. Pearson education Boston, MA.CoreTheoryGrounded Theory"The first and second authors conducted an open coding process derived from the grounded theory method [74]. They coded the transcripts at the sentence-level independently."Anselm Strauss and Juliet Corbin. 1994. Grounded theory methodology: An overview. (1994).
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✅81ACMMonica Kowalczyk, Johanna T. Gunawan, David Choffnes, Daniel J Dubois, Woodrow Hartzog, and Christo Wilson. 2023. Understanding Dark Patterns in Home IoT Devices. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 179, 1–27. https://doi.org/10.1145/3544548.3581432Understanding Dark Patterns in Home IoT Devices 2023https://doi.org/10.1145/3544548.3581432Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study.insufficient refererrd from oringinal theoryCursoryTheoryNudgep. 2: "Conceptually, dark patterns relate to malicious interfaces [ 25 ], online manipulation [93], nudges [94 ], and UX design [ 47 ]. Dark patterns have received public attention in the press [ 58, 73, 84 ], scholarly and regulatory workshops [ 21 , 59, 79 ], and government reports [24 , 33 , 34]."Richard H. Thaler and Cass R. Sunstein. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press.
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x83ACMRené Schäfer, Paul Miles Preuschoff, and Jan Borchers. 2023. Investigating Visual Countermeasures Against Dark Patterns in User Interfaces. In Proceedings of Mensch und Computer 2023 (MuC '23). Association for Computing Machinery, New York, NY, USA, 161–172. https://doi.org/10.1145/3603555.3603563Investigating Visual Countermeasures Against Dark Patterns in User Interfaces2023https://doi.org/10.1145/3603555.3603563Dark patterns are malicious interface design strategies on the web and in apps that trick users into decisions that go against their best interests, costing them money, time, or private data. While there are approaches to classifying these patterns and investigating user awareness, there has been little work looking into visual countermeasures against dark patterns. In this work, we used an online survey to investigate concepts for six visual countermeasures against three common dark patterns: Confirmshaming, Low-stock Message, and Visual Interference. Our results indicate two opposing forces for users: On the one hand, users dislike systems actively making silent changes to their screen, preferring to be informed about the presence of dark patterns. On the other hand, they do not want applications to become visually cluttered, as this may impact their productivity. We found that different applications of dark patterns require different countermeasures, and that individual preferences vary strongly.
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x86ACMMildner, Thomas and Freye, Merle and Savino, Gian-Luca and Doyle, Philip R. and Cowan, Benjamin R. and Malaka, RainerDefending Against the Dark Arts: Recognising Dark Patterns in Social Media - Proceedings of the 2023 ACM Designing Interactive Systems Conference2023https://doi.org/10.1145/3563657.3595964Interest in unethical user interfaces has grown in HCI over recent years, with researchers identifying malicious design strategies referred to as “dark patterns”. While such strategies have been described in numerous domains, we lack a thorough understanding of how they operate in social networking services (SNSs). Pivoting towards regulations against such practices, we address this gap by offering novel insights into the types of dark patterns deployed in SNSs and people’s ability to recognise them across four widely used mobile SNS applications. Following a cognitive walkthrough, experts (N = 6) could identify instances of dark patterns in all four SNSs, including co-occurrences. Based on the results, we designed a novel rating procedure for evaluating the malice of interfaces. Our evaluation shows that regular users (N = 193) could differentiate between interfaces featuring dark patterns and those without. Such rating procedures could support policymakers’ current moves to regulate deceptive and manipulative designs in online interfaces.insufficient refererrd from oringinal theory
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x87ACMHidaka, Shun and Kobuki, Sota and Watanabe, Mizuki and Seaborn, KatieLinguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps2023https://doi.org/10.1145/3544548.3580942Dark patterns are deceptive and malicious properties of user interfaces that lead the end-user to do something different from intended or expected. While now a key topic in critical computing, most work has been conducted in Western contexts. Japan, with its booming app market, is a relatively uncharted context that offers culturally- and linguistically-sensitive differences in design standards, contexts of use, values, and language, all of which could influence the presence and expression of dark patterns. In this work, we analyzed 200 popular mobile apps in the Japanese market. We found that most apps had dark patterns, with an average of 3.9 per app. We also identified a new class of dark pattern: “Linguistic Dead-Ends” in the forms of “Untranslation” and “Alphabet Soup.” We outline the implications for design and research practice, especially for future cross-cultural research on dark patterns.
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✅88ACMThomas Mildner, Gian-Luca Savino, Philip R. Doyle, Benjamin R. Cowan, and Rainer Malaka. 2023. About Engaging and Governing Strategies: A Thematic Analysis of Dark Patterns in Social Networking Services. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 192, 1–15. https://doi.org/10.1145/3544548.3580695About Engaging and Governing Strategies: A Thematic Analysis of Dark Patterns in Social Networking Services - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems2023https://doi.org/10.1145/3544548.3580695Research in HCI has shown a growing interest in unethical design practices across numerous domains, often referred to as “dark patterns”. There is, however, a gap in related literature regarding social networking services (SNSs). In this context, studies emphasise a lack of users’ self-determination regarding control over personal data and time spent on SNSs. We collected over 16 hours of screen recordings from Facebook’s, Instagram’s, TikTok’s, and Twitter’s mobile applications to understand how dark patterns manifest in these SNSs. For this task, we turned towards HCI experts to mitigate possible difficulties of non-expert participants in recognising dark patterns, as prior studies have noticed. Supported by the recordings, two authors of this paper conducted a thematic analysis based on previously described taxonomies, manually classifying the recorded material while delivering two key findings: We observed which instances occur in SNSs and identified two strategies — engaging and governing — with five dark patterns undiscovered before.CursoryTheoryProxemics "Elsewhere, Greenberg et al. [20] consider dark patterns in con- junction with proxemics theory [23]"Edward T. Hall. 1966. The hidden dimension. Doubleday, Garden City, N.Y.
