Staring Into the Darkness: The Heuristic Evaluation of Manipulative Interfaces.
Bibliographic record
Abstract
Dark Patterns (DPs) are the foundation of what has come to be called ``Deceptive Design'' and represent a significant issue in the world of user interface and user experience design. By exploiting user cognition through interface design, DPs cause users to forfeit their resources (i.e., time, money, data) for the benefit of the interface designer. As a ubiquitous phenomenon, numerous taxonomies and ontologies from various domains have been proposed to classify DPs and fragmented our understanding of them. This fragmentation complicates legislative efforts as well as knowledge-sharing between the domains they appear in. To address limitations surrounding the fragmentation, extensibility, and comprehensibility of contemporary DP ontologies and taxonomies, this thesis proposes a new method of DP identification and classification based on heuristic evaluation. By condensing the past decade of DP taxonomies via network analysis and distilling/contextualizing the result with Straussian grounded theory, we have created a set of five heuristics to evaluate potentially manipulative elements in user interfaces. Our work describes the construction of our heuristics and reports on their use in an evaluation conducted by human computer interaction researchers at the University of Saskatchewan. The results suggest that our heuristics and the heuristic evaluation process can improve DP detection and delineate manipulative from benign design choices. As a naturally inexpensive, approachable, and versatile evaluation method that provides rich and accessible qualitative data, we propose heuristic evaluation as a valuable addition to the evaluation of manipulative interfaces.
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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 | 0.025 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 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".