Occupational identities in the age of generative AI: the case of librarianship
DOI:
https://doi.org/10.47989/ir31iConf64153Keywords:
Gen AI, Librarianship, Stereotypes, Text to image modelsAbstract
Introduction. This paper analyses AI-generated depictions of librarians to determine their alignment with stereotypical portrayals. Previous research has highlighted gender biases in large language models (LLMs) and AI-generated images. However, no studies have examined AI-generated images of librarians. This study fills that gap by exploring how these images uphold stereotypes.
Method. Data was collected from ChatGPT, DALL-E, Midjourney, and Adobe Firefly using gender-neutral prompts in American English and Brazilian Portuguese.
Analysis. We performed quantitative and qualitative analysis to compare languages and models. Thematic analysis revealed recurring themes and patterns in the visual representations.
Results. Our findings indicate that AI-generated images often depict librarians as white women, with stereotypical elements like glasses and cardigans. We also observed that different model versions and languages generate different portrayals of the profession. When prompted in Brazilian Portuguese, the models tended to offer a more representative image of the professionals.
Conclusion. The study underscores the need for a critical approach to Generative AI, as training data reflects societal biases, perpetuating stereotypes. These portrayals can impact the public perception of librarians, potentially alienating users and reinforcing an outdated, predominantly white, female, and middle-class image of the profession.
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