Catherine D'Ignazio and Lauren Klein


Data, AI, and Power: Lessons from Intersectional Feminism
Thursday, March 4, 2021, 2pm EST/EDT

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.

This Plenary is sponsored by OneAmerica.


Catherine D'Ignazio & Lauren Klein

Catherine D'Ignazio is the Director of the Data + Feminsm Lab and an Assistant Professor of Urban Science and Planning at MIT's Department of Urban Studies and Planning. Lauren Klein is the Director of the Digital Humanities Lab and an Associate Professor in the Departments of English & Quantitative Theory and Methods at Emory University

Drs. Ignazio and Klien are authors of Data Feminisma trade book that explores the intersection of feminist thinking and data science.