"We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand 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 better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought"--
Inhaltsverzeichnis
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
