Civiverse: A Dataset for Analyzing User Engagement with Open-Source Text-to-Image Models
This addresses the problem of understanding user engagement and cultural impacts for researchers and developers in AI ethics, but it is incremental as it applies existing analysis methods to a new dataset.
The study tackled the lack of cultural analysis of open-source text-to-image models by analyzing the CivitAI platform, introducing the Civiverse dataset with millions of images, and found a predominant user preference for generating explicit content and homogenization of semantic content.
Text-to-image (TTI) systems, particularly those utilizing open-source frameworks, have become increasingly prevalent in the production of Artificial Intelligence (AI)-generated visuals. While existing literature has explored various problematic aspects of TTI technologies, such as bias in generated content, intellectual property concerns, and the reinforcement of harmful stereotypes, open-source TTI frameworks have not yet been systematically examined from a cultural perspective. This study addresses this gap by analyzing the CivitAI platform, a leading open-source platform dedicated to TTI AI. We introduce the Civiverse prompt dataset, encompassing millions of images and related metadata. We focus on prompt analysis, specifically examining the semantic characteristics of text prompts, as it is crucial for addressing societal issues related to generative technologies. This analysis provides insights into user intentions, preferences, and behaviors, which in turn shape the outputs of these models. Our findings reveal a predominant preference for generating explicit content, along with a focus on homogenization of semantic content. These insights underscore the need for further research into the perpetuation of misogyny, harmful stereotypes, and the uniformity of visual culture within these models.