ADORKABLE AI: HOW ALGORITHMS SHAPE LIBRARIAN STEREOTYPES IN BRAZIL AND THE US - Ito, Viviane Mieko; Grimes, Lyric (AoIR Selected Papers of Internet Research, 2026)
This paper analyzes 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, often depicting professions like secretaries and nurses as women and medical professionals as white males. However, no studies have examined AI-generated images of librarians. This study fills that gap by exploring how these images uphold stereotypes, focusing on portrayals in American English and Brazilian Portuguese. Data was collected from DALL-E, Midjourney, and Adobe Firefly using gender-neutral prompts in both languages. Thematic analysis revealed recurring themes and patterns in the visual representations. Preliminary findings indicate that AI-generated images often depict librarians as white, slender, intellectual women, with stereotypical elements like glasses and cardigans. 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 These portrayals can impact the public perception of librarians, potentially alienating users and reinforcing an outdated, predominantly white, female, and middle-class image of the profession.