Data Feminism

Welcome to the community review site for Data Feminism. Thank you for your generosity and time in choosing to read and comment on this manuscript draft. The review period for this draft will close on January 7, 2019, although the ability to leave comments will still be available after that point.

We have chosen to put this draft online because of a foundational principle of this project: that all knowledge is incomplete, and that the best knowledge is gained by bringing together multiple partial perspectives. A corollary to this principle is that our own perspectives are limited, especially with respect to the topics and issues that we have not personally experienced. As we describe more fully in our values statement, we recognize that the people who are most directly affected by specific topics and issues are the ones who know the most about them. In our book, we have attempted to elevate their voices, and amplify their ideas. In our attempt to do so, we have also likely made mistakes. We strive to be reflexive and accountable in our work, and we hope to learn from you about places where we’ve gotten things wrong, and about how we can do better.

This is a book that aspires to speak to multiple audiences. These include professionals such as data scientists, data journalists, visualization designers, and software developers, as well as activists and organizers who work with data. Additional audiences include students and scholars from a range of academic fields, including digital humanities, women's and gender studies, critical race studies, media studies, information science/studies, STS, HCI, and information visualization, among others. We also welcome your help in pointing out any places that may require additional explanation, or that may not be accessible to newcomers in those professions and fields.

We are grateful to those who have shown us generosity in giving us their feedback up to this point. To readers of this manuscript—our future teachers—we commit to being open listeners. We recognize direct and critical words as generosity, and as a vote of confidence in our ability to hear and be transformed by you.

Below you will find the complete draft of the manuscript, as well as our values statement, and a code of conduct for commenting. Please feel free to email Catherine and/or Lauren with any comments that you would rather not publicly disclose.

Thank you, once again, for your generosity and time. We look forward to learning from you.

Catherine D’Ignazio, Assistant Professor, Emerson College

Lauren Klein, Associate Professor, Georgia Institute of Technology


Nov 06, 2018Updated: Jan 12, 2019
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.
Nov 01, 2018Updated: Jan 14, 2019
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.
Nov 01, 2018Updated: Jan 14, 2019
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?
Nov 05, 2018Updated: Jan 14, 2019
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.
Nov 05, 2018Updated: Jan 14, 2019
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?
Nov 05, 2018Updated: Jan 15, 2019
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.
Nov 05, 2018Updated: Jan 15, 2019
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.
Nov 01, 2018Updated: Jan 15, 2019
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.
Nov 05, 2018Updated: Jan 15, 2019
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?
Nov 05, 2018Updated: Jan 15, 2019
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.

Additional Reading

Oct 17, 2018Updated: Oct 30, 2018
Oct 20, 2018Updated: Nov 02, 2018
Oct 30, 2018Updated: Nov 02, 2018

Data Feminism cover image: Digital visualizations by Christopher Pietsch and Siqi Zhu from Art of the March, an archival project led by Alessandra Renzi, Dietmar Offenhuber, and Nathan Felde, based on posters collected from the 2017 Boston Women's March.