U-M Data Science Annual Symposium 2020: Data Feminism Keynote


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Participants : 
  • Catherine D’Ignazio, Assistant Professor, Urban Science & Planning; Director, Data + Feminism Lab; Department of Urban Studies & Planning, MIT
  • Lauren Klein, Associate Professor, English, Quantitative Theory and Methods, Emory University
Event Date: 
November 10, 2020
Event Time: 
9:00am to 10:05am

As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists–and others who rely on data in their work–to ignore. But it is precisely this power that makes it worth asking: “Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from what we call data feminism, a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. Illustrating data feminism in action, this talk will show how challenges to the male/female binary can help to challenge other hierarchical (and empirically wrong) classification systems; it will explain how an understanding of emotion can expand our ideas about effective data visualization; how the concept of invisible labor can expose the significant human efforts required by our automated systems; and why the data never, ever “speak for themselves.” The goal of this talk, as with the project of data feminism, is to model how scholarship can be transformed into action: how feminist thinking can be operationalized in order to imagine more ethical and equitable data practices.

The 2020 U-M Data Science Symposium will be held on Nov. 10 and 11.  The event is fully online and free. 

For more information about the MIDAS symposium, including registration and Zoom links, visit: https://midas.umich.edu/2020-symposium-keynote-speakers/