Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists.

IF 1.5 Q3 PHARMACOLOGY & PHARMACY International Journal of Pharmacy Practice Pub Date : 2024-09-04 DOI:10.1093/ijpp/riae049
Geoffrey Currie, George John, Johnathan Hewis
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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.

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生成式人工智能文本到图像对药剂师描述中的性别和种族偏见。
简介:在澳大利亚,64% 的药剂师是女性,但女性药剂师的比例仍然偏低。人工智能(AI)具有潜在的变革性,但也有可能出现错误、误导和偏见。使用 DALL-E 3(OpenAI)从文本到图像的生成式人工智能制作方法方便易用,但可能会强化性别和种族偏见:2024 年 3 月,DALL-E 3 用于生成澳大利亚药剂师的个人和群体图像。DALL-E 3 共生成了 40 张图像用于评估,其中 30 张为单个人物,其余 10 张由多个人物组成(N = 155)。所有图像均由两名审查员独立分析,以确定明显的性别、年龄、种族、肤色和体态。结果:在 DALL-E 3 中,69.7% 的药剂师为男性,29.7% 为女性,93.5% 为浅肤色,6.5% 为中肤色,0% 为深肤色。性别分布与实际澳大利亚药剂师的性别分布有显著的统计学差异(P < .001)。在单个药剂师的图像中,DALL-E 3 生成的图像 100% 为男性,100% 为浅肤色:本次评估揭示了使用 DALL-E 3 生成澳大利亚药剂师的人工智能文本到图像时存在的性别和种族偏见。生成的图像中,白人男性药剂师的比例过高,这不能代表当今澳大利亚药剂师的多样性。
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来源期刊
CiteScore
2.90
自引率
5.60%
发文量
146
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.
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