Evaluating diversity and stereotypes amongst AI generated representations of healthcare providers

Abstract

Introduction: Generative artificial intelligence (AI) can simulate existing societal data, which led us to explore diversity and stereotypes among AI-generated representations of healthcare providers. Methods: We used DALL-E 3, a text-to-image generator, to generate 360 images from healthcare profession terms tagged with specific race and sex identifiers. These images were evaluated for sex and race diversity using consensus scoring. To explore stereotypes present in the images, we employed Google Vision to label objects, actions, and backgrounds in the images. Results: We found modest levels of sex diversity (3.2) and race diversity (2.8) on a 5-point scale, where 5 indicates maximum diversity. These findings align with existing workforce statistics, suggesting that Generative AI reflects real-world diversity patterns. The analysis of Google Vision image labels revealed sex and race-linked stereotypes related to appearance, facial expressions, and attire. Discussion: This study is the first of its kind to provide a ML-based framework for quantifying diversity and biases amongst generated AI images of healthcare providers. These insights can guide policy decisions involving the use of Generative AI in healthcare workforce training and recruitment.

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