Yes, an absolutely classic example of implicit biases in training sets.
On the one hand, the network should eventually learn to classify high heels as shoes.
On the other, when these classification system actually get used, they're always at some arbitrary point in their training, so you can't just wait for "all the biases to go away."
Erm... high heels are not the only kind of shoes women wear. They're not even the most common kind of shoes women wear. Pointing to this as a 'gender disparity experience' is showing your own bias. Yes, high heels are shoes and it should learn to recognise them, but most women don't actually wear them most of the time.
Sit in a shopping centre, movie theater lobby, or even just out on the street. Watch the shoes of the women as they stroll by[1], and you'll find very few of the ridiculously high heels that are pictured in that tweet. Claiming that tweet's shoe as the typical women's shoe is laughably erroneously stereotyped.
[1] Not just the young fashionistas that specifically dress up, but every woman.
I don't think that's true for shoes. The male equivalent to high heels would be dress shoes, and women wearing male dress shoes would be weird and unusual. The examples shown appear to be casual or athletic shoes, which are indeed neutral.
You might be able to explain it, but it still shows that it's wrong. (Though I disagree that these shoes are gender neutral; only ~5% of the shoes in my household look like "gender-neutral" shoes, and they're all mine)
The comment was pointing out a specific example about how an AI miscategorized something because of a small sample-size in data, something that has been shown to be often the result of unintended biases in the training set, and you say that we're just talking about "gender identities"??
This is the kind of thing AI researchers write papers on (source: AI MSc), not some SJW topic, yet you saw the word "gender" and assumed it didn't belong?
On the one hand, the network should eventually learn to classify high heels as shoes.
On the other, when these classification system actually get used, they're always at some arbitrary point in their training, so you can't just wait for "all the biases to go away."