Applying Communication Privacy Management Theory to Youth Privacy Management in AI Contexts
Bibliographic record
Abstract
The rapid integration of Artificial Intelligence (AI) technologies into the lives of young digital citizens has escalated privacy concerns and the need for critical examination. This study uses Communication Privacy Management (CPM) Theory to understand how youth and critical stakeholders navigate these concerns. A total of 306 participants were surveyed, comprising 146 AI professionals, 127 parents and educators and 33 youths (aged 16-19). Employing a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from open-ended responses. Descriptive statistics reveal distinct perspectives among different demographics regarding data ownership, education, transparency and trust, parental role and perceived risks and benefits associated with AI systems. Structural equation modelling identified key influences on youth privacy management, highlighting the significance of transparency and trust, education and awareness, and parental data sharing among AI professionals, parents, educators, and young digital citizens. The qualitative analysis further underscored unique concerns, emphasizing a lack of understanding and data misuse contributed to the feeling of helplessness shared by all stakeholders. This study underscores the importance of integrating diverse stakeholders’ perspectives in the development of AI systems to address the complex challenges faced by youth. Recommendations include collaborative policymaking, implementing user-centric design practices, and enhancing privacy education to empower young digital citizens.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".