Designing Effective Warnings for Manipulative Designs in Mobile Applications
Why this work is in the frame
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Bibliographic record
Abstract
There is a notable rise in websites and mobile apps that use manipulative (also known as "deceptive") designs or "dark patterns". Leveraging visual perception effects and cognitive biases or object manipulations, these designs influence user behavior in ways that may not be beneficial or can even be harmful for users. It is important to both warn and educate users about manipulative designs. While numerous studies have investigated warning designs across various domains, little attention has been given to exploring how to warn users about the presence of manipulative designs in applications. We conducted a user study with a three-level warning about the presence of manipulative designs on a simulated app page on the Google Play Store and explored the impact of different warning levels on user attention and decision-making. We also explored possibilities for personalization of warning levels based on the user’s personality (Big 5) characteristics. While our findings did not discover opportunities for personalization, they underscore the benefit of a multi-level warning design, and the pivotal role of visual elements in capturing attention, complemented by the contribution of textual explanations and more details on demand. We discuss the factors influencing users to install an app despite being informed about the presence of manipulative designs and demonstrate how app distribution platforms can embed warnings in the app information to prevent or mitigate the harms of manipulative designs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it