Evaluation of Generative Artificial Intelligence Safeguards Against the Creation of Images and Videos Harmful to Public Health.
Objectives: As generative artificial intelligence (AI) continues to advance, an environment that lacks strong safeguards could create opportunities for misuse by malicious actors. This study aimed to evaluate the safeguards of publicly accessible generative AI applications against the creation of im...
| Publicado en: | Public Health Reports Vol. 141; no. 4; pp. 542 - 552 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Jul/Aug2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=194675100&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 194675100 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00333549 PHR jtl: Public Health Reports issn: 00333549 maglogo: Y pubinfo: dt: Jul/Aug2026 vid: 141 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 194675100 10.1177/00333549261418596 ppf: 542 ppct: 10 formats: tig: atl: Evaluation of Generative Artificial Intelligence Safeguards Against the Creation of Images and Videos Harmful to Public Health. aug: au: Chu, Bianca Modi, Natansh D. Menz, Bradley D. Cornelisse, Erik Bacchi, Stephen Bulamu, Norma Ullah, Shahid McKinnon, Ross A. Gradon, Kacper Rowland, Andrew Sorich, Michael J. Hopkins, Ashley M. affil: Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Australia Academic Unit of Clinical and Health Sciences, University of South Australia, Adelaide, Australia Adelaide Medical School, The University of Adelaide, Adelaide, Australia Lyell McEwin Hospital, Elizabeth, Australia Department of Security and Crime Science, University College London, London, United Kingdom Department of Cybersecurity, Warsaw University of Technology, Warsaw, Poland su: Internet access Risk-taking behavior Smoking Pregnant women Decision making Motivation (Psychology) Health behavior Public health Alcohol drinking Obesity Social stigma Generative artificial intelligence Audiovisual materials Safety Mobile apps Video production & direction Research funding Electronic cigarettes Chi-squared test Descriptive statistics Thematic analysis sug: subj: Internet access Risk-taking behavior Smoking Pregnant women Decision making Motivation (Psychology) Health behavior Public health Alcohol drinking Obesity Social stigma Drinking Places (Alcoholic Beverages) Health and Welfare Funds Motion Picture and Video Production Tobacco product manufacturing Tobacco Manufacturing Generative artificial intelligence Audiovisual materials Safety Mobile apps Video production & direction Research funding Electronic cigarettes Chi-squared test Descriptive statistics Thematic analysis keyword: AI safeguards AI safety artificial intelligence generative AI public health AI safeguards AI safety artificial intelligence generative AI public health ab: Objectives: As generative artificial intelligence (AI) continues to advance, an environment that lacks strong safeguards could create opportunities for misuse by malicious actors. This study aimed to evaluate the safeguards of publicly accessible generative AI applications against the creation of image and video content potentially harmful to public health. Methods: We assessed the safeguards of 10 leading text-to-image models and 2 text-to-video models across 5 public health themes: promoting solariums as safe, stigmatizing overweight people, promoting alcohol use as safe during pregnancy, depicting vaping as healthy, and depicting smoking cigarettes as cool for teenagers. For each theme, we submitted 10 paraphrased prompts in duplicate to the image models and once to each video model. Two independent reviewers categorized outputs as potentially harmful or not, with a third reviewer responsible for resolving discrepancies. We used χ² tests to determine significant differences in outputs. Results: Among 1000 image prompt submissions, we judged 521 (52%) of the generated images to be potentially harmful to public health. Image generation rates varied significantly by public health theme—from 43% (85 of 200) of prompts promoting alcohol use as safe during pregnancy to 64% (128 of 200) of prompts depicting vaping as healthy (P <.001)—and across models, from 0% for ChatGPT to 98% for Reve (P <.001). Of 100 video prompt submissions, we classified 52% of outputs from Sora and 30% from Flow as potentially harmful. Conclusions: Generative AI applications varied significantly in safeguards, with several systems often generating images that could be harmful to public health. The findings underscore the urgent need for greater transparency, safety, and oversight of generative AI to mitigate public health harms. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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