If you know that men and women differ in a distributional sense with respect to some trait, that gives you a prior to work off-of when you meet a new individual. This is rational from bayes theorem, so simply saying "you should treat everyone as an individual" is not nuanced enough.
However, as you acquire more information about a particular individual (such as passing a difficult google interview, or knowing that they've succeeded in a reputable CS curriculum), this should quickly "swamp" the prior, causing it to contribute very little to the final inference.
The problem is the humans are not great at adjusting like this: we're not perfect at applying bayes theorem in our heads. We tend to overstate the influence of various priors when there are stronger signals at hand. Nevertheless, incorporating prior distributional information is NOT irrational, but generally overdone.
Therefore, it seems like the approach of some is to shout down information that would suggest biological distributional differences, to try guarantee that people don't overuse prior information.