Dhawka, PriyaPerera, LaurenWillett, Wesley2024-05-012024-05-012024-05-11Dhawka, P., Perera, L., & Willett, W. (2024). Better little people pictures: Generative creation of demographically diverse anthropographics. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems.https://doi.org/10.1145/3613904.3641957https://hdl.handle.net/1880/118444https://doi.org/10.11575/PRISM/43286We explore the potential of generative AI text-to-image models to help designers efficiently craft unique, representative, and demographically diverse anthropographics that visualize data about people. Currently, creating data-driven iconic images to represent individuals in a dataset often requires considerable design effort. Generative text-to-image models can streamline the process of creating these images, but risk perpetuating designer biases in addition to stereotypes latent in the models. In response, we outline a conceptual workflow for crafting anthropographic assets for visualizations, highlighting possible sources of risk and bias as well as opportunities for reflection and refinement by a human designer. Using an implementation of this workflow with Stable Diffusion and Google Colab, we illustrate a variety of new anthropographic designs that showcase the visual expressiveness and scalability of these generative approaches. Based on our experiments, we also identify challenges and research opportunities for new AI-enabled anthropographic visualization tools.enUnless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Attribution 4.0 InternationalPreprintRGPIN-2021-02492