Dhawka, PriyaHe, HelenWillett, Wesley2023-02-272023-02-272023-02Priya Dhawka, Helen Ai He, and Wesley Willett. 2023. We are the Data: Challenges and Opportunities for Creating Demographically Diverse An- thropographics. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 14 pages. https://doi.org/10.1145/3544548.3581086http://hdl.handle.net/1880/115888https://dx.doi.org/10.11575/PRISM/40773Anthropographics 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.engUnless 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.anthropographicsdemographic datadiversitymarginalized populationsconference paper10.1145/3544548.3581086