Navigating the Gray: Design Practitioners' Perceptions Toward the Implementation of Privacy Dark Patterns
Bibliographic record
Abstract
Designers are sometimes accused of using deceptive methods to manipulate users' information privacy decisions through "privacy dark patterns." Through semi-structured interviews, we explore the perceptions of 23 design practitioners towards the implementation of "privacy dark patterns" created by other designers. This paper explores designers' perceived responsibilities toward users' privacy and their interpretations of the reasons behind the design implementation. We found a range of empathetic rationales among our participants toward other designers' intentions. An example theme is Designer Followed the Status Quo, where common and widely used privacy interfaces are normalized a practice that is typically viewed as reasonable. Our participants' interpretations of the design intent influenced their self-reported practices for navigating similar design requests. We propose a set of factors that influence privacy design practices, including following conventions and norms, ensuring legal compliance, reliance on established usability standards, perceived benefits to businesses and consumers, and degrees of privacy harm.
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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 | 0.080 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| 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 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".