Abstract

We 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.

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Abstract

Anthropographics are human-shaped visualizations that aim to emphasize the human importance of datasets and the people behind them. However, current anthropographics tend to employ homogeneous human shapes to encode data about diverse demographic groups. Such anthropographics can obscure important differences between groups and contemporary designs exemplify the lack of inclusive approaches for representing human diversity in visualizations. In response, we explore the creation of demographically diverse anthropographics that communicate the visible diversity of demographically distinct populations. Building on previous anthropographics research, we explore strategies for visualizing datasets about people in ways that explicitly encode diversity—illustrating these approaches with examples in a variety of visual styles. We also critically reflect on strategies for creating diverse anthropographics, identifying social and technical challenges that can result in harmful representations. Finally, we highlight a set of forward-looking research opportunities for advancing the design and understanding of diverse anthropographics.

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