Recapitulation of Ageism in Artificial Intelligence-Generated Images: Longitudinal Comparative Study.

Background: Positive images of aging in traditional media promote better health outcomes in older adults, including increased life expectancy. Images produced by generative artifical intelligence (AI) technologies may reflect and amplify societal age-related biases, a phenomenon known as digital ageism. This study addresses a gap in research on the perpetuation of digital ageism in AI-generated images over time. Objective: This study examined how visual characteristics of digital ageism in AI-generated representations of older adults changed over time. It aims to provide insight into the interplay between technology advancements, societal attitudes toward aging, and the well-being of older adults interacting with digital media. Methods: This longitudinal study compared 164 images generated by Open AI's DALL-E 2 at 2 time points, 1 year apart (2022 and 2023). Identical text prompts from the geriatric lexicon (eg, frail older adult, dementia) were used at both time points. Authors evaluated the images generated for demographic characteristics (perceived gender, race, and socioeconomic status), and primary emotion characteristics, then compared the frequency of these characteristics between years and evaluation characteristics using a type III 2-way ANOVA. Results: Representations of White-racialized older adults were 5-fold higher than those of other races in both years. The mean number of representations of Asian-racialized individuals increased from 20 to 31 (P=.004), and the mean number of other racialized representations also increased, from 6 to 14 (P=.007). Representations of people with a middle-class socioeconomic status were significantly more frequent than other statuses in 2022 and 2023 with no changes in socioeconomic status from one year to the next. Prompts were largely neutral for expression terms, while image analyses for expressions did not show significant differences in positive, neutral, or negative emotions between 2022 and 2023. Prompts used for image generation had more male-oriented terms than expected, and male representation was higher then female representation in the images, with no difference in sex representation between the 2 time points. Conclusions: Despite a social emphasis on positive views on aging, AI text-to-image generators persistently generated images with characteristics of digital ageism. Images predominantly featured White-racialized individuals at both time points, with no improvement in emotional representation despite using neutral text prompts. These findings highlight the persistence of ageist visual characteristics in AI-generated images over time. A limitation of this study is that it focused only on AI image generation and did not analyze other AI-generated content that may express digital ageism.