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Catherine D'Ignazio

Assistant Professor, Emerson College
Catherine D'Ignazio is a scholar, artist/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement.
Boston, MAwww.kanarinka.comkanarinkakanarinka
All Pubs (14)Authored Pubs (0)

About Us

Published: Oct 17, 2018

Code of Conduct

Published: Oct 30, 2018

Our Values and Our Metrics for Achieving Them

Published: Oct 30, 2018

Acknowledgments

Published: Oct 20, 2018

Introduction

Published: Nov 06, 2018
Intersectional feminism isn't just about women nor even just about gender. Feminism is about power – who has it and who doesn’t. And in a world in which data is power, and that power is wielded unequally, data feminism can help us understand how it can be challenged and changed.

Chapter One: Bring Back the Bodies

Published: Nov 01, 2018
Why do data science and visualization need feminism? Because bodies are missing from the data we collect, from the decisions made about their analysis and display, and from the field of data science as a whole. Bringing back the bodies is how we can right this power imbalance.

Chapter Two: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

Published: Nov 01, 2018
Sociologist Patricia Hill Collins describes an ideal knowledge situation as one in which "neither ethics nor emotions are subordinated to reason." So why has emotion been so systematically excluded from data visualization? What happens when we bring back emotion and embodiment?

Chapter Three: “What Gets Counted Counts”

Published: Nov 05, 2018
Feminists have spent a lot of time thinking about categories, since “male” and “female” are binary categories, and limited categories too. How we count matters as much as what we count. But we don't always count-- or account for-- what is most important to the questions at hand.

Chapter Four: Unicorns, Janitors, Ninjas, Wizards, and Rock Stars

Published: Nov 05, 2018
Unicorns, wizards, ninjas, rock stars and janitors all have something in common: they all work alone. But what might be gained if we understood data work not as a solitary undertaking, but as one that embraced multiple voices and forms of expertise at all phases of the process?

Chapter Five: The Numbers Don’t Speak for Themselves

Published: Nov 05, 2018
Do numbers ever speak for themselves? The short answer: no. The longer answer: no. In this chapter, we explain why context and theory matter deeply for the datasets that we employ in our work, the questions we ask about them, and the methods we use to arrive at our answers.

Chapter Six: Show Your Work

Published: Nov 05, 2018
The products of data science are the work of many hands. Unfortunately, though, we tend not to credit the many hands who perform this work. Sometimes, it's because we can't see the people who performed it, but other times, it's because the work itself is invisible to the eye.

Chapter Seven: The Power Chapter

Published: Nov 01, 2018
Examining how power is wielded through data means participating in projects that wield it back. The projects we discuss in this chapter deal openly and explicitly with questions about power, and name the structural forces like sexism and racism that lead to power imbalances.

Chapter Eight: Teach Data Like an Intersectional Feminist!

Published: Nov 05, 2018
Much of current data science education functions as a "Man Factory", focused on reproducing data work that is abstract, individual, & led by elite men. But what if we imagined teaching data as a place to start creating the connected, collective, caring world that we want to see?

Conclusion: Now Let’s Multiply

Published: Nov 05, 2018
A feminist approach to data science, to visualization, or to anything else in the world, cannot account for all perspectives on inequality. Here we point to some additional bodies of work that can help inform our understanding, action and activism around power and data.
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