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x89ACMChen, Jieshan and Sun, Jiamou and Feng, Sidong and Xing, Zhenchang and Lu, Qinghua and Xu, Xiwei and Chen, ChunyangUnveiling the Tricks: Automated Detection of Dark Patterns in Mobile Applications - Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology2023https://doi.org/10.1145/3586183.3606783Mobile apps bring us many conveniences, such as online shopping and communication, but some use malicious designs called dark patterns to trick users into doing things that are not in their best interest. Many works have been done to summarize the taxonomy of these patterns and some have tried to mitigate the problems through various techniques. However, these techniques are either time-consuming, not generalisable or limited to specific patterns. To address these issues, we propose UIGuard, a knowledge-driven system that utilizes computer vision and natural language pattern matching to automatically detect a wide range of dark patterns in mobile UIs. Our system relieves the need for manually creating rules for each new UI/app and covers more types with superior performance. In detail, we integrated existing taxonomies into a consistent one, conducted a characteristic analysis and distilled knowledge from real-world examples and the taxonomy. Our UIGuard consists of two components, Property Extraction and Knowledge-Driven Dark Pattern Checker. We collected the first dark pattern dataset, which contains 4,999 benign UIs and 1,353 malicious UIs of 1,660 instances spanning 1,023 mobile apps. Our system achieves a superior performance in detecting dark patterns (micro averages: 0.82 in precision, 0.77 in recall, 0.79 in F1 score). A user study involving 58 participants further showed that UIGuard significantly increases users’ knowledge of dark patterns. We demonstrated potential use cases of our work, which can benefit different stakeholders, and serve as a training tool for raising awareness of dark patterns.
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x91ACMOwens, Kentrell and Gunawan, Johanna and Choffnes, David and Emami-Naeini, Pardis and Kohno, Tadayoshi and Roesner, FranziskaExploring Deceptive Design Patterns in Voice Interfaces - Proceedings of the 2022 European Symposium on Usable Security2022https://doi.org/10.1145/3549015.3554213Deceptive design patterns (sometimes called “dark patterns”) are user interface design elements that may trick, deceive, or mislead users into behaviors that often benefit the party implementing the design over the end user. Prior work has taxonomized, investigated, and measured the prevalence of such patterns primarily in visual user interfaces (e.g., on websites). However, as the ubiquity of voice assistants and other voice-assisted technologies increases, we must anticipate how deceptive designs will be (and indeed, are already) deployed in voice interactions. This paper makes two contributions towards characterizing and surfacing deceptive design patterns in voice interfaces. First, we make a conceptual contribution, identifying key characteristics of voice interfaces that may enable deceptive design patterns, and surfacing existing and theoretical examples of such patterns. Second, we present the findings from a scenario-based user survey with 93 participants, in which we investigate participants’ perceptions of voice interfaces that we consider to be both deceptive and non-deceptive.
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x92ACMGundelach, Ralf and Herrmann, DominikCookiescanner: An Automated Tool for Detecting and Evaluating GDPR Consent Notices on Websites - Proceedings of the 18th International Conference on Availability, Reliability and Security2023https://doi.org/10.1145/3600160.3605000The 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.
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✅93ACMThomas Mildner, Philip Doyle, Gian-Luca Savino, and Rainer Malaka. 2022. Rules Of Engagement: Levelling Up To Combat Unethical CUI Design. In Proceedings of the 4th Conference on Conversational User Interfaces (CUI '22). Association for Computing Machinery, New York, NY, USA, Article 26, 1–5. https://doi.org/10.1145/3543829.3544528Rules Of Engagement: Levelling Up To Combat Unethical CUI Design - Proceedings of the 4th Conference on Conversational User Interfaces2022https://doi.org/10.1145/3543829.3544528While a central goal of HCI has always been to create and develop interfaces that are easy to use, a deeper focus has been set more recently on designing interfaces more ethically. However, the exact meaning and measurement of ethical design has yet to be established both within the CUI community and among HCI researchers more broadly. In this provocation paper we propose a simplified methodology to assess interfaces based on five dimensions taken from prior research on so-called dark patterns. As a result, our approach offers a numeric score to its users representing the manipulative nature of evaluated interfaces. It is hoped that the approach - which draws a distinction between persuasion and manipulative design, and focuses on how the latter functions rather than how it manifests - will provide a viable way for quantifying instances of unethical interface design that will prove useful to researchers, regulators and potentially even users.CursoryTheoryProxemics "Elsewhere, Greenberg et al. [13] consider dark patterns in conjunction with proxemics theory [14]"Edward T. Hall. 1966. The hidden dimension. Doubleday, Garden City, N.YCoreTheoryNudge"Thaler and Sunstein provide an example of this type of design technique when introducing the term of Nudges [17] to describe interventions that alternate peoples’ decision making process in a predictable way, allowing design to be used to navigate a users’ focus into a predefined direction or goal. ThomasC.Leonard.2008.RichardH.Thaler,CassR.Sunstein,Nudge:Improving decisions about health, wealth, and happiness. Constitutional Political Economy 19, 4 (Dec. 2008), 356–360. https://doi.org/10.1007/s10602-008-9056-2https://doi.org/10.1007/s10602-008-9056-2CoreModelMental Model"This is a common general understanding of design in HCI research, and echoes widely known basic principles for ensuring alignment between a user’s mental model and a system forwarded by Norman in ’The Design of Everyday Things’ [22 ]."Donald A. Norman. 2002. The design of everyday things. Basic Books, [New York].
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✅95ACMIshita Chordia, Lena-Phuong Tran, Tala Tayebi, Emily Parrish, Sheena Erete, Jason Yip, and Alexis Hiniker. 2023. Deceptive Design Patterns in Safety Technologies: A Case Study of the Citizen App. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 18 pages. https: //doi.org/10.1145/3544548.3581258Deceptive Design Patterns in Safety Technologies: A Case Study of the Citizen App - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems2023https://doi.org/10.1145/3544548.3581258Deceptive design patterns (known as dark patterns) are interface characteristics which modify users’ choice architecture to gain users’ attention, data, and money. Deceptive design patterns have yet to be documented in safety technologies despite evidence that designers of safety technologies make decisions that can powerfully influence user behavior. To address this gap, we conduct a case study of the Citizen app, a commercially available technology which notifies users about local safety incidents. We bound our study to Atlanta and triangulate interview data with an analysis of the user interface. Our results indicate that Citizen heightens users’ anxiety about safety while encouraging the use of profit-generating features which offer security. These findings contribute to an emerging conversation about how deceptive design patterns interact with sociocultural factors to produce deceptive infrastructure. We propose the need to expand an existing taxonomy of harm to include emotional load and social injustice and offer recommendations for designers interested in dismantling the deceptive infrastructure of safety technologies.insufficient refererrd from oringinal theoryCoreTheorySocial Control"In contrast to the victimization theory, the social control theory focused on the community and the informal and formal controls in place to deter crime [68, 92]"Dan A Lewis and Greta Salem. 2017. Community crime prevention: An analysis of a developing strategy. The Fear of Crime (2017), 507–523.Robert J Sampson. 1988. Local friendship ties and community attachment in mass society: A multilevel systemic model. American sociological review (1988), 766–779.
