Sexual health in the era of artificial intelligence and its implications on children and adolescents
Bibliographic record
Abstract
The rapid advancement of artificial intelligence (AI) presents both unprecedented opportunities and significant challenges for sexual health, particularly concerning children and adolescents. This commentary explores the multifaceted implications of AI in this sensitive domain, examining its potential in sexual health education, prevention of sexual risks, and the provision of support, while also critically analyzing the ethical considerations, potential harms, and the crucial role of parents and educators. AI-powered tools, such as chatbots and personalized learning platforms, offer novel avenues for delivering accessible and destigmatized sexual health information. However, the same technologies also introduce risks related to privacy, data security, exposure to harmful content, and the erosion of human connection and critical thinking. This paper delves into these complex dynamics, emphasizing the need for careful consideration of developmental vulnerabilities, ethical guidelines, and robust safeguarding mechanisms to ensure that AI serves to protect and empower young people rather than expose them to new forms of harm in the realm of sexual health.
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 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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".