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Kosinski asserted in an interview with Quartz that regardless of the methods of his paper, his research was in service of gay and lesbian people that he sees under siege in modern society.
By showing that it’s possible, Kosinski wants to sound the alarm bells for others to take privacy-infringing AI seriously.
A paper late last year tried to prove a similar algorithm could tell if someone was a criminal from their face—it was later shown that the original data for “innocent” people were filled with businessmen wearing white collars.
The algorithm thought if you wore a white collar you were innocent.
It’s a deep-learning algorithm custom-built for working with faces, which means the original authors of the software, a group from the highly-regarded Oxford Vision Lab, went through a lot of pains to make sure it focuses on the face and not a face’s surroundings.
It’s been shown to be great at recognizing people’s faces across different images and even finding people’s doppelgängers in art.
He’s in an undeniably tough place: Defending the validity of his work because he’s trying to be taken seriously, while implying that his methodology isn’t even a good way to go about this research.
But some AI researchers doubt that VGG-Face is actually ignoring expression and pose, because the model isn’t being used for its intended use, to simply identify people.
It’s important to focus only on the face because deep-learning algorithms have been shown to pick up on biases in the data they analyze.
When they’re looking for patterns between data, they pick up all kinds of other patterns that may not be relevant to the intended task but affects the outcome of the machine’s decision.
“Super standard, super simple.” Once the algorithm has analyzed those patterns, it should be able to find similar patterns on new images.
Researchers typically set a few images aside from the data the algorithm is taught with in order to test it and make sure it’s actually learning patterns between people in general and not just those specific people.
He says his work stands on the shoulders of research happening for decades—he’s not reinventing anything, just translating known differences about gay and straight people through new technology.