Excited to launch this new investigation w - to be read alongside ImageNet Roulette. It's about how training data works, the costs of classification, and why "removing bias" or "increasing diversity of data" isn't enough πŸ‘οΈ
Startled by the racist, sexist, odd results of ImageNet Roulette? Wondering how AI learns to see, what the politics of its perception are, and who decides? The brilliant and break it down
This is certainly not a β€˜fix’, and there are still half a million people’s photos there without their knowledge or consent, classified in ways that they’d likely reject. Also, deleting this history raises its own problems- as we explain here
Required Reading! Excavating AI: The Politics of Images in Machine Learning Training Sets
'What if the challenge of getting computers to β€œdescribe what they see” will always be a problem? In this essay, we will explore why the automated interpretation of images is an inherently social and political project, rather than a purely technical one.'
"The Politics of Images in Machine Learning Training Sets” A very interesting and important read from &
A long, very interesting and rather important, read on The Politics of Images in Machine Learning Training Sets