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x96ACMEghtebas, Chloe and Klinker, Gudrun and Boll, Susanne and Koelle, MarionCo-Speculating on Dark Scenarios and Unintended Consequences of a Ubiquitous(Ly) Augmented Reality - Proceedings of the 2023 ACM Designing Interactive Systems Conference2023https://doi.org/10.1145/3563657.3596073The vision of a ‘metaverse’ may soon bring a ubiquitous(ly) Augmented Reality (UAR) delivering context-aware, geo-located, and continuous blends of real and virtual elements into reach. This paper draws on speculative design to explore, question, and problematize consequences of AR becoming pervasive. Elaborating on Desjardin et al.’s bespoke booklets, we co-speculate together with 12 globally dispersed participants. Each participant received a custom-made design workbook containing pictures of their immediate surroundings, which they elaborated on in situated brainstorming activities. We present an integration of their speculative ideas and lived experiences in 3 overarching themes from which 7 ‘dark’ scenarios caused by UAR were formed. The Scenarios are indicative of deceptive design patterns that can (and likely will be) devised to misuse UAR, and anti-patterns that could cause unintended consequences. These contributions enable the timely discussion of potential antidotes and to which extent they can mitigate imminent harms of UAR.insufficient refererrd from oringinal theory
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✅97ACMBongard-Blanchy, Kerstin and Sterckx, Jean-Louis and Rossi, Arianna and Sergeeva, Anastasia and Koenig, Vincent and Rivas, Salvador and Distler, VerenaAnalysing the Influence of Loss-Gain Framing on Data Disclosure Behaviour: A Study on the Use Case of App Permission Requests - Proceedings of the 2023 European Symposium on Usable Security2023https://doi.org/10.1145/3617072.3617108This paper examines the effect of the dark pattern strategy “loss-gain framing” on users’ data disclosure behaviour in mobile settings. Understanding whether framing influences users’ willingness to disclose personal information is important to (i) determine if and how this technique can subvert consent and other privacy decisions, (ii) prevent abuse with appropriate policies and sanctions, and (iii) provide clear evidence-based guidelines for app privacy engineering. We conducted an online user study (N=848), in which we varied the framing of app permission requests (i.e., positive, negative, or neutral framing) and examined its impact on participants’ willingness to accept the permission, their evaluation of the trustworthiness of the request and their perception of being informed by it. Our findings reveal effects on disclosure behaviour for request types that users cannot easily understand. In this case, negative framing makes users more likely to disclose personal information. Contrary to our expectations, positive framing reduces disclosure rates, possibly because it raises users’ suspicion. We discuss implications for the design of interfaces that aim to facilitate informed, privacy-enhancing decision-making.CursoryTheoryProspect "Indeed, the design of the prompts induces “goal framing” which, according to Levin et al. [36], is subject to loss aversion for which Tversky and Kahneman saw choice reversal Figure 10: Steps where uncertainty can arise and play a part in app disclosure behaviour. behaviour [63]."Amos Tversky and Daniel Kahneman. 1981. The Framing of Decisions and the Psychology of Choice. Science 211, 4481 (1981), 453–458. https://doi.org/10.1126/ science.7455683 arXiv:https://www.science.org/doi/pdf/10.1126/science.7455683https://www.science.org/doi/pdf/10.1126/science.7455683
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✅98ACMKyi, Lin and Ammanaghatta Shivakumar, Sushil and Santos, Cristiana Teixeira and Roesner, Franziska and Zufall, Frederike and Biega, Asia J.Investigating Deceptive Design in GDPR’s Legitimate Interest - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems2023https://doi.org/10.1145/3544548.3580637Legitimate interest is one of the six grounds for processing data under the European Union’s General Data Protection Regulation (GDPR). The flexibility and ambiguity of the term "legitimate interests" can be problematic; coupled with the lack of enforcement from legal authorities and different interpretations from the various data protection authorities, legitimate interests can be taken advantage of as a loophole to collect more user data. Drawing insights from multiple disciplines, we ran two studies to empirically investigate the deceptive designs being used when legitimate interests are applied in privacy notices, and how user perceptions line up with these practices. We identified six deceptive designs, and found that the ways legitimate interest is applied in practice does not match user expectations.CursoryTheoryNudge"Paternalism is the idea that UI designs should nudge, or influence, users into making decisions that are better for them [4, 69]. "Adrien Barton and Till Grüne-Yanoff. 2015. From libertarian paternalism to nudging—and beyond. Review of Philosophy and Psychology 6, 3 (2015), 341–359.Richard H Thaler and Cass R Sunstein. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press.
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x99ACMFreeman, Guo and Wu, Karen and Nower, Nicholas and Wohn, Donghee YvettePay to Win or Pay to Cheat: How Players of Competitive Online Games Perceive Fairness of In-Game Purchases2022https://doi.org/10.1145/3549510The advent of various in-game purchasing systems has led to several ethical concerns in contemporary gaming ecosystems, including the monetary dark patterns in game design and the potential harms on gamer welling by introducing cheating, gambling, and addictive mechanisms. These concerns have resulted in the rise of tensions regarding the impacts of in-game purchases on players who pay versus those who do not pay, such as their perceptions of "fairness" in highly competitive gaming contexts when spending is involved. Using 2,685 Reddit posts from five subreddits of popular online sports and card games that focus on player-to-player competition, we investigate how players of these games perceive fairness of their in-game purchases. This research expands our existing knowledge on ethical concerns and fairness in gaming by highlighting consumers' (players') diverse ethical judgments regarding the increasingly popular monetization mechanisms in modern gaming. It also highlights ethical dilemmas surrounding competition, spending, and enjoyment in online gaming and informs the design of future digital consumption systems for fairer, healthier, and more ethical gaming dynamics.
