The Face of Persuasion: Analyzing Bias and Generating Culture-Aware Ads (2025)

Abstract: Text-to-image models are appealing for customizing visual advertisements and targeting specific populations. We investigate this potential by examining the demographic bias within ads for different ad topics, and the disparate level of persuasiveness (judged by models) of ads that are identical except for gender/race of the people portrayed. We also experiment with a technique to target ads for specific countries.

## introduction advertisements have great significance: they affect perceptions on a variety of topics, from products to politics and societal values. given recent progress on generative models, their use for aicreated ads is imminent. these models could in theory customize ads, targeting specific populations through demographically diverse content. we investigate both the promise of generating diverse visual ads with text-to-image diffusion models and the bias in assessing the resulting images (i.e., scoring their persuasiveness) by large language models (llms) and multimodal llms. we begin with an investigation of gender and race bias in an existing dataset @xcite . we compare to bias in ads generated with three text-to-image models: dalle3 @xcite , flux (black forest labs, 2024), and auraflow @xcite . we find that both the dataset and generated images exhibit racial bias: for example, black individuals are greatly underrepresented in clothing and shopping ads. we then run controlled experiments where we only alter one demographic feature in ads keeping the rest of the details and quality the same, and study how persuasiveness judgments vary with gender and race. for example, in fig. 1 , the model chooses the image with a white woman as more persuasive because it appears "more elegant". figure 1 : selection of the more persuasive image by internvl @xcite . image 1 features a black woman; image 2 is a white woman. internvl selected image 2 as more persuasive. red marks reasoning bias. second, we attempt to create ads that convey a particular message and are tailored toward a particular culture/country. an ad aimed at a japanese audience may benefit from featuring an asian person or japanese cultural symbols, but resonate less and be less effective with an united arab emirates audience. we experiment with a technique that incorporates symbols from other ads in the generation process and shows promising results. our contributions are: (1) we analyze demographic bias in both the pittad dataset foot_0 and generative models for persuasive content creation, across different advertisement topics. (2) we demonstrate bias in llms and mllms when selecting the most persuasive images, revealing preference patterns based on demographic attributes. (3) we propose culgen, a culture-aware image generation method for producing advertisement images addressing specific cultural/regional contexts. ## results we discuss the results based on the experimental setup in sec. a (appx). ## conclusion we analyzed racial and gender representation biases in real and t2i-generated advertisements. we showed perception biases of persuasivensess by mllm and llm judges in controlled experiments with nearly identical images. we showed promise of country targeting through cultural symbols.