Description
What is feminist data science? How is feminist thinking being incorporated into data-driven work? And how are humanities scholars, in particular, bringing together data science and feminist theory in their research? Drawing from my recent book, Data Feminism (MIT Press), co-authored with Catherine D’Ignazio, I will present a set of principles for doing data science that are informed by the past several decades of intersectional feminist activism and critical thought. In order to illustrate these principles, as well as some of the ways that humanities scholars have begun to put them into action, I will discuss a range of recent research projects including several of my own: 1) a thematic analysis of a large corpus of nineteenth-century newspapers that reveals the invisible labor of women newspaper editors; 2) the development of a model of lexical semantic change that, when combined with network analysis, tells a new story about Black activism in the nineteenth-century United States; and 3) the design and fabrication of a large-scale haptic data visualization, inspired by a forgotten historical visualization scheme, that suggests new possibilities for visualization design. Taken together, these examples demonstrate how feminist thinking can be operationalized into more ethical, more intentional, and more capacious data practices, in the humanities and beyond.