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x100ACMKeleher, Maxwell and Westin, Fiona and Nagab and i, Preethi and Chiasson, SoniaHow Well Do Experts Understand End-Users’ Perceptions of Manipulative Patterns? - Nordic Human-Computer Interaction Conference2022https://doi.org/10.1145/3546155.3546656How well do experts understand end-users’ perceptions of manipulative patterns? We conducted online surveys with end-users and with experts assessing perceptions of manipulative patterns. Participants saw images of interfaces and evaluated each through a series of semantic scales (e.g., deceitful to honest). After being shown a definition of manipulative patterns, they then decided whether each interface exemplified a manipulative pattern. End-users correctly identified images as manipulative approximately half of the time, and though experts were more often correct, the differences were not statistically significant. However, end-users’ descriptions of the images were significantly more positive than experts assumed, resulting in experts over-estimating end-users’ ability to recognize when they were being manipulated by an interface.
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✅101Google ScholarKim, Kawon Kathy and Kim, Woo Gon and Lee, MinwooImpact of dark patterns on consumers’ perceived fairness and attitude: Moderating effects of types of dark patterns, social proof, and moral identity2023https://doi.org/10.1016/j.tourman.2023.104763Despite the likely prevalence of deceptive dark patterns tactics in the tourism industry, specifically in online travel agencies (OTA), there is a dearth of dark patterns research in tourism literature. This study offers comprehensive knowledge regarding dark patterns tactics and how they influence consumers’ perceived fairness and their attitude toward OTAs. Two separate scenario-based experimental studies were conducted to test the moderating effects of social proof and types of dark patterns tactics as well as moral identity. Drawing on social influence and social proof theories, the authors demonstrate the moderating effect of social proof on the relationship between dark patterns practices and perceived fairness and attitude toward OTAs. Under the low stock message condition, the influence of deception on fairness and attitude perception was greater under negative social proof in comparison to a positive one. The results demonstrate that deception interacts with moral identity to influence fairness and attitude, confirming the moderating role of moral identity.CoreTheorySocial Influence"Furthermore, social influence theory states that individuals develop their own opinion on the basis of the group's agreement (Fromkin, 1970). Accordingly, obtaining social consensus from others may have a compelling effect that individuals use to mitigate their inner conflicts (Xu & Schwarz, 2009). If people perceive that they are gaining social approval from others for a negative situation, they are more likely to forgive the negative consequences and accept the situation (Raghunathan & Corfman, 2006)."Fromkin, H. L. (1970). Effects of experimentally aroused feelings of undistinctiveness upon valuation of scarce and novel experiences. Journal of personality and social psychology, 16(3), 521.CorePrincipleSocial Proof"According to the principle of social proof, people determine proper behavior for themselves by examining the behavior or responses of others existing in the same situation (Goethals & Darley, 1977). Therefore, the critical source of information for social proof is the responses of referent others (Cialdini, Wosinska, Barrett, Butner, & Gornik-Durose, 1999)."Goethals, G. R., & Darley, J. M. (1977). Social comparison theory: An attributional approach. Social comparison processes: Theoretical and empirical perspectives, 259-278.CorePrincipleDual Entitlement"The principle of dual entitlement explains the process of how consumers perceive fairness toward business pricing strategy (Kahneman, Knetsch, & Thaler, 1986). According to the dual entitlement principle, most customers think that they are entitled to a reasonable price and that firms are entitled to a reasonable profit (Kimes & Wirtz, 2002)."Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1986). Fairness and the assumptions of economics. Journal of business, S285-S300.CoreTheoryTheory of Planned Behaviour"According to the theory of planned behavior (Ajzen, 1985, pp. 11–39), attitude is the individual's positive or negative evaluation of performing the particular behavior of interest. When an individual holds a positive evaluation (i.e., positive attitude) toward a specific behavior, he/she intends to perform the behavior in a positive way. However, when an individual evaluates that behavior negatively, such an attitude has a negative impact on behavior intention."Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control: From cognition to behavior (pp. 11-39). Berlin, Heidelberg: Springer Berlin Heidelberg.
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✅102Google ScholarKollmer, T., Eckhardt, A. Dark Patterns. Bus Inf Syst Eng 65, 201–208 (2023). https://doi.org/10.1007/s12599-022-00783-7Dark Patterns: Conceptualization and Future Research Directions2023https://link.springer.com/article/10.1007/s12599-022-00783-7noneCoreTheoryNudge"The term nudging was first introduced in behavioral economics by Thaler and Sunstein (2008), who define it as ‘‘any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives’’ (Thaler and Sunstein 2008)." " Dark patterns (also referred to as deceptive designs) deceive (Narayanan et al. 2020) and manipulate (Westin and Chiasson 2021) users using elements of the choice architecture, which is defined as structure and presentation of choices (Thaler and Sunstein 2008), and the exploitation of psychological vulnerabilities (Mathur et al. 2021)." ; Sunstein 2015) to ‘‘maximize the good of the nudgee, as judged by the nudgee him- or herself’’ (Renaud and Zimmermann 2018), which can be achieved by promoting user autonomy and informed choices (Sunstein 2015). ---- Besides slowing and extending users’ time for task completion, digital sludging also induces unwanted side effects, such as an increased cognitive load, to manipulate users’ choices (Thaler 2018).Thaler RH, Sunstein CR (2008) Nudge: improving decisions about health, wealth, and happiness. Penguin.Sunstein CR (2015) The ethics of nudging. Yale J Reg 32:413CursoryTheoryChoice Architecture"Dark patterns (also referred to as deceptive designs) deceive (Narayanan et al. 2020) and manipulate (Westin and Chiasson 2021) users using elements of the choice architecture, which is defined as structure and presentation of choices (Thaler and Sunstein 2008)"Thaler RH, Sunstein CR (2008) Nudge: improving decisions about health, wealth, and happiness. PenguinCursoryTheorySludge"Besides slowing and extending users’ time for task com- pletion, digital sludging also induces unwanted side effects, such as an increased cognitive load, to manipulate users’ choices (Thaler 2018)"Thaler RH (2018) Nudge, not sludge. Sci 361(6401):431
