This Designer Is Fighting Back Against Bad Data–With Feminism by Katharine Schwab.
From the post:
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“Intersectionality,” declares one in all caps. “Men Explain Things to Me– Solnit,” another one reads, referencing a 2008 essay by the writer Rebecca Solnit. “Is there a feminist programming language?” asks another. “Buffy 4eva,” reads an orange Post-it Note, next to a blue note that proclaims, “Transwomen are women.”These are all ideas for the themes and pieces of content that will inform the “Feminist Data Set”: a project to collect data about intersectional feminism in a feminist way. Most data is scraped from existing networks and websites or collected by surveilling people as they move through digital and physical space–as such, it reflects the biases these existing systems have. The Feminist Data Set, on the other hand, aspires to a more equitable goal: collaborative, ethical data collection.
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Step one? Sinders asks everyone in the room to spend five minutes brainstorming ideologies (like femininity, virtue, and implicit bias) and specific pieces of content (like old maid, cyberfeminism, and Mary Shelley) for the data set on sticky notes. Then, the entire group organizes them into categories, from high-level ideological frameworks down to individual pieces of content. The exercise is a chance for a particular artistic community to have a say over what feminist data is, while participating in an open-source project that they’ll one day be able to use for their own purposes. Right now, the data set includes a gender-neutral dictionary, essays by Donna Haraway, and journalist Clare Evans’s new book, Broad Band, a female-centric history of computing.
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If you know the work of Caroline Sinders, @carolinesinders, you are already following her. If you don’t, get to Twitter and follow her!
There are any number of aspects of Sinders’ work that are important but the “Feminist Data Set” foregrounds one that is often overlooked.
As you start to speak, the mere shifting your weight to enter a conversation, you are making decisions that will shape the data set that results from a group discussion.
No ill will or evil intent on your part, or anyone else’s, but the context that shapes our contributions, the other voices, prior suggestions, all shape the resulting view of “data.” Moreover, that shaping is unavoidable.
I see Sinder’s as pulling to the foreground what is often taken as “that’s the way it is.” No indeed, data is never the way it is. Data and data sets are the product of social agreements between people, people no more or less skilled than you.
This looks like deeply promising work and I look forward to hearing more about its progress.