You can monkey around with it here: http://bionlp-www.utu.fi/wv_demo/ (choose the English Google News model, but this may not be exactly the same set/model as the original report).

Man is to Woman as Doctor is to ___ gives 1) gynecologist 2) nurse 3) doctors 4) physician 5) pediatrician

Woman is to Man as Doctor is to ___ gives: 1) physician 2) doctors 3) surgeon 4) dentist 5) cardiologist

These are just generally near "Doctor" though: the ten nearest terms are physician, doctors, gynecologist, surgeon, dentist, pediatrician, pharmacist, neurologist, cardiologist, and nurse.

Some gender differences may persist (nurse is #2 for `woman`, but #68 for `man`, but it's also near `woman` generally and you could imagine it gets a bit of a boost from the verb ("to feed a baby") being attached exclusively to women too.

Anyway, my point is not that there's no bias (there certainly can be--seed GTP-3 with a prompt about Muslims) but that one should be wary of thinking they know what the model is doing.

It highly depends on the corpus and you can check for yourself.

I'm being lazy, but the results for Gutenberg books you can check online at http://labs.statsbiblioteket.dk/dsc/

- man is to woman as doctor is to reprovingly (nurse is the first noun, on position 4) - woman is to man as doctor is to snodgrass (after a couple nonsense/rare words)

The most important thing that teaches us is that big corpora (bigger than PG) are essential for this method.