Exploring the Feasibility and Acceptability of Technological Interventions to Prevent Adolescents’ Exposure to Online Pornography: Qualitative Research
Bibliographic record
Abstract
BACKGROUND: Amid growing concern over children's access to online pornography, policy makers are looking toward new and emerging technological concepts for unexplored solutions including artificial intelligence and facial recognition. OBJECTIVE: This study sought to explore and ideate emerging technological interventions that are feasible, acceptable, and effective in preventing and controlling the exposure of young people to online pornographic material. METHODS: We conducted a series of qualitative co-design workshops with both adult (n=8; aged 32-53 years) and adolescent participants (n=4; aged 15-17 years) to ideate potential technological interventions that are feasible, acceptable, and effective at preventing and controlling the exposure of young people to online pornographic material. A story stem methodology was used to explore participants' attitudes toward two unique technological prototypes. RESULTS: Participants expressed a generally favorable view of the proposed technological concepts but remained unconvinced of their overall utility and effectiveness in preventing the intentional viewing of pornography by young people. Age-appropriate parent-child conversations remained participants' preferred approach to mitigating potential harms from pornographic material, with parents also expressing a desire for more educational resources to help them better navigate these discussions. User privacy and data security were a primary concern for participants, particularly surrounding the use and collection of biometric data. CONCLUSIONS: Internationally, policy makers are taking action to use age assurance technologies to prevent children's access to online pornography. It is important to consider the needs and opinions of parents and young people in the use and implementation of these technologies. Participants in this study were generally supportive of new and emerging technologies as useful tools in preventing the accidental exposure of young people to online pornographic material. However, participants remained less convinced of their ability to avert intentional viewing, with substantial concerns regarding technological efficacy, adaptability, and user privacy. Further, co-design and prototype refinement are needed to better understand user acceptability and comfortability of these new technological interventions, alongside additional research exploring sociocultural differences in information needs and user experiences.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".