Designing Alternative Form-Autocompletion Tools to Enhance Privacy Decision-making and Prevent Unintended Disclosure
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
Modern Web browsers provide users with tools to reduce the burden of filling out forms. Despite the widespread adoption of these tools, little is known about how they affect users’ privacy decision-making. This research compares traditional form autocompletion tools with two alternative tools designed for elaboration for this study (“add” and “remove” tools). The results show that the use of traditional form autocompletion tools significantly diminishes users’ deliberate privacy decision-making, while the proposed tools can mitigate these adverse effects, such that users (1) disclose significantly less information and (2) are more likely to assess the alignment between the type of the data requested and the goal of the entity requesting that data (i.e., context specificity ). While both proposed tools help users become more deliberate in their disclosure behavior, they prefer the “add” tool over the “remove” tool. Our results show that tools designed for elaboration can nudge users toward protecting their privacy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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