Earlier quoted context omitted.
If they were using any sort of neural networks approach with stochastic gradient descent, the network would have to spend some "gradient juice" to cut a divot that recognizes and penalizes women's colleges and the like. It wouldn't do this just because there were fewer women in the batches, rather it would just not assign any weight to those factors. Unless they presented lots of unqualified resumes of people not in…
> It wouldn't do this just because there were fewer women in the batches, rather it would just not assign any weight to those factors. That's exactly the issue we are talking about here. Woman's colleges would have less training data so they would get updated less. For many classes of models (such as neural networks with weight decay or common initialization schemes) this would encourage the model to be more "neutral…
A class imbalance doesn't change that: if there's no gradient to follow, then the class in question will be strictly ignored unless you've somehow forced the model to pay attention to it in the architecture (which is possible, but would take some specific effort).
What I'm suggesting is that it's likely that they did (perhaps accidentally?) let a loss gradient between the classes slip into their data, because they had a whole bunch of female resumes that were from people not in tech. That would explain the difference, whereas at least with NNs, simply having imbalanced classes would not.