Bibliographic record
Abstract
Dark patterns are deceptive or manipulative design techniques in technology. They pose challenges for policymakers because they impact user autonomy and financial well-being. However, the lack of quantifiability of dark patterns complicates regulation efforts, and much of the existing research remains theoretical, lacking practical application. This study aims to address these gaps by identifying and quantifying various types of dark patterns in day-to-day software applications across different domains, namely shopping, health and fitness, and education. Drawing from the conceptual framework outlined in previous research, the top three apps in Canada within each application domain were selected for analysis. By identifying dark patterns and assessing the attributes of the identified dark patterns, each app was assigned a darkness score. The findings revealed that Temu, Yuka-Food & Cosmetic Scanner, and Duolingo reported the highest darkness scores within their respective categories, with scores of $7.5,5$, and 6.5, respectively. Furthermore, the shopping category exhibited the highest mean darkness scores, indicating a greater prevalence of dark patterns in this domain. Across all categories, the modification of decision space emerged as the most commonly influenced choice architecture. This study contributes to a deeper understanding of where regulatory efforts should be focused in addressing dark patterns in applications. It also sheds light on the nature of different types of dark patterns, their utilization across various domains, and the similarities and differences in their implementation. Moreover, by quantifying darkness in applications, this research underscores the importance of quantifiability in the evaluation of dark patterns, providing valuable insights for policymakers, researchers, and practitioners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".