Full Description
Cutting edge strategies for thinking about data science and data ethics through an intersectional feminist lens.
"Without ever finger-wagging, Data Feminism reveals inequities and offers a way out of a broken system in which the numbers are allowed to lie."—WIRED
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.
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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