Data Feminism

Catherine D'Ignazio is Assistant Professor of Urban Science and Planning at MIT and coauthor of Data Feminism (MIT Press).Lauren F. Klein is Associate Professor of English and Quantitative Theory and Methods at Emory University.

• 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

“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"Anyone who works with data—and all scientists do, of course—will benefit from reading this book. But the readers who may gain the most from it are those who are trying to use data in the public interest. Data Feminism does such a good job of integrating theories and projects across several fields that it will likely become a touchstone for teaching data science that goes beyond data ethics."—American Scientist“...the authors' demystification of data science and advocacy for data feminism are extremely timely. The book also serves as an important introduction to intersectional feminist practice by providing inspiring examples of marginalized women and communities taking power back by collecting and wielding “counter-data” to challenge the status quo.”—Times Higher Education"This call to action is especially important as issues of power and privilege continue to re-create inequalities in contemporary society."—CHOICE“‘Data Feminism is a powerful call to action for everyone who cares about how technology reflects and reproduces social hierarchies and injustices. Brilliantly argued, engagingly written, and collaboratively crafted, this groundbreaking work enacts a feminist politics of knowledge production that will serve as a guide for generations to come.”—Ruha Benjamin, Princeton University; author of Race after Technology “Data Feminism is an exceptional and entertaining primer for data scientists to understand essential ethical concepts like power, inequality, gender, and race.”—DJ Patil, Head of Technology at Devoted Health, Inc., Former U.S. Chief Data Scientist  “If you want to build a foundation in data ethics and data justice, Data Feminism is a must-read. D'Ignazio and Klein have written a remarkable book that defines the kind of critical, intersectional feminist thinking we need right now. I can think of no better entry point to understand digital technology and its impact on society than Data Feminism, which amplifies so many important ideas we need to act upon. This book is a major contribution in defining what biased and harmful data is, and more importantly, what we can do about it.”—Safiya Umoja Noble, UCLA; author of Algorithms of Oppression: How Search Engines Reinforce Racism and coeditor of The Intersectional Internet: Race, Sex, Class and Culture Online “Most thinking about data science and data visualization tends to focus on statistics and technique. D'Ignazio and Klein take us out of that daze, opening our eyes to the realities that lie behind every data set: its motivation, its biases, and its existence in a harshly unequal world. Required reading for data scientists looking to conduct their craft responsibly.”—Fernanda Viégas. Senior Researcher, co-leader at People + AI Research, Google“Data Feminism belongs on the shelf with Algorithms of Oppression as required reading for understanding historical patterns of oppression and society’s current obsession with data-driven decision making.”–RGWS: A Feminist Review