Data Feminism

Catherine D'Ignazio, Lauren F. Klein, David Weinberger

• Publisher: The MIT Press
• Year: 2020
• eBook ISBN: 9780262358538
• Edition: 0
• Topic: Social Sciences
• Subtopic: Feminism & Feminist Theory
• Index: Social Sciences

Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, 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.

Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves."

Data Feminism offers strategies 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. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

The open access edition of this book was made possible by generous funding from the MIT Libraries.

Table of contents

• Cover
• Contents
• Acknowledgments
• Introduction: Why Data Science Needs Feminism
• 1 The Power Chapter
• 2 Collect, Analyze, Imagine, Teach
• 3 On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints
• 4 “What Gets Counted Counts”
• 5 Unicorns, Janitors, Ninjas, Wizards, and Rock Stars
• 6 The Numbers Don’t Speak for Themselves
• 7 Show Your Work
• Conclusion: Now Let’s Multiply
• Our Values and Our Metrics for Holding Ourselves Accountable
• Auditing Data Feminism, by Isabel Carter
• Acknowledgment of Community Organizations
• Figure Credits
• Notes
• Name Index
• Subject Index