Data Feminism

Data Feminism

Data Feminism

Data Feminism


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A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.

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.

Product Details

ISBN-13: 9780262044004
Publisher: MIT Press
Publication date: 03/17/2020
Series: Strong Ideas
Pages: 328
Product dimensions: 8.20(w) x 9.10(h) x 1.00(d)
Age Range: 18 Years

About the Author

Catherine D'Ignazio is Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT.

Lauren F. Klein is Associate Professor of English and Quantitative Theory and Methods at Emory University.

Table of Contents

Acknowledgments ix

Introduction: Why Data Science Needs Feminism 1

1 The Power Chapter 21

Principle: Examine Power

2 Collect, Analyze, Imagine, Teach 49

Principle: Challenge Power

3 On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints 73

Principle: Elevate Emotion and Embodiment

4 "What Gets Counted Counts" 97

Principle: Rethink Binaries and Hierarchies

5 Unicorns, janitors, Ninjas, Wizards, and Rock Stars 125

Principle: Embrace Pluralism

6 The Numbers Don't Speak for Themselves 149

Principle: Consider Context

7 Show Your Work 173

Principle: Make Labor Visible

Conclusion: Now Let's Multiply 203

Our Values and Our Metrics for Holding Ourselves Accountable 215

Auditing Data Feminism, by Isabel Carter 223

Acknowledgment of Community Organizations 225

Figure Credits 227

Notes 235

Name Index 303

Subject Index 307

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From the Publisher

Modern Language Association Prize for Collaborative, Bibliographical, or Archival Scholarship, 2022.

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