Algorithmic Gaze: Gender and Ethnicity Bias on Algorithmic Intelligence Text-to-Image Platforms

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Drawing on feminist visual culture critique, this study examines how three text-to-image algorithmic intelligence (Al.I) platforms reproduce gender and perceived ethnic appearance patterns in occupational imagery. Using neutral English prompts with no gender or ethnicity specification, 225 images were generated across 15 occupations via the APIs of OpenAI, Google Gemini, and xAI Grok; images were analyzed through quantitative content analysis, and the accompanying textual data through descriptive analysis. All three platforms generated only images depicting women for the occupations of nurse, teacher, secretary, housekeeper, and social worker, whereas images depicting men predominated for occupations such as pilot, engineer, and CEO. In perceived ethnic appearance, White dominated at 54.2%, and all 15 pilot images were coded “White male”; intersectionally, 65.6% of White-coded images were male and 77.8% of Black-coded images female. The textual data suggest bias is encoded not only in the image but in the generation process: Grok genders-neutral prompts, and Gemini uses “she/her” pronouns in its reasoning text for female-dominated occupations. That all three platforms, despite different intervention strategies, reproduce the same patterns points to a problem rooted in the structural logic of generation rather than individual model settings; bias traceable in the textual layer before any image is produced offers a new dimension of analysis for auditing generative image models.