{"title":"Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists.","authors":"Geoffrey Currie, George John, Johnathan Hewis","doi":"10.1093/ijpp/riae049","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases.</p><p><strong>Methods: </strong>In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus.</p><p><strong>Results: </strong>Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone.</p><p><strong>Conclusions: </strong>This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.</p>","PeriodicalId":14284,"journal":{"name":"International Journal of Pharmacy Practice","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Pharmacy Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/ijpp/riae049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases.
Methods: In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus.
Results: Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone.
Conclusions: This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.
期刊介绍:
The International Journal of Pharmacy Practice (IJPP) is a Medline-indexed, peer reviewed, international journal. It is one of the leading journals publishing health services research in the context of pharmacy, pharmaceutical care, medicines and medicines management. Regular sections in the journal include, editorials, literature reviews, original research, personal opinion and short communications. Topics covered include: medicines utilisation, medicine management, medicines distribution, supply and administration, pharmaceutical services, professional and patient/lay perspectives, public health (including, e.g. health promotion, needs assessment, health protection) evidence based practice, pharmacy education. Methods include both evaluative and exploratory work including, randomised controlled trials, surveys, epidemiological approaches, case studies, observational studies, and qualitative methods such as interviews and focus groups. Application of methods drawn from other disciplines e.g. psychology, health economics, morbidity are especially welcome as are developments of new methodologies.