Effect of Gender Fair Job Description on Generative AI Images
This addresses the problem of generative AI reinforcing societal gender biases in STEM fields, which is incremental as it builds on existing bias research.
The study analyzed gender representation in STEM occupation images generated by OpenAI DALL-E 3 and Black Forest FLUX.1 using 150 prompts in different linguistic forms, revealing significant male bias across all forms, with the German pair form showing reduced but still present bias.
STEM fields are traditionally male-dominated, with gender biases shaping perceptions of job accessibility. This study analyzed gender representation in STEM occupation images generated by OpenAI DALL-E 3 \& Black Forest FLUX.1 using 150 prompts in three linguistic forms: German generic masculine, German pair form, and English. As control, 20 pictures of social occupations were generated as well. Results revealed significant male bias across all forms, with the German pair form showing reduced bias but still overrepresenting men for the STEM-Group and mixed results for the Group of Social Occupations. These findings highlight generative AI's role in reinforcing societal biases, emphasizing the need for further discussion on diversity (in AI). Further aspects analyzed are age-distribution and ethnic diversity.