Data Feminism

"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"--

Acknowledgments ixIntroduction: Why Data Science Needs Feminism 11 The Power Chapter 21Principle: Examine Power2 Collect, Analyze, Imagine, Teach 49Principle: Challenge Power3 On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints 73Principle: Elevate Emotion and Embodiment4 "What Gets Counted Counts" 97Principle: Rethink Binaries and Hierarchies5 Unicorns, Janitors, Ninjas, Wizards, and Rock Stars 125Principle: Embrace Pluralism6 The Numbers Don't Speak for Themselves 149Principle: Consider Context7 Show Your Work 173Principle: Make Labor VisibleConclusion: Now Let's Multiply 203Our Values and Our Metrics for Holding Ourselves Accountable 215Auditing Data Feminism, by Isabel Carter 223Acknowledgment of Community Organizations 225Figure Credits 227Notes 235Name Index 303Subject Index 307