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✅103Google ScholarMills S, Whittle R, Ahmed R, Walsh T, Wessel M. Dark patterns and sludge audits: an integrated approach. Behavioural Public Policy. Published online 2023:1-27. doi:10.1017/bpp.2023.24Dark patterns and sludge audits: an integrated approach2023https://doi.org/10.1017/bpp.2023.24Dark patterns are user interface design elements which harm users but benefit vendors. These harms have led to growing interest from several stakeholders, including policymakers. We develop a high-level analytical framework – the dark patterns auditing framework (DPAF) – to support policymaker efforts concerning dark patterns. There are growing links between dark patterns and the behavioural science concept of sludge. We examine both literatures, noting several worthwhile similarities and important conceptual differences. Using two ‘sludge audits,’ and the DPAF, we examine 14 large online services to provide a high-level review of the user experience of these services. Our approach allows policymakers to identify areas of the user ‘journey’ (dark paths) where sludge/dark patterns persist. For regulators with constrained resources, such an approach more be advantageous when planning more granular analyses. Our approach also reveals several important limitations, notably, within some of the tools for sludge auditing which we develop, such as the ‘equal clicks principle.’ We discuss these limitations and directions for future research.CoreTheoryNudge"These behavioural concepts generally reject the labels of coer- cion or manipulation, and instead advocate as a core ethical principle one’s freedom of choice (e.g., Thaler and Sunstein, 2008; Sunstein, 2016; Schmidt and Engelen, 2020; Lades and Delaney, 2022)." "Forced action – in terms of free- dom of choice – stands out as a wholly unacceptable technique in a free society and is certainly a distinctly different means of ‘influencing’ an individual, compared to, say, nudging (Thaler and Sunstein, 2008)." "Sludge audits have been proposed by Sunstein (2019, 2022) as a means of assessing and removing behavioural impediments to decision-makers – what has been dubbed sludge (Thaler, 2018)." "We draw inspiration from several notions expressed in Thaler’s (2018, 2021) dis- cussion of sludge."Thaler, R. H. and C. R. Sunstein (2008), Nudge: Improving Decisions about Health, Wealth, and Happiness. UK: Penguin Books.Thaler, R. H. (2018), ‘Nudge, not sludge’, Science, 361(6401): 431.Thaler, R. H. (2021), ‘Nudge: the Final Edition. LSE Online Event’, London School of Economics and Political Science. https://www.youtube.com/watch?v=FEkfqQAp6wk [24 January 2022].CoreTheoryDual Process "Firstly, dark patterns typically try to encourage users to only engage so-called System 1 thinking – the fast, intuitive mode of cognition described under dual-processing theory (e.g., Kahneman, 2003, 2011)."Kahneman, D. (2003), ‘Maps of bounded rationality: psychology for behavioral economics’, The American Economic Review, 93(5): 1449–1475.Kahneman, D. (2011), Thinking, Fast and Slow, UK: Penguin Books.
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✅104Google ScholarAhuja, S., Kumar, J. Conceptualizations of user autonomy within the normative evaluation of dark patterns. Ethics Inf Technol 24, 52 (2022). https://doi.org/10.1007/s10676-022-09672-9Conceptualizations of user autonomy within the normative evaluation of dark patterns2022https://doi.org/10.1007/s10676-022-09672-9Dark patterns have received significant attention in literature as interface design practices which undermine users’ autonomy by coercing, misleading or manipulating their decision making and behavior. Individual autonomy has been argued to be one of the normative lenses for the evaluation of dark patterns. However, theoretical perspectives on autonomy have not been sufficiently adapted in literature to identify the ethical concerns raised by dark patterns. The aim of this paper is to conceptualize user autonomy within the context of dark patterns. In this paper, we systematically review 151 dark patterns from 16 taxonomies to understand how dark patterns threaten users’ autonomy. We demonstrate through this analysis that implications for autonomy arise along four dimensions, because autonomy itself can be understood as subsuming several distinguishable concepts. These are agency, freedom of choice, control and independence. We argue that an assessment of whether a design pattern qualifies as ‘dark’ should account for the sense in which autonomy is threatened, as individuals’ rights and expectations of autonomy vary in various contexts and depend upon the interpretation of autonomy. This paper aims to contribute to the development of the normative lens of individual autonomy for the evaluation of dark patterns, as well as for persuasive design more broadly.CoreTheorySelf-Determination "According to self-determination theory, autonomy is one of the three innate psychological needs of humans (Ryan & Deci, 2000). Deci & Ryan (2000) argued that the sense of autonomy creates a heightened sense of motivation and positive emotions. "Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68.CoreTheoryPsychological Reactance "Conversely, the psychological reactance theory suggests that restrictions on one’s freedom elicit a state of negative arousal (Brehm, 1966; Steindl et al., 2015). André et al. (2018) outlined the benefits of experiencing a sense of autonomy for positive affect, satisfaction, overall well-being and better life outcomes."Brehm, J. W. (1966). A theory of psychological reactance. Academic Press.Steindl, C., Jonas, E., Sittenthaler, S., Traut-Mattausch, E., & Greenberg, J. (2015). Understanding psychological reactance. Zeitschrift für Psychologie, 223(4), 205–214. https://doi.org/10.1027/2151-2604/a000222CursoryModelPersuasion Knowledge "When consumers become aware of and accustomed to persuasive techniques, it provides them with an opportunity to adjust their behavior, which partially nullifies their influence (Friestad & Wright, 1994). "Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31.
