Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
This introduces a new dimension for understanding bias and fairness in multilingual, multimodal AI systems, addressing a previously overlooked fundamental question in bias research.
The study tackled the problem of how grammatical gender in languages influences visual representation in text-to-image models, revealing that masculine grammatical markers increase male representation to 73% on average compared to 22% in gender-neutral English, while feminine markers increase female representation to 38% compared to 28% in English.
Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does grammatical gender influence visual representation across languages? We introduce a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations (e.g., ``une sentinelle'' - grammatically feminine in French but referring to the stereotypically masculine concept ``guard''). Our dataset spans five gendered languages (French, Spanish, German, Italian, Russian) and two gender-neutral control languages (English, Chinese), comprising 800 unique prompts that generated 28,800 images across three state-of-the-art T2I models. Our analysis reveals that grammatical gender dramatically influences image generation: masculine grammatical markers increase male representation to 73% on average (compared to 22% with gender-neutral English), while feminine grammatical markers increase female representation to 38% (compared to 28% in English). These effects vary systematically by language resource availability and model architecture, with high-resource languages showing stronger effects. Our findings establish that language structure itself, not just content, shapes AI-generated visual outputs, introducing a new dimension for understanding bias and fairness in multilingual, multimodal systems.