Other: The Dangers of Sorting People by Race and Sex

Award-winning science journalist Angela Saini investigates the bias lurking within data collection and human classification, and interrogates what purpose it truly serves.

Our societies are underpinned by technologies that rely on being able to sort us into categories as defined by governments, big institutions and corporations. We’re told that the more data we provide, the better off we will be. Angela Saini explains why this faith is misplaced and how tick-boxes can harm us.

Data systems built on the shaky foundation of human classification have turned out to be riddled with errors, some so fatal that they’re exacerbating inequality and discrimination. At best, the categories we’re offered are shadowy versions of reality; at worst, they perpetuate crude stereotypes.

Extraordinary lengths are taken to make sure nobody goes unclassified, but why is it so important to those in power that we be relentlessly defined by race, gender, caste and disability? When systems fail, we blame not having enough data, or the way that data is used, but we never ask if the root of our problems is the foundational myth that putting people in boxes actually works. Saini argues that classification too often reinforces the same injustices that created those categories in the first place.

OTHER calls for a radical rethinking of the use of human classification, with potentially profound implications for data collection, social policy, healthcare and scientific research – as well as how we imagine ourselves.