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✅106Google ScholarKoh, W. C., & Seah, Y. Z. (2023). Unintended consumption: The effects of four e-commerce dark patterns. Cleaner and Responsible Consumption, 11, 100145.Unintended consumption: The effects of four e-commerce dark patterns2023https://doi.org/10.1016/j.clrc.2023.100145Dark patterns, the manipulation and deliberate presentation of information to influence consumer decision-making, can lead to unintended purchases and overconsumption. Past studies have established the prevalence of dark patterns in e-commerce. Yet, few studies have investigated the differential effects of dark patterns on the consumption choices of different groups of consumers. Similarly, few studies have examined the effectiveness of interventions countering dark patterns which may reduce unintended consumption. This study seeks to 1) investigate the effects of low-stock message, activity message, countdown timer, and limited-time message dark patterns and its influence on product selection decisions, 2) determine if demographic variables would predict susceptibility to dark patterns, and 3) explore the effectiveness of video- and activity-based dark patterns awareness intervention. 195 adult volunteers aged 19 to 53 participated in this experiment. Results indicated that participants were significantly more likely to select products with dark patterns. The use of limited-time message dark pattern was significantly more effective than other dark patterns in inducing consumption. Older individuals tend to be more susceptible to dark patterns. No clear evidence for the effectiveness of video- and activity-based dark patterns awareness intervention was found. Several study limitations were noted. We note how companies can support cleaner and responsible consumption by refraining from using dark patterns. On individuals, the implications of dark patterns on unintended consumption, in a world with aging populations and post-pandemic accelerated e-commerce were discussed.CoreTheoryNudge"Recent research into cleaner and responsible consumption has looked into the use of nudges, defined as “liberty-preserving approaches that steer people in particular directions, but that also allow them to go their own way” (Sunstein, 2014, p. 583), for “positive” interventions (e. g., how to encourage sustainable washing machine use; Visser and Schoormans, 2023). For the journal Cleaner and Responsible Consumption, there are at least 12 articles with the search term “nudge”."Sunstein, C.R., 2014. Nudging: a very short guide. J. Consum. Pol. 37, 583–588. https:// doi.org/10.1007/s10603-014-9273-1. United States Federal Trade Commission, 2023. FTC Takes Action. Against Publishers.https:// doi.org/10.1007/s10603-014-9273-1.
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✅107Google ScholarYuwen Lu, Chao Zhang, Yuewen Yang, Yaxing Yao, and Toby Jia-Jun Li. 2023. From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX. Proc. ACM Hum.-Comput. Interact. 6, CSCW, Article 1 (November 2023), 41 pagesFrom Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX2023https://arxiv.org/pdf/2310.17846.pdfThe study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer’s perspective on education and advocacy for ethical designs; and (2) the policymaker’s perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users’ needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users’ awareness of and action on dark patterns being empowered in a realistic in-situ setting.CoreTheoryProtection-Motivation "Guided by the Protection-Motivation Theory (PMT) [106], we coined two types of intervention for our end-user-empowerment approach, targeting users’ awareness and action. First, we enhance awareness by increasing transparency about the presence and impacts of dark patterns."Ronald W. Rogers. 1975. A protection motivation theory of fear appeals and attitude change. The journal of psychology 91, 1 (1975), 93–114. Publisher: Taylor & Francis.
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x109Google ScholarLacey, Cherie and Beattie, Alex and Sparks, TristamClusters of Dark Patterns Across Popular Websites in New Zealand2023“Dark patterns” are interface design techniques that aim to trick or mislead Internet users. Most dark-patterns research has been undertaken in the United States and Europe and by user experience or human computer interaction researchers. In this study, we adopt a media and communication studies and science and technology studies approach to investigate where dark patterns “cluster” in online environments. A walkthrough of the top 100 New Zealand websites leads us to the following findings: (1) dark patterns cluster around financial transactions; (2) the most common types of dark patterns constitute a form of interface interference; and (3) dark patterns are often deployed as mechanisms to drive revenue, facilitate customer surveillance, and reduce business operations costs, and appear to be largely imported from overseas markets.
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x111Google ScholarY. Yada, J. Feng, T. Matsumoto, N. Fukushima, F. Kido and H. Yamana, "Dark patterns in e-commerce: a dataset and its baseline evaluations," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 3015-3022, doi: 10.1109/BigData55660.2022.10020800.Dark patterns in e-commerce: a dataset and its baseline evaluations - 2022 IEEE International Conference on Big Data (Big Data)2022https://doi.org/10.1109/BigData55660.2022.10020800Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.’s study in 2019 [1], which consists of 1,818 dark pattern texts from shopping sites. Then, we added negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.’s dataset. We also applied state-of-the-art machine learning methods to show the automatic detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975 with RoBERTa. The dataset and baseline source codes are available at https://github.com/yamanalab/ec-darkpattern.
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✅112Google ScholarKollmer, Tim; Hauser, Alessa; Oberhofer, Viviana; Blossey, Gregor; and Eckhardt, Andreas, "Uncovering Drivers for the Integration of Dark Patterns in Conversational Agents" (2023). Wirtschaftsinformatik 2023 Proceedings. 6. https://aisel.aisnet.org/wi2023/6Uncovering Drivers for the Integration of Dark Patterns in Conversational Agents2023https://aisel.aisnet.org/wi2023/6/Today, organizations increasingly utilize conversational agents (CAs), which are smart technologies that converse in a human-to-human interaction style. CAs are very effective in guiding users through digital environments. However, this makes them natural targets for dark patterns, which are user interface design elements that infringe on user autonomy by fostering uninformed decisions. Integrating dark patterns in CAs has tremendous impacts on supposedly free user choices in the digital space. Thus, we conducted a qualitative study consisting of semi-structured interviews with developers to investigate drivers of dark patterns in CAs. Our findings reveal that six drivers for the implementation of dark patterns exist. The technical drivers include heavy guidance of CAs during the conversation and the CAs' data collection potential. Additionally, organizational drivers are assertive stakeholder dominance and time pressure during the development process. Team drivers incorporate a deficient user understanding and an inexperienced team.CursoryTheoryNudge"Dark patterns subconsciously target the automatic system to alter individuals' choices (Thaler and Sunstein, 2008)"Thaler, R. H. and Sunstein, C. R. (2008). Nudge: improving decisions about health. Wealth, and Happiness, 6, 14-38.
