Divergent deceptions: comparative analysis of Deceptive Patterns in iOS and Android apps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deceptive Patterns (also known as Dark Patterns) are manipulative interface elements that can cause users to experience financial, temporal, and privacy-related losses. While Deceptive Patterns have been extensively studied in Android applications, their prevalence in iOS apps remains largely unexplored, despite significant ecosystem differences and iOS's growing popularity among younger users. Notably, Apple's tight control over its ecosystem has fostered public perception of iOS being the safer platform and as a byproduct, iOS users may be less vigilant towards app-related risks. To investigate how the prevalence of Deceptive Patterns on iOS compares to Android, we conducted a review of the same 143 mobile apps across both platforms. Our analysis reveals statistically significant differences between Deceptive Patterns on iOS and Android, with iOS apps exhibiting more instances overall (1477 vs. 1398). The findings suggest that iOS users may be more vulnerable to the risks posed by Deceptive Patterns. Furthermore, our analysis identified four specific types of Deceptive Patterns with notable discrepancies between the mobile platforms, indicating potential influences by app store guidelines and developer tools, and the rise of A/B testing Deceptive Patterns. These findings highlight the need to further explore different digital platforms and user protections on mobile devices.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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