Data Feminism

Data Literacy, Data Science, Data Visualization, Feminism

Data Feminism is a book that I co-authored with Lauren F. Klein which will be published by MIT Press in March 2020. In the book, we outline a new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.

Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, we present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.

Illustrating data feminism in action, we show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. We explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And we show why the data never, ever “speak for themselves.”

Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn’t, and about how those differentials of power can be challenged and changed.




Data Feminism cover image: Digital visualizations by Christopher Pietsch and Siqi Zhu from Art of the March, an archival project led by Alessandra Renzi, Dietmar Offenhuber, and Nathan Felde, based on posters collected from the 2017 Boston Women’s March.