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      From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX

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          Abstract

          The 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.

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          The preregistration revolution

          Progress in science relies in part on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning, such as hindsight bias, make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan before observing the research outcomes—a process called preregistration. Preregistration distinguishes analyses and outcomes that result from predictions from those that result from postdictions. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are preexisting. Services are now available for preregistration across all disciplines, facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.
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            Yielding to Temptation: Self‐Control Failure, Impulsive Purchasing, and Consumer Behavior

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              Nudging and Boosting: Steering or Empowering Good Decisions

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                Author and article information

                Contributors
                Journal
                Proceedings of the ACM on Human-Computer Interaction
                Proc. ACM Hum.-Comput. Interact.
                Association for Computing Machinery (ACM)
                2573-0142
                April 17 2024
                April 26 2024
                April 17 2024
                : 8
                : CSCW1
                : 1-41
                Affiliations
                [1 ]University of Notre Dame, Notre Dame, IN, USA
                [2 ]Cornell University, Ithaca, NY, USA
                [3 ]Cornell Tech, New York, NY, USA
                [4 ]Virginia Tech, Blacksburg, VA, USA
                Article
                10.1145/3637336
                c5255d6b-7858-470e-b57f-fde83a28abc0
                © 2024
                History

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