\n\n\nUnderlying data feminism is a belief in and commitment to co-liberation: the idea that oppressive systems of power harm all of us, that they undermine the quality and validity of our work, and that they hinder us from creating true and lasting social impact with data science. ^ref-16557
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\n\n\nData feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression. ^ref-33662
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\n\n\nSo what were these complications, more precisely? And how many women had actually died as a result? Nobody was counting. A 2014 United Nations report, coauthored by SisterSong, described the state of data collection on maternal mortality in the United States as “particularly weak.” ^ref-24631
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\n\n\nThe chief of the CDC’s Maternal and Infant Health branch, William Callaghan, makes the significance of this “embarrassing” data more clear: “What we choose to measure is a statement of what we value in health,” he explains.7 We might edit his statement to add that it’s a measure of who we value in health, too.8 ^ref-53282
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\n\n\nExamining power means naming and explaining the forces of oppression that are so baked into our daily lives—and into our datasets, our databases, and our algorithms—that we often don’t even see them. ^ref-1470
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