Understanding the Behavioral Effects of Dark Pattern Designs on User Disclosuresby@escholar

Understanding the Behavioral Effects of Dark Pattern Designs on User Disclosures

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The discussion delves into the intricate effects of dark pattern designs on user behavior, particularly regarding privacy concerns and disclosures. It reveals how age disparities influence responses to these designs, with older adults exhibiting heightened awareness but succumbing to behavioral nudges. Design implications underscore the need for ethical considerations and transparency to counteract dark patterns' influence on user decision-making
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(1) Reza Ghaiumy Anaraky, New York University;

(2) Byron Lowens;

(3) Yao Li;

(4) Kaileigh A. Byrne;

(5) Marten Risius;

(6) Xinru Page;

(7) Pamela Wisniewski;

(8) Masoumeh Soleimani;

(9) Morteza Soltani;

(10) Bart Knijnenburg.

Abstract & Introduction


Research Framework




Limitations and Future Work

Conclusion & References


6 Discussion

In this section, we first discuss the main effects of dark pattern design strategies on users’ disclosures and privacy concerns (RQ1). We then discuss how dark pattern designs influence older adults differently compared to younger adults (RQ2). Finally, we conclude the discussion section by presenting the design implications of this work.

6.1 The Impact of Dark Pattern Designs on Disclosure Behaviors

In this study, we did not find support for H1 and therefore did not find privacy concerns significantly influence the tagging decision. This finding confirms the privacy paradox theory that individuals’ disclosure behaviors are not necessarily inline with their self-reported privacy concerns [8]. However, we should consider that the effect of privacy concerns on disclosure was not studied in isolation. We had framing and default nudges present in the decision scenario which, indeed, significantly influenced users’ decisions (H2, H3). It is possible that concern for privacy was not a determining factor for users in the presence of such dark pattern design interventions. As an implication of our RQ1, future studies should focus more on dark pattern designs, even more so than privacy concerns, as dark pattern designs have a stronger effect on users’ disclosure behaviors. We see that even raising privacy concern does not translate to privacy protective behaviors.

6.2 The Impact of Dark Pattern Designs on Privacy Concern

Despite many privacy frameworks depicting privacy concerns as a dynamic and very fluid concept [2, 61, 63], the majority of the privacy literature has studied privacy concerns as a static variable predicting disclosure [73, 97]. We studied privacy concerns as a dynamic result of design patterns. Our results suggest that concern for privacy is not necessarily a static trait, and rather can change in response to the design patterns. Specifically, negative justifications can decrease users’ privacy concerns. A possible explanation is that showing a negative justification makes users feel that the app is more sincere. This , in turn, lowers levels of privacy concern. For example, showing a negative normative justification is an indication of low popularity of a product and is an uncommon practice and induces lowers levels of privacy concern.

6.3 Dark Pattern Designs May Disproportionately and Negatively Impact Older Adults

In terms of RQ2, we studied the the difference between older and younger adults privacy concerns and disclosures. In addition, we studied the moderating effects of age. While older and younger adults had similar privacy concerns, our results show that an opt-out default dark pattern alerts older adult users and makes them privacy-cautious. Using opt-out defaults is one of the most common means of data collection by firms [30]. It is possible that our older adult participants had more experience and familiarity with the default mechanisms and, therefore, an opt-out default led them to be more privacy-cautious.

With regards to the effects of age on tagging decisions, we found that older adults were more likely to use the tagging feature. This is in-line with some previous findings in the literature on older adults disclosing more data [71]. In addition to this main effect of age on decision, we found that the framing effect is a stronger nudge in pushing older adults to use the tagging feature when compared to younger adults. Likewise, we found opt-out defaults to be a stronger nudge for older adult participants; however, the moderation effect with defaults did not reach the significant thresholds (p = 0.062). These effects may be explained by the literature on loss-aversion. Losses are weighed more heavily than gains and so individuals put forth more effort to avoid losses than to acquire gains [88, 89]. An opt-out default can trigger an instant endowment for the user, where the tagging disclosure is seen as something they have [20, 51]. Therefore, changing the default is being perceived as a loss and individuals are more likely to keep the default option [36, 37]. Likewise, a positive framing endows individuals with the benefits of disclosure, but foregoing disclosure is perceived as a loss [30, 32]. This is further supported by the psychology literature which suggests that older adults are generally more lossadverse than young adults [16, 18, 31, 52]. Scholars have found that older adults are willing to take more risks [52] or exert more effort [16] to avoid a loss, in comparison with young adults. Therefore, our framing and default manipulations triggered a loss-aversion process which influenced older adults more than younger adults.

6.4 Implications for Design

Our study has several design implications. A key finding is that while using opt-out defaults increases older adults’ privacy concerns, it still ends up increasing their disclosure levels. This goes counter to the common perception about older adults having low privacy awareness, since they identify an opt-out dark pattern design—even more so than younger adults—and become privacy-couscous. However, these dark pattern interventions had stronger behavioral effects than any heightened privacy concerns. Therefore, instead of efforts to make individuals privacy-couscous and increase individuals’ privacy concerns, hoping for them to take privacy protective measures, it may be more effective to focus on how to counter dark design patterns. This might even include developing policies that discourage or regulate the use of dark design patterns.

We also found that older adults may be more amenable to framing and default nudges due to their loss-aversive nature. This result is a call to technology developers to be mindful of their older adult audiences and take on the ethical responsibility of creating technologies that avoid such nudges. In fact, prior research suggests that older adults may choose not to use technology as a result of high privacy concerns [42]. While the opt-out default increased disclosure in this study, it is conceivable that having to make a plethora of lossaversive decisions could push privacy concerns beyond a threshold where older adults decide to stop using technology altogether. Further research is needed to investigate this, but in the meantime, product designers should be conscientious towards their older adult users and not increase their concerns.

Furthermore, while it may seem counter intuitive, if product designers are honest about the negative aspects of their products, especially the low adoption of their features, it may actually alleviate concerns. Our negative justifications manipulation proved to reduce privacy concerns. Being honest seems to be the best policy for gaining consumer confidence. Finally, Designers can use various methods such as graph-based models to assess the potential impact of their design choices on user behavior and ensure that they are not inadvertently creating dark patterns [74, 76, 77].

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.