Measuring Preferred Representation in AI-generated Images with Community Scorers
Melanie F. Pradier, Camilla Longden, Anja Thieme, Rita F. Marques, Nari Johnson, Sidhika Balachandar, Xinnuo Xu, Linda Y. Wen 等 12 位作者
原文摘要
AI-generated images often misrepresent historically marginalised communities, with real-world consequences beyond the images themselves. However, conventional metrics fail to measure how communities want to be represented in AI-generated images. To address this, we introduce the concept of preferred representation. Building on previous work, we propose a measurement instrument that operationalises this concept through a Community Scorer - a model that evaluates a specific community's preferred representation in AI-generated images. We interrogate the validity of Community Scorers on three diverse disability communities that span the Global North and South. The results show that Community Scorers align significantly better with community ratings compared to other widely used text-to-image metrics. We then demonstrate the usefulness of these scorers in a downstream content filtering task. We release the Community Preference Dataset to enable others to optimise how Community Scorers can be scaled for culturally specific evaluation of AI-generated visual media.
cs.LG