The gender bias exists due to the assumptions it makes on the sample data. I'm sure if you searched "<gender> <profession>" it would get you what you want.

If 95% of publicly available images of a profession are of one gender, should the tool be deliberately modified so that it's X% instead?

I totally agree that models that purport to represent the world can be hugely biased and reconfirming of negative biases, but how would avoiding representing those biases be achieved? Strict gender ratios in training data?