Highlights

Underlying 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

Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression. ^ref-33662

So 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

The 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

Examining 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

Metadata

Author: Catherine D'Ignazio and Lauren F. Klein
ASIN: B08BT3SY5N
ISBN: 0262044005
Reference: https://www.amazon.com/dp/B08BT3SY5N
Kindle link

Data Feminism

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