Feminism begins with a belief in the “political, social, and economic equality of the sexes,” as the Merriam-Webster Dictionary defines the term—as does, for the record, Beyoncé. And any definition of feminism also necessarily includes the activist work that is required to turn that belief into reality. In Data Feminism, we bring these two aspects of feminism together, demonstrating a way of thinking about data, their analysis, and their display, that is informed by this tradition of feminist activism as well as the legacy of feminist critical thought.
Chapter 1: The Power Chapter
Principle #1 of Data Feminism is to Examine Power. Data feminism begins by analyzing how power operates in the world.
Chapter 2: Collect, Analyze, Imagine, Teach
Principle #2 of Data Feminism is to Challenge Power. Data feminism commits to challenging unequal power structures and working toward justice.
Chapter 3: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints
Principle #3 of Data Feminism is to Elevate Emotion and Embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world.
Chapter 4: “What Gets Counted Counts”
Principle #4 of Data Feminism is to Rethink Binaries and Hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.
Chapter 5: Unicorns, Janitors, Ninjas, Wizards, and Rock Stars
Principle #5 of Data Feminism is to Embrace Pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
Chapter 6: The Numbers Don’t Speak for Themselves
Principle #6 of Data Feminism is to Consider Context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis.
Chapter 7: Show Your Work
Principle #7 of Data Feminism is to Make Labor Visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued.
Conclusion: Now let’s multiply
The core workers [tech companies] rely on have an extraordinary amount of bargaining power.” They also have messaging power, interruption power, and subversion power. How might tech workers marshal these strengths to mass-occupy digital infrastructure? To teach algorithms to “work to rule” in the style of assembly-line slow-downs? To slow the flow of everyday capitalism to gather attention? To channel digital solidarities back into physical spaces and human relationships?
Our values and our metrics for holding ourselves accountable
We created this document as we began the writing of this book and included it as part of the manuscript draft that was posted online as part of the open peer review process. We were prompted to write it because of work on a prior project with equity consultant Jenn Roberts of Versed Education, and because of the values statements (and related statements of principles) published by groups such as the University of Maryland’s African American History, Culture, and Digital Humanities Initiative (AADHum) and the University of Delaware’s Colored Conventions Project. From these projects, we saw how statements of shared values can become important orientation points, guiding internal decisions at challenging junctures and making ethical commitments public and transparent.
Auditing Data Feminism, by Isabel Carter
In the interest of remaining accountable to the values statement for this book, the authors tasked me with performing an audit of all the individuals, projects, and organizations referenced in Data Feminism. Quantifying these references provided important information about which perspectives were being included in the work and to what extent. At the same time, this process presented difficult-to-answer questions about identification and classification. Therefore, this methods statement will serve to explain how we approached those questions and what our answers were.
Acknowledgment of Community Organizations
Many community organizations are already modeling the principles of data feminism that we have described in this book. As part of our continued efforts to share power, we are redirecting a portion of royalties from this book to two of these organizations that have significance to the authors. We encourage you to seek out these organizations, engage with them on social media and in real life, and consider how you might contribute to their work.
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