POSTER: A Soviet-Style Synthetic Composite Dataset for Visual Bias Research in Image Embeddings
Abstract
This dataset and accompanying code support research into visual bias in image embedding models. Using a programmatic compositing pipeline, foreground subjects are placed onto background scenes across six social and occupational categories. By systematically swapping backgrounds while keeping the foreground subject identical, the dataset enables a direct test of the central research question: when an image embedding model encodes a composite image, does it prioritise the foreground subject or the background scene? Why Soviet Propaganda Posters? This project was developed as part of a digital humanities course, with the deliberate aim of grounding the dataset in a historically and artistically meaningful visual corpus. Soviet propaganda posters from the 1930s–1960s represent one of the most studied traditions of applied visual communication — a form of art produced at the intersection of politics, society, and mass media. Their stylistic conventions (flat bold shading, limited colour palettes, heroic composition, clearly delineated subjects) make them particularly well-suited for computational analysis: the visual language is unambiguous, subjects are easy to isolate, and the categorical themes (agriculture, military, industry, family, politics, sport) map naturally onto the social roles that bias research is most concerned with. Studying this visual tradition through the lens of modern image embeddings also raises meaningful questions about how machine learning models encode…
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