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✅113Google ScholarSousa, Carla and Oliveira, AnaThe Dark Side of Fun: Understanding Dark Patterns and Literacy Needs in Early Childhood Mobile Gaming - European Conference on Games Based Learning2023https://doi.org/10.34190/ecgbl.17.1.1656Play has always been recognized as an essential aspect of human development, particularly during early childhood, as it contributes to learning, the formation of meanings, and experiencing the world. In today's digitalized society, early childhood education has increasingly integrated digital media into its practices, both in schools and at the family level. Mobile digital games (MDG) have received significant attention due to their impact on children's interactions, play, and learning. However, as young children engage more with MDGs, concerns about problem gaming have arisen, referring to conflicts and issues that emerge from game playing within everyday sociocultural contexts. Scholars such as Zagal et al. (2013) have identified certain game design patterns as "dark", which can be considered unethical as they manipulate players against their best interests. Given the prevalence of mobile gaming in early childhood, studying these dark patterns becomes even more crucial. This study aims to investigate the presence of dark patterns in MDG for young children (0-5 years old), through qualitative analysis. The five most popular free games for this age range on App Store (February 2023) were analysed, particularly focusing on the presence of temporal, monetary, social, and/or psychological dark patterns. The analysis uncovers the presence of temporal, monetary, and psychological dark patterns, including aesthetic manipulations, paywalls, and periodic rewards resembling gambling elements. The games also employ advertising strategies and engagement tactics that challenge young children's navigation. Parental control mechanisms offer limited safeguards, requiring continuous monitoring and parental involvement in play dynamics. The study highlights the importance of adult media and digital literacy in supporting children's online play effectively, while also emphasising the responsibility of game designers and developers to create healthier and less risky game experiences.No theory but theoryCoreTheoryCognitive Development"According to Piaget's theory of cognitive development, play facilitates the construction of knowledge by allowing children to actively explore their environment, manipulate objects, and engage in imaginative and symbolic play scenarios. Through play, children develop critical cognitive skills such as problem-solving, decision-making, and abstract thinking (Piaget, 1962)"Piaget, J. (1962). Play, dreams, and imitation in childhood. Norton.
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x115Google ScholarChen, Dawei and Hahn, Jungpil, "The Economic Implications of Privacy Dark Patterns (PDPs)" (2023). Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023. 1. https://aisel.aisnet.org/icis2023/cyber_security/cyber_security/1The Economic Implications of Privacy Dark Patterns (PDPs)2023https://aisel.aisnet.org/icis2023/cyber_security/cyber_security/1/This study investigates the economic implications of privacy dark patterns (PDPs) through which firms ``wisely'' play privacy games. It is believed that PDPs advantage firms by deceiving consumers. However, it could also hinder firms' credibility. Thus, we aim to examine whether PDPs always benefit firms and hurt consumers. We also seek to answer whether market forces are sufficient to keep PDPs at relatively low levels. Our results show that PDPs make users weakly worse off and the seller weakly better off. Nevertheless, the seller has incentives to not utilize any PDPs when users' privacy cost is high, and the ratio of privacy concern and search cost is either too high or too low under which market shrinkage effect dominates market division effect. Finally, we show that a welfare maximizing social planner would allow the presence of PDPs when users' privacy cost is sufficiently low.
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x116Google ScholarA. Nimkoompai, "Risk Analysis of Encountering Dark Patterns of UX E-commerce Applications Affecting Personal Data," 2022 6th International Conference on Information Technology (InCIT), Nonthaburi, Thailand, 2022, pp. 115-119, doi: 10.1109/InCIT56086.2022.10067640.Risk Analysis of Encountering Dark Patterns of UX E-commerce Applications Affecting Personal Data2022https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10067640Much attention is paid to personal data protection on the digital world in the form of the PDPA, which concurs with attention to designs called nowadays as the ‘‘Dark Patterns of UX especially in e-commerce. In this work, the focus is on mobile applications because the users are more convenient with mobile applications. The researcher collected data from potentiallyvulnerable, relevant samples, along with dark patterns data from various sources to derive a conclusion for future awareness campaign. The study revealed that 58.3% of the sampled group did not know dark patterns. The top misleading dark patterns that were commonly used by designers were Forced continuity (71.8%), Disguised ads (59.3%). The riskiest dark patterns was found to be risk of disclosing personal data in apps that force the user to pay prior to usage. The gathered data showed that many users were not familiar with dark patterns, which put them at risk of incurring damage or infringement of personal data.
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✅118Google ScholarLi, Danyang (2022) "The FTC and the CPRA’s Regulation of Dark Patterns in Cookie Consent Notices," The University of Chicago Business Law Review: Vol. 1: No. 1, Article 19. Available at: https://chicagounbound.uchicago.edu/ucblr/vol1/iss1/19The FTC and the CPRA’s Regulation of Dark Patterns in Cookie Consent Notices2022https://chicagounbound.uchicago.edu/ucblr/vol1/iss1/19Dark patterns are designed to confuse and manipulate users to select the option preferred by website owners. Dark patterns are especially prevalent in cookie consent notices, which are notices that websites display to inquire users regarding their cookie preferences. Cookies are often used by websites to track and store user information for functional and marketing purposes. Dark patterns exploit various psychological biases, and the interaction among the biases will likely exacerbate their effects. This Article examines 100 cookie consent notices from the most popular ecommerce websites in the United States and offers a set of empirical data on the current landscape of dark patterns in cookie consent notices. Based on our results and analysis, most cookie consent notices we examined are likely considered unfair and deceptive under Section 5 of the FTC Act. Moreover, under the CPRA legal framework, most notices are also considered coercive and manipulative. Future regulators should focus on the design of online consent mechanisms to better protect consumer interest in privacy.CoreTheoryNudge"Thaler and Sunstein defined a nudge as “any aspect of the choice architecture that alters peo- ple’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives.”"Richard H. Thaler & Cass R. Sunstein, Libertarian Paternalism, 93 AM. ECON. REV. 175 (2003).CursoryTheoryDual Process "Dark patterns can induce users to make irrational choices be- cause they prompt users to use System 1 decision-making, which relies on impulse and heuristics, instead of System 2, which involves deliberate thinking. Under System 1 decision-making, people will usually operate automatically and make quick judg- ments with almost no voluntary control.23 System 2 allows people to allocate their attention to their complex and deliberate deci- sion-making.24 Dark patterns exploit System 1 decision-making and tempt users to make decisions quickly and unconsciously."Daniel Kahneman, Of 2 Minds: How Fast and Slow Thinking Shape Perception and Choice [Excerpt], SCI. AM. (June 15, 2012)https://perma.cc/DE6W-279K.
