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Investigating Bias in Generative Text-to-Image AI Models
Davide Crociati • Davide Sonno
sommario
Generative Text-to-Image (T2I) models have gained more and more popularity, enabling users to easily produce detailed images from textual prompts. However, these models often encode and reproduce the social or cultural biases present in their training data. Such biases can influence people perception and reinforce stereotypes. In this project we aim to explore and identify patterns of bias in six of the current popular T2I models.
