Is Generative AI Increasing the Risk for Technology‐Mediated Trauma Among Vulnerable Populations?
Bibliographic record
Abstract
The proliferation of Generative Artificial Intelligence (Generative AI) has led to an increased reliance on AI-generated content for designing and deploying digital health interventions. While generative AI has the potential to facilitate and automate healthcare, there are concerns that AI-generated content and AI-generated health advice could trigger, perpetuate, or exacerbate prior traumatic experiences among vulnerable populations. In this discussion article, I examined how generative-AI-powered digital health interventions could trigger, perpetuate, or exacerbate emotional trauma among vulnerable populations who rely on digital health interventions as complementary or alternative sources of seeking health services or information. I then proposed actionable strategies for mitigating AI-generated trauma in the context of digital health interventions. The arguments raised in this article are expected to shift the focus of AI practitioners against prioritizing dominant narratives in AI algorithms into seriously considering the needs of vulnerable minority groups who are at the greatest risk for trauma but are often invisible in AI data sets, AI algorithms, and their resultant technologies.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".