• Lorde, Audre, 1984: Sister Outsider: Essays and Speeches. Berkeley: University of California Press, 110.
• Sekundärquelle zu Audre Lorde
• Dhawan, Nikita, 2013: Coercive Cosmopolitanism and Impossible Solidarities. Duke University Press: Qui Parle, 156f.
• ??? Die Quelle zu diesem Zitat habe ich (js) leider noch nicht finden können. Im Text von 2013 nimmt Dhawan auf Seite 157 aber Bezug zu den „Master's Tools“ und zum „Master's house“ von Audre Lorde

Rückblick der Autorinnen

• „ In this book, we have described intersectional feminism — a vibrant body of knowledge and action that challenges the unequal distribution of power - and how it can be applied to the field of data science today. […] We hope that you, our readers, will use this work in order to reflect on your own identities, as well as to examine how power and privilege operate in data science and in the world..“ (207)

Seven Principles of Data Feminism

1. Examine Power: „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. Seeing oppression is especially hard for those of us who occupy positions of privilege“ (24)
2. Challenge Power: „Data feminism commits to challenging unequal power structures and working toward justice“ (49)
3. Elevate Emotion and Embodiment: „Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world“(73)
4. Rethink Binaries and Hierarchies: „Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression“ (97)
5. Embrace Pluralism: „Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing“ (125)
6. Consider Context: „Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis“ (149)
7. Make Labor Visible: „The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued“ (173)

Das Ziel der Autorinnen:
“This book is intended to provide concrete steps to action for data scientists seeking to learn how feminism can help them work toward justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is also addressed to professionals in all fields in which data-driven decisions are being made, as well as to communities that want to resist or mobilize the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to communicate the significance of such charts and statistics to others.” 19

Matrix of domination (24-25) ⇒ (Power Chapter)

Die Matrix of domination, inspiriert durch Partricia Hill Collins, soll erklären, wie sich Machtsysteme formieren und wie sie wirken, bzw. erlebt werden (24f).