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“the privilege hazard: the phenomenon that makes those who occupy the most privileged positions among us—those with good educations, respected credentials, and professional accolades—so poorly equipped to recognize instances of oppression in the world.29”
― Data Feminism
― Data Feminism
“The key question to keep in mind is how we can scale up data for co-liberation in ways that remain careful, community-based, and complex.”
― Data Feminism
― Data Feminism
“I’m forced to choose ‘female’ over ‘male’ every single time, because that’s what my passport says, and ... being non-binary is still not legally recognised in the UK,” Munir explains.”
― Data Feminism
― Data Feminism
“write a short history of a particular dataset and answer five basic questions: Where did it come from? Who collected it? When? How was it collected? Why was it collected?”
― Data Feminism
― Data Feminism
“When approaching any new source of knowledge, whether it be a dataset or dinner menu (or a dataset of dinner menus), it’s essential to ask questions about the social, cultural, historical, institutional, and material conditions under which that knowledge was produced, as well as about the identities of the people who created it.”
― Data Feminism
― Data Feminism
