Data Feminism
Host
Arvind Satyanarayan
Abstract
As data are increasingly mobilized in the service of global corporations, governments, and elite institutions, their unequal conditions of produc- tion, their inequitable impacts, and their asymmetrical silences become increasingly more apparent. It is precisely this power that makes it worth asking: "Data science by whom? For whom? In whose interest? Informed by whose values?" And most importantly, "How do we begin to imagine alternatives for data’s collection, analysis, and communication?" These are some of the questions that emerge from what Lauren Klein and I call Data Feminism (forthcoming from MIT Press in early 2020). Data feminism is a way of thinking about data science and its products that is informed by the past several decades of intersectional feminist activism and critical thought, emerging anti-oppression design frameworks, and scholarship from the fields of Critical Data Studies, Science & Technology Studies, Geography/GIS, Digital Humanities and Human Computer Interaction. An intersectional feminist lens prompts questions about how, for instance, challenges to the male/female binary can also help challenge other binary (and empirically wrong) classification systems. It encourages us to ask how the concept of invisible labor can help to expose the gendered, racialized, and colonial forms of labor associated with data work. And it demonstrates why the data never, ever, speak for themselves. In this talk, I will introduce seven principles for data feminist work: examining and challenging power, rethinking binaries and hierarchies, considering context, embracing pluralism, making labor visible, and elevating emotion. The goal of this work is to transform scholarship into action – to operationalize feminism in order to imagine more ethical and more equitable data practices.
Bio
Catherine D'Ignazio is a scholar, artist/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement. She has run women's health hackathons, designed global news recommendation systems, created talking and tweeting water quality sculptures, and led walking data visualizations to envision the future of sea level rise. Her forthcoming book from MIT Press, Data Feminism, co-authored with Lauren Klein, charts a course for more ethical and empowering data science practices. Her research at the intersection of technology, design & social change has been published in the Journal of Peer Production, the Journal of Community Informatics, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). In Jan 2020, D'Ignazio will be an assistant professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT where she is starting the Data + Feminism Lab.
As data are increasingly mobilized in the service of global corporations, governments, and elite institutions, their unequal conditions of produc- tion, their inequitable impacts, and their asymmetrical silences become increasingly more apparent. It is precisely this power that makes it worth asking: "Data science by whom? For whom? In whose interest? Informed by whose values?" And most importantly, "How do we begin to imagine alternatives for data’s collection, analysis, and communication?" These are some of the questions that emerge from what Lauren Klein and I call Data Feminism (forthcoming from MIT Press in early 2020). Data feminism is a way of thinking about data science and its products that is informed by the past several decades of intersectional feminist activism and critical thought, emerging anti-oppression design frameworks, and scholarship from the fields of Critical Data Studies, Science & Technology Studies, Geography/GIS, Digital Humanities and Human Computer Interaction. An intersectional feminist lens prompts questions about how, for instance, challenges to the male/female binary can also help challenge other binary (and empirically wrong) classification systems. It encourages us to ask how the concept of invisible labor can help to expose the gendered, racialized, and colonial forms of labor associated with data work. And it demonstrates why the data never, ever, speak for themselves. In this talk, I will introduce seven principles for data feminist work: examining and challenging power, rethinking binaries and hierarchies, considering context, embracing pluralism, making labor visible, and elevating emotion. The goal of this work is to transform scholarship into action – to operationalize feminism in order to imagine more ethical and more equitable data practices.
Bio
Catherine D'Ignazio is a scholar, artist/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement. She has run women's health hackathons, designed global news recommendation systems, created talking and tweeting water quality sculptures, and led walking data visualizations to envision the future of sea level rise. Her forthcoming book from MIT Press, Data Feminism, co-authored with Lauren Klein, charts a course for more ethical and empowering data science practices. Her research at the intersection of technology, design & social change has been published in the Journal of Peer Production, the Journal of Community Informatics, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). In Jan 2020, D'Ignazio will be an assistant professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT where she is starting the Data + Feminism Lab.