The ethics of global health communication in the artificial intelligence era: avoiding poverty porn 2.0
Bibliographic record
Abstract
The ethics of global health communication in the artificial intelligence era: avoiding poverty porn 2.0 Following widespread criticism, so-called poverty porn, which usually appears in the guise of photographs featuring people in extreme states of suffering, 1,2 is now largely discouraged in contemporary ethical communications guidelines, 3 because it reduces people to decontextualised, suffering, and racialised bodies.Does this reduction mean that such biased imagery is a thing of the past?Alarmingly, this seems not to be the case.Generative artificial intelligence (AI) imagery allows people to generate images in seconds.Importantly, it is cheaper than hiring a photographer or artist.Therefore, in the age of budget cuts, organisations are increasingly experimenting with AI-generated imagery.For example, in 2023, WHO published an AI-generated anti-tobacco campaign depicting a suffering and hungry child of presumed African heritage in dusty clothing standing alone in a field, with the caption "When you smoke, I starve".4 In the same year, Plan International created two videos of AI-generated images depicting pregnant and abused adolescent girls forced into marriage, gaining more than 300 000 views.5,6 In 2024, the UN official YouTube channel, which boasts 3 million subscribers, posted a video featuring AI-generated avatars re-enacting testimonies from survivors of conflict-related sexual violence, along with the hashtag "#EndRapeInWar".7 These same organisations would probably not create such depictions featuring actual people due to internal ethical policies.Is the artificiality of the image thus being used as justification to reintroduce much-contested visual tropes under a new guise?An unemotional argument based on marketing strategies can be made that these images are ethical because they supposedly protect the anonymity of real, suffering people while still yielding the sought-after engagement.A similar phenomenon of using AI imagery for communication seems to be rippling across the global health industry.From social media outputs, such as LinkedIn and X (previously Twitter), we collected a sample of more than 100 AI-generated images posted between Jan 1 and July 1, 2025, by individuals and smallscale organisations often based in the low-income and middle-income countries, many of which replicate the AA received funding for this work from the Marie Skłodowska-Curie Actions postdoctoral fellowship (GAP-101150547).All other authors declare no competing interests.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".