FEED issue 28 Web


divide between the types of things that are valued – these are numbers, bigness, objectivity, distance from the subject at hand, things that historically and culturally tend to be coded male, and the things that historically have been associated with women. These are things that appeal more to emotion rather than reason, things that are maybe smaller, more nuanced, maybe qualitative data rather than quantitative data. Those things have sort of been devalued. We trace this back to binary thinking, which feminist theory for many years has helped to challenge and we show how things are never an either/or. It’s always a both/and. And usually when there’s some sort of binary, it’s hiding some sort of hidden hierarchy and there’s a reason why certain people want it like that. 6. 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. 7. Make labour visible: The work of data science is the work of many hands. Data feminism makes this labour visible so that it can be recognised and valued. 1. Examine power: Data feminism begins by analysing how power operates in the world. 2. Challenge power: Data feminism commits to challenging unequal power structures and working toward justice. 3. Elevate emotion & 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. 4. Rethink binaries & hierarchies: Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression. 5. Embrace pluralism: Data feminism insists that the most complete knowledge comes from synthesising multiple perspectives, with priority given to local, indigenous and experiential ways of knowing. SEVEN CORE PRINCIPLES OF DATA FEMINISM

FEED: So a very basic question, or maybe not so basic: what is data?

I was working on called Feminist Data Visualisation. It turned out that Catherine and I had a couple of mutual friends who saw that we were working on the same things. We originally wrote a short article together for the visualisation community, for the IEEE VIS conference on the idea of feminist data visualisation. We asked what would its principles be. How could you put it into action? Then Catherine was approached by an editor of a new series at MIT Press who said: “I actually think this could be a book”. When someone asks if you want to write a book for MIT Press, you don’t say no. And so we were off and running. Unfortunately, though not surprisingly, the stuff that we write about in the book is very topical for everything we’re experiencing right now.

LAUREN KLEIN: Data is one of those words that you think should have an easy definition, but how people choose to define data, as we explain in the book, is actually quite contextual and political. We say in the book that data is pretty much anything that has been systematically collected. So data can be qualitative or quantitative. It can be numbers, it can be words, it can be big, it can be small. And this is sort of pushing back at the idea of data as only Big Data, or as numbers that are seemingly objective. We talk about what it means to be truly objective, and why what people think is objective is not necessarily actual, objective information. There’s this historical gender

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