Exploring the Use of Pictograms in Privacy Agreements to Facilitate Communication Between Users and Data Collecting Entities: Randomized Controlled Trial
Bibliographic record
Abstract
Background Privacy agreements can foster trust between users and data collecting entities by reducing the fear of data sharing. Users typically identify concerns with their data privacy settings, but due to the complexity and length of privacy agreements, users opt to quickly consent and agree to the terms without fully understanding them. Objective This study explores the use of pictograms as potential elements to assist in improving the transparency and explanation of privacy agreements. Methods During the development of the pictograms, the Double Diamond design process was applied for 3 instances of user interactions and 3 iterations of pictograms. The testing was done by performing a comparative study between a control group, which received no pictograms, and an experimental group, which received pictograms. The pictograms were individually tested to assess their efficacy by using an estimated comprehension of information symbols test. Results A total of 57 participants were recruited for the pictogram evaluation phase. With the addition of pictograms, the overall understanding improved by 13% (P=.001), and the average time spent answering the questions decreased by 57.33 seconds. A 9% decrease in perceived user frustration was also reported by users, but the difference was not significant (χ24=4.80; P=.31). Additionally, none of the pictograms passed the estimated comprehension of information symbols test, with 7 being discarded immediately and 5 requiring further testing to assess their efficacy. Conclusions The addition of pictograms appeared to improve users’ understanding of the privacy agreements, despite the pictograms needing further changes to be more understandable. This proves that with the aid of pictographic images, it is possible to make privacy agreements more accessible, thereby allowing trust and open communication to be fostered between users and data collecting entities. Trial Registration ClinicalTrials.gov NCT05631210; https://clinicaltrials.gov/ct2/show/NCT05631210
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".