Catherine D’Ignazio: 'Data is never a raw, truthful input – and it is never neutral'

The co-author of Data Feminism on the importance of recognising discrimination in algorithms, understanding it at a technical level - and introducing measures to stamp it out

Our ability to collect and record information in a digital form has exploded as has our adoption of AI systems, which use data to make decisions. But data isn't neutral, and sexism, racism and other forms of discrimination are showing up in our data products. Catherine D'Ignazio, an assistant professor of urban science and planning at the Massachusetts Institute of Technology (MIT), argues we need to do better. Along with Lauren Klein, who directs the Digital Humanities Lab at Emory University, she is the co-author of the new book Data Feminism, which charts a course for a more equitable data science. D'Ignazio also directs MIT's new Data and Feminism lab, which seeks to use data and computation to counter oppression.

What is data feminism and why do we need it?
It is data science with an intersectional feminist lens. It takes all inequality into account at every stage of the data processing pipeline, including gender discrimination but also other forms of intersecting discrimination like racism, classism and ableism. And the reason we need it is to stop producing harmful racist and sexist data products.