Data Feminism is a book written by Catherine D’Ignazio and Lauren F. Klein as part literature review, part call to action, Data Feminism provides a framework for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. Through seven chapters Data Feminism provide examples of data biases and injustices, as well as strategies to redress them. In doing so, D’Ignazio and Klein suggest data feminism as "a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by intersectional feminist thought".[1] The chapters are organised according to seven guiding principles (see below): examine power, challenge power, elevate emotion and embodiment, rethink binaries and hierarchies, embrace pluralism, consider context, and make labor visible.[1]
The starting point for data feminism is something that has gone mostly unacknowledged in data science: power is not distributed equally in the world. Data science is a form of power, and it can be used to uphold existing hierarchies or, alternatively, to discover and redress injustices. The book therefore consistently emphasises why data never, ever “speak for themselves", and how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. The authors explain how, for example, a better understanding of emotions challenges and improves ideas about effective data visualization, and how the concept of invisible labor exposes the significant human efforts behind technologies and data-related work.[2]
The authors apply an intersectional feminist framework to data science. Using this framework the authors examine intertwined structural forces of power such as sex, race, sexuality, and class. The authors therefore also explicitly focus on data justice, as opposed to data ethics, arguing that data ethics and its focus on fairness and biases create structures that protect dominant powers.[3]
^ abD'Ignazio, Catherine; Klein, Lauren F. (2020). Data Feminism. The MIT Press. doi:10.7551/mitpress/11805.001.0001. ISBN 978-0-262-35852-1. S2CID 241838270.
^"Data Feminism · MIT Press Open". MIT Press Open.
^Cite error: The named reference :1 was invoked but never defined (see the help page).
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