Keywords: Feminism, feminism and science, big data – social aspects, quantitative research, methodology, power (social sciences)
Data Feminism offers strategies for data scientists seeking to learn how feminism can help them toward justice, and for feminists who want to focus their efforts on the growing field of data science.
Authors: Catherine D’Ignazio & Lauren F. Klein
The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. Catherine d’Ignazio and Lauren Klein present a new way of thinking about data science and data ethics – one that is informed by intersectional feminist thought. Thereby, the audience of this book is largely women or other non-cishet white men interested in STEM or data science.
The guidebook openly addresses how women are all too often excluded from data activism or data science spaces and therefore how their influence is absent in the developments of data science today. If these challenges or rather blatant exclusions are addressed, the expansion of data science would improve for the better, especially if the data targeted at women and minorities are more deeply explored.
This guidebook is immensely helpful for groups who want to work with data activism in a progressive manner – including women, minorities, BIPOC folks, disabled folks, or otherwhise excluded groups. This guidebook, while not all-encompassing, is a good start relating to improving revealing invisible labour and ensuring previously excluded groups have space to take part in data science.