From Faces to Politics: Vision-Language Models (Sometimes) Link Visual Demographic Characteristics to Ideological Labels
2026-04-01
When foundation models analyze political content, do they use demographic characteristics as shortcuts for ideological attribution? We conducted detailed experiments with GPT-4o-mini and validated key findings across GPT-4o and LLaVA , using identical, ideologically neutral campaign advertisements with systematically varied candidate demographics. All models consistently attributed more liberal ideologies to women than men. These effects exceeded real-world gender differences from a nationally representative survey. However, racial associations differed by model: strong in GPT-4o-mini (where Black candidates received substantially more liberal attributions), attenuated in GPT-4o , and insignificant in LLaVA . These demographic effects persisted across temperature settings, prompt variations, and even explicit debiasing instructions in GPT-4o-mini . Our findings reveal that visual demographic features can shape AI outputs in ways that vary across models, with implications for applications such as content classification.