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✅119Google ScholarBarros, L., Klein, T., Shchepetova, A., & Hogg, T. (2022). The rise of dark patterns: does competition law make it any brighter?. Competition Law Journal, 21(3), 136-144. Retrieved Dec 8, 2023, from https://doi.org/10.4337/clj.2022.03.06The rise of dark patterns: does competition law make it any brighter?2022https://doi.org/10.4337/clj.2022.03.06Dark patterns are deceptive online interface designs that may nudge users into making decisions that are in the interest of the online business at the expense of the user. This article considers the economics behind dark patterns: what are they, what can economics teach us regarding how they work, how is digitalization changing the economics behind dark patterns and to what extent can competition and consumer protection laws solve the problems that arise?CoreTheoryNudge"For example, healthy food options can be displayed more prominently within a canteen or supermarket in order to promote a healthier lifestyle. This is often referred to as ’nudging’, a term which was popularized by Richard Thaler and Cass Sunstein; Thaler received the 2017 Nobel Prize in Economic Sciences for his con- tributions to behavioural economics.23"R. Thaler and C. Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness (Yale University Press, 2008) R. Thaler and C. Sunstein, Nudge: The Final Edition (Penguin Books, 2021). L. Fields, ‘The science of misbehaving: Richard Thaler wins the Nobel Prize’, Agenda (October 2017), available at: https://www.oxera.com/ insights/agenda/articles/the-science-of-misbehaving-richard-thaler-wins- the-nobel-prize (accessed 24 November 2022).R. Thaler, ‘Nudge, not sludge’ (2018) 361(6401) Science 431–431.
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✅120Google ScholarJennifer Klütsch, Christian Böffel, Sophia von Salm-Hoogstraeten, and Sabine J. Schlittmeier. 2023. Defeating Dark Patterns: The Impact of Supporting Information on Dark Patterns and Cookie Privacy Decisions. In Proceedings of the International Symposium on Technikpsychologie. https://doi.org/10.2478/9788366675896-004Defeating Dark Patterns: The impact of supporting information on dark patterns and cookie privacy decisions2023https://sciendo.com/chapter/9788366675896/10.2478/9788366675896-004In our daily usage of websites, we face a multitude of privacy decisions. However, as users often lack the knowl -edge, motivation or time for these decisions, they are vulnerable to privacy-unfriendly designs, so-called dark pat-terns. To promote deliberate cookie decisions even in presence of dark patterns, the present study investigates,within an experimental online-study (N = 207), the effectiveness of a knowledge intervention. During cookie deci-sions, either a knowledge intervention in the form of decision-supporting information (“cookie assistance”) orno such information (instead an add) were presented. The intervention effects on deliberation, reaction time andcookie activation were investigated, in the presence and absence of dark patterns. Results indicate that supportinginformation neither stimulated more deliberate and slower reactions nor encouraged participants to make privacy-friendlier decisions in the presence of dark patterns. Instead, evidence for highly conditioned decision-making wasfound. Practical implications for future privacy-interventions and legal regulations are discussedCoreTheoryNudge"Dark patterns are one type of nudge (Acquisti et al., 2017) in which a nudge describe “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives” (Thaler & Sunstein, 2008, p. 6). "Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. CoreTheoryDual Process "The Dual Process Model by Kahneman (2003) is a framework that can be used to explain how dark patterns impact decision making." " Derived from the Dual Process Model by Kahneman (2003), dark patterns could influence users’ privacy decisions due to automatic System 1 processing (Bösch et al., 2016)." "Deliberation was measured through six items on a Likert scale from 1 (I do not agree at all) to 7 (I totally agree). As the Dual Process Model distinguishes System 1 and System 2 processing (Kahneman, 2003)"Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449– 1475. https://doi.org/10.1257/000282803322655392CursoryTheoryPrivacy Calculus"This could be due to the fact, that users face difficulties in balancing perceived risks (e.g., release of personal data) and benefits (e.g., social affiliation, discounts) of privacy decisions equally, as assumed by the privacy calculus theory by Culnan and Armstrong (1999, see, Barth & de Jong, 2017; Kokolakis, 2017, for a review)."Culnan, M. J., & Armstrong, P. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104-115.https://doi.org/10.1287/orsc.10.1.104
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x122Google ScholarXian Wang, Lik-Hang Lee, Carlos Bermejo Fernandez & Pan Hui (2023) The Dark Side of Augmented Reality: Exploring Manipulative Designs in AR, International Journal of Human–Computer Interaction, DOI: 10.1080/10447318.2023.2188799The Dark Side of Augmented Reality: Exploring Manipulative Designs in AR2023https://doi.org/10.48550/arXiv.2303.02843Augmented Reality (AR) applications are becoming more mainstream, with successful examples in the mobile environment like Pokemon GO. Current malicious techniques can exploit these environments' immersive and mixed nature (physical-virtual) to trick users into providing more personal information, i.e., dark patterns. Dark patterns are deceiving techniques (e.g., interface tricks) designed to influence individuals' behavioural decisions. However, there are few studies regarding dark patterns' potential issues in AR environments. In this work, using scenario construction to build our prototypes, we investigate the potential future approaches that dark patterns can have. We use VR mockups in our user study to analyze the effects of dark patterns in AR. Our study indicates that dark patterns are effective in immersive scenarios, and the use of novel techniques such as `haptic grabbing' to drag participants' attention can influence their movements. Finally, we discuss the impact of such malicious techniques and what techniques can mitigate them.
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x124Google ScholarD. Kirkman, K. Vaniea and D. W. Woods, "DarkDialogs: Automated detection of 10 dark patterns on cookie dialogs," 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), Delft, Netherlands, 2023, pp. 847-867, doi: 10.1109/EuroSP57164.2023.00055.DarkDialogs: Automated detection of 10 dark patterns on cookie dialogs2023https://ieeexplore.ieee.org/document/10190482In theory, consent dialogs allow users to express privacy preferences regarding how a website and its partners process the user’s personal data. In reality, dialogs often employ subtle design techniques known as dark patterns that nudge users towards accepting more data processing than the user would otherwise accept. Dark patterns undermine user autonomy and can violate privacy laws. We build a system, DarkDialogs, that automatically extracts arbitrary consent dialogs from a website and detects the presence of 10 dark patterns. Evaluating DarkDialogs against a hand-labelled dataset reveals it extracts dialogs with an accuracy of 98.7% and correctly classifies 99% of the studied dark patterns. We deployed DarkDialogs on a sample of 10,992 websites, where it successfully collected 2,417 consent dialogs and found 3,744 different dark patterns automatically present on the consent dialogs. We then test whether dark pattern prevalence is associated with each of: the website’s popularity, the presence of a third-party consent management provider, and the number of ID-like cookies.