Designing Alternative Form-Autocompletion Tools to Enhance Privacy Decision-making and Prevent Unintended Disclosure
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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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 | 0.018 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".