Data Feminism casts a feminist perspective on data science. The book is organized around a set of principles intended to show and do feminist data work:

1. Examine power;
2. Challenge power;
3. Elevate emotion and embodiment;
4. Rethink binaries and hierarchies;
5. Embrace pluralism;
6. Consider context;
7. Make labour visible.

D’Ignazio and Klein draw on feminist Science and Technology Studies (STS), critical theory, and information science scholars to contest that data are “never neutral; they were always the biased output of unequal social, historical, and economic conditions: this is the matrix of domination once again” (39). Data must be contextualized and recognized as necessarily partial and necessarily constructed. Throughout the book, D’Ignazio and Klein grapples with themes of situated knowledges and epistemic violence or epistemic injustice. Whose bodies and knowledge gets counted? they ask. Why or why not?

 

Work cited

D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. Cambridge, MA: MIT Press.