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Editors’ Choice: What can data visualization learn from feminism?

It’s about time to infuse feminism into data science and visualization. At least, that’s what Emerson data visualization and civic tech professor Catherine D’Ignazio says based on her research into what an intersectional feminist perspective on data could look like.

“We’re in this moment when big data and visualization are being heralded as powerful new ways of producing knowledge about the world,” D’Ignazio said at a recent talk hosted by the Northeastern University Visualization Consortium. “So whenever anything has lots of power and is valued very widely by society, we just want to interrogate that a little more and say ‘Is it being valued equally?’ and ‘Is it benefitting all people equally?’”

She and her research partner found that the field has major problems with inequality, inclusion and quantification. Those who have the resources to collect, store, maintain, analyze and derive insight from large amounts of data are generally corporations, governments and universities. This creates an imbalance between who data is about and who has access to that data.

There is an imbalance between who data is about and who has access to that data.

D’Ignazio says this issue is compounded by the fact that women and people of color are underrepresented in data science and technical fields in general, a trend that is worsening. She also highlights skewed quantity and quality of data that is collected about various groups of people. For instance, there are very detailed datasets on gross domestic product and prostate function, but very poor datasets on hate crimes and the composition breast milk.

“Even when there is institutional and political will to collect data, data on sensitive topics — such as domestic violence, war crimes, sexual assault — is often highly flawed because there is powerful incentives for institutions and individuals not to report, not to collect, not to come forward,” she said.

So how do we take a feminist perspective on the design of visualizations? D’Ignazio cited six points that might bring us there.


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