“As a queer Black woman living in a white supremacist world, the traumas I endure and the impacts on my health are never-ending.”

The point of this essay is to encourage a critical approach to the relationship between race and data. It points to three questions that anyone working with data should ask if they are going to be collecting and using data about race. § If we are not careful, data can divide and sort us in exactly the sort of essentialising ways that the colonial idea of race supported. But if researchers ask the right questions, and know their history, we can use data to advocate for racial justice.

Black people are experts in their lived experience of racism.That does not automatically mean Black people are experts in Diversity and Inclusion (D&I) or Anti-Racism. § Having a corridor chat with a random member of staff who happens to be Black to pick their brains about D&I and Anti-Racism policy is not strategic. It's tokenism. And tokenism is a byproduct of racism.

Tools like GPT-3 are stunningly good, but they feed on the cesspits of the internet. How can we make them safe for the public to actually use? § Sometimes, to reckon with the effects of biased training data is to realize that the app shouldn't be built. That without human supervision, there is no way to stop the app from saying problematic stuff to its users, and that it's unacceptable to let it do so.

Many of the public and private services we use are now digital. The move to digital is likely to increase as technology becomes more embedded in our lives. But what does this mean for how essential public services understand who is using, or indeed not using, them and why? § Data about the protected characteristics of people using these services isn’t currently collected andstatistics aren’t published in a consistent or collective way. This means it is harder tofind out who is excluded from using these services and why.