Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models

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 crosslinguistic 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 AIgenerated visual outputs, introducing a new dimension for understanding bias and fairness in multilingual, multimodal systems.