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03/11/2020

Data Feminism

Time: 12:00 pm to 1:30 pm
Location: Snell Library 90
Sponsored By: NULab for Texts, Maps, and Networks
More Information: https://web.northeastern.edu/nulab/event/klein-dignazio/

Please join the NULab for Texts, Maps, and Networks on Wednesday, March 11th at 12–1:30pm in 90 Snell Library for an exciting discussion with NULab visiting speakers, Lauren Klein (Emory University) and Catherine D’Ignazio (MIT). Klein and D’Ignazio will be discussing their forthcoming book, Data Feminism (2020).

About the Authors

Lauren Klein is an Associate Professor in the English and Quantitative Theory and Methods Departments at Emory University and the Director of the Digital Humanities Lab. Her research explores the intersections of history, race, and data science. Her two current projects include Data by Design, an interactive history of data visualization and Vectors of Freedom, a project exploring quantitative methods in the archive of the abolitionist movement in the US.

Catherine D’Ignazio is the Director of the Data + Feminism Lab and an Assistant Professor of Urban Science and Planning at MIT. Her research explores creative ways to democratize data science for social justice, including public art and design projects, feminist hackathons, and data storytelling workshops.

Book Description

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, Catherine D’Ignazio and Lauren Klein 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, D’Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They 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 they 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.

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