School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, Australia.
Department of Radiology, Baylor College of Medicine, Houston, United States.
Int J Pharm Pract. 2024 Nov 14;32(6):524-531. doi: 10.1093/ijpp/riae049.
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.
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.
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.
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.
在澳大利亚，64%的药剂师是女性，但仍未得到充分代表。生成式人工智能（AI）具有变革性的潜力，但也有可能出现错误、失实陈述和偏见。使用 DALL-E 3（OpenAI）的生成式 AI 文本到图像生成功能易于使用，但可能会强化性别和种族偏见。
在 2024 年 3 月，使用 DALL-E 3 生成澳大利亚药剂师的个人和群体图像。使用 DALL-E 3 共生成了 40 张图像，用于评估其中 30 张是个人形象，其余 10 张是由多个角色组成的图像（N=155）。两位评审员独立分析所有图像，以确定其性别、年龄、种族、肤色和体型。对答复意见的分歧由第三方达成共识。
DALL-E 3 生成的药剂师中，69.7%为男性，29.7%为女性，93.5%为浅色肤色，6.5%为中等肤色，0%为深色肤色。这种性别分布与实际的澳大利亚药剂师有显著差异（P<0.001）。在个体药剂师的图像中，DALL-E 3 生成的图像 100%为男性，100%为浅色肤色。
这项评估揭示了使用 DALL-E 3 进行的生成式 AI 文本到图像生成在澳大利亚药剂师中存在的性别和种族偏见。生成的图像中白人男性作为药剂师的代表性过高，这与澳大利亚当今药剂师的多样性不符。
