Privacy Policies for Apps Targeted Toward Youth: Descriptive Analysis of Readability
Bibliographic record
Abstract
BACKGROUND: Due to the growing availability of consumer information, the protection of personal data is of increasing concern. OBJECTIVE: We assessed readability metrics of privacy policies for apps that are either available to or targeted toward youth to inform strategies to educate and protect youth from unintentional sharing of personal data. METHODS: We reviewed the 1200 highest ranked apps from the Apple and Google Play Stores and systematically selected apps geared toward youth. After applying exclusion criteria, 99 highly ranked apps geared toward minors remained, 64 of which had a privacy policy. We obtained and analyzed these privacy policies using reading grade level (RGL) as a metric. Policies were further compared as a function of app category (free vs paid; entertainment vs social networking vs utility). RESULTS: Analysis of privacy policies for these 64 apps revealed an average RGL of 12.78, which is well above the average reading level (8.0) of adults in the United States. There was also a small but statistically significant difference in word count as a function of app category (entertainment: 2546 words, social networking: 3493 words, and utility: 1038 words; P=.02). CONCLUSIONS: Although users must agree to privacy policies to access digital tools and products, readability analyses suggest that these agreements are not comprehensible to most adults, let alone youth. We propose that stakeholders, including pediatricians and other health care professionals, play a role in educating youth and their guardians about the use of Web-based services and potential privacy risks, including the unintentional sharing of personal data.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".