Seven intersectional feminist principles for equitable and actionable COVID-19 data

• Type of Material: Article
• Authors: Catherine D'Ignazio, Massachusetts Institute of Technology; Lauren Klein, Emory University
• Language: English
• Date: 2020-07-30
• Publisher: SAGE Publications (UK and US)
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2020.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1177/2053951720942544
• Title of Journal or Parent Work: Big Data & Society
• Volume: 7
• Issue: 2
• Grant/Funding Information: The author(s) received no financial support for the research, authorship, and/or publication of this article.
• Keywords: data collection; counterdata; Data science; coronavirus; antiracism; feminism
• Research Categories: Statistics; Gender Studies; Sociology, Ethnic and Racial Studies; Health Sciences, Public Health; Urban and Regional Planning

Abstract

This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism (D'Ignazio and Klein, 2020), offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning the work of change.