Lauren Klein

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.

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.

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?

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.

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?

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.

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.

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.

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?

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.

Who makes maps and who gets mapped? Using a comparative reading of three maps, this case study introduces the idea that data may be useful, but they are not neutral. Rather, they represent the interests and goals of the groups and institutions that are doing the data ...

🎧 The authors of Data Feminism reunite to discuss the joys and benefits of community review.

Many of the techniques used by Artificial Intelligence systems - from official statistics to health records to online metadata to sensors and satellites - rely on training via vast quantities of data.

DataGénero (Coordinación: Mailén García. Traductoras: Ivana Feldfeber,Sofía García, Gina Ballaben, Giselle Arena y Mariángela Petrizzo. Revisión: Helena Suárez Val.Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Ivana Feldfeber,Sofía García, Gina Ballaben y Mariángela Petrizzo. Revisión: Helena Suárez Val. Con la ayuda deDiana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Ivana Feldfeber,Sofía García, Gina Ballaben, Giselle Arena y Mariángela Petrizzo. Revisión: Helena Suárez Val.Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Mailén García. Revisión: Helena Suárez Val. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Mailén García.Revisión: Helena Suárez Val. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, yPatricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Mailén García. Revisión: Helena Suárez Val. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Sofía García. Revisión: Helena Suárez Val y Mailén García. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Gina Ballaben. Revisión: Helena Suárez Val y Mailén García. Con la ayuda de Diana Duarte Salinas, Ana AmeliaLetelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Gina Ballaben. Revisión: Helena Suárez Val y Mailén García. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Sofía García. Revisión: Helena Suárez Val y Mailén García. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: SofíaGarcía. Revisión: Helena Suárez Val y Mailén García. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

DataGénero (Coordinación: Mailén García. Traductoras: Sofía García. Revisión: Helena Suárez Val y Mailén García. Con la ayuda de Diana Duarte Salinas, Ana Amelia Letelier, y Patricia Maria Garcia Iruegas)

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