Lauren Klein

With their ability to depict hundreds, thousands, and sometimes even millions of relationships at a single glance, visualizations of data can dazzle, inform, and persuade. It is precisely this power that makes it necessary to ask: Who is creating these visualizations? Who are they for? Whose interest are they serving? By whose values are they informed? These are some of the questions that emerge from what this project calls data feminism, a way of thinking about data and its visualization that is informed by the past several decades of feminist critical thought. The project draws upon examples that range from the jittery election gauge that was displayed on the front page of The New York Times on the night of the 2016 presidential election, to the starkly designed and provocatively titled map of a section of Detroit drawn in 1970 called “Where Commuters Run Over Black Children on the Pointes-Downtown Track.” It prompts questions about how challenges to the male/female binary can also help challenge other binary and hierarchical classification systems; how the concept of invisible labor can help to expose the invisible forms of labor associated with data work; and how an understanding of affective and embodied knowledge can help to expand the notion of what constitutes data and what does not. A collaboration between Lauren Klein, a scholar of American studies and digital humanities and Catherine D’Ignazio, an expert in data visualization and civic media, the project reveals how a feminist approach to thinking about data not only exposes how power and privilege operate in data science and visualization work, but also suggests how new design principles can help to mitigate inequality and work toward justice. In addition to a jointly authored book, Data Feminism will result in a companion website and an art exhibition of feminist visualization work, scheduled for installation in Boston in Fall 2019 and Atlanta in Fall 2020. Klein and D’Ignazio have previously collaborated on the IEEE Visualization workshop paper, “Feminist Data Visualization” (2016). Award period: January 1, 2019 through December 31, 2020