The Politics of Images in Machine Learning Training Sets🔒 SSL Verified

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• ↓ Excavating AI
• ↓ Training AI
• ↓ Anatomy of a Training Set
• ↓ Epistemics of Training Sets
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"You open up a database of pictures used to train artificial intelligence systems. At first, things seem straightforward. You’re met with thousands of images: apples and oranges, birds, dogs, horses, mountains, clouds, houses, and street signs. But as you probe further into the dataset, people begin to appear: cheerleaders, scuba divers, welders, Boy Scouts, fire walkers, and flower girls. Things get strange: A photograph of a woman smiling in a bikini is labeled a “slattern, slut, slovenly woman, trollop.” A young man drinking beer is categorized as an “alcoholic, alky, dipsomaniac, boozer, lush, soaker, souse.” A child wearing sunglasses is classified as a “failure, loser, non-starter, unsuccessful person.” You’re looking at the “person” category in a dataset called ImageNet, one of the most widely used training sets for machine learning."

"Where did these images come from? Why were the people in the photos labeled this way? What sorts of politics are at work when pictures are paired with labels, and what are the implications when they are used to train technical systems?"

"There’s an urban legend about the early days of machine vision, the subfield of artificial intelligence (AI) concerned with teaching machines to detect and interpret images. In 1966, Marvin Minsky was a young professor at MIT, making a name for himself in the emerging field of artificial intelligence.[1] Deciding that the ability to interpret images was a core feature of intelligence, Minsky turned to an undergraduate student, Gerald Sussman, and asked him to “spend the summer linking a camera to a computer and getting the computer to describe what it saw.”[2] This became the Summer Vision Project.[3] Needless to say, the project of getting computers to “see” was much harder than anyone expected, and would take a lot longer than a single summer."

"The story we’ve been told goes like this: brilliant men worked for decades on the problem of computer vision, proceeding in fits and starts, until the turn to probabilistic modeling and learning techniques in the 1990s accelerated progress. This led to the current moment, in which challenges such as object detection and facial recognition have been largely solved.[4] This arc of inevitability recurs in many AI narratives, where it is assumed that ongoing technical improvements will resolve all problems and limitations."

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Excavating AI

Training AI

Anatomy of a Training Set

Epistemics of Training Sets