Unpacking Youth Privacy Management in AI Systems: A Privacy Calculus Model Analysis
Bibliographic record
Abstract
The increasing use of Artificial Intelligence (AI) in daily life has introduced substantial issues in protecting user privacy, particularly for young digital citizens. This study examines the complex dynamics of privacy management in AI systems utilizing the Privacy Calculus Model (PCM), with 482 participants: 176 young digital citizens (ages 16–19), 146 parents and educators, and 160 AI specialists. The research used a mixed methods approach to analyze key characteristics, including data ownership, user control, parental data sharing attitude, transparency, trust, perceived risks, benefits, and education. The results underscore the necessity of promoting digital literacy, establishing trust through transparent practices, and implementing collaborative approaches for privacy governance. The study emphasizes the significance of customized educational activities and regulatory frameworks that enable users to manage the trade-offs between the advantages and risks of data sharing by including varied views. This research enhances ethical AI development and advocates equal privacy safeguards for children and young adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.052 | 0.088 |
| 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; both teacher heads agree on what is shown here.
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".