I recently spoke to Trevor Paglen – well known for works like ‘Limit Telephotography’ (2007-2012) and its images of NSA buildings and deep-sea fibreoptic cables – about surveillance, machine vision, and the changing politics of the visible / machine-readable. Full piece @ Art in America.
Much of that discussion – around the proliferation of images created by and for machines, and the exponential expansion of pathways by which surveillance, data, and capital can profitably intersect – is also taken up in my upcoming book, Technologies of Speculation (NYUP 2020). There my focus is on what happens after Snowden’s leaks – the strange symbiosis of transparency and conspiracy, the lingering unknowability of surveillance apparatuses and the terrorists they chase. It also examines the passage from the vision of the Quantified Self, where we use all these smart machines to hack ourselves and know ourselves better, to the Quantified Us/Them which plugs that data back into the circuits of surveillance capitalism.
In the piece, Paglen also discusses his recent collaboration with Kate Crawford on ImageNet Roulette, also on display at the Training Humans exhibition (Fondazione Prada Osservertario, Milan):
“Some of my work, like that in “From ‘Apple’ to ‘Anomaly,’” asks what vision algorithms see and how they abstract images. It’s an installation of about 30,000 images taken from a widely used dataset of training images called ImageNet. Labeling images is a slippery slope: there are 20,000 categories in ImageNet, 2,000 of which are of people. There’s crazy shit in there! There are “jezebel” and “criminal” categories, which are determined solely on how people look; there are plenty of racist and misogynistic tags.
If you just want to train a neural network to distinguish between apples and oranges, you feed it a giant collection of example images. Creating a taxonomy and defining the set in a way that’s intelligible to the system is often political. Apples and oranges aren’t particularly controversial, though reducing images to tags is already horrifying enough to someone like an artist: I’m thinking of René Magritte’s Ceci n’est pas une pomme (This is Not an Apple) [1964]. Gender is even more loaded. Companies are creating gender detection algorithms. Microsoft, among others, has decided that gender is binary—man and woman. This is a serious decision that has huge political implications, just like the Trump administration’s attempt to erase nonbinary people.”
Crawford & Paglen also have a longer read on training sets, Excavating AI (also source for above image).
Sun-ha Hong 