What if people named David got hired 10/100 times in the past but people named Denise only got hired 6/100 times? Hiring practices as expressed in the data get

What if people named David got hired 10/100 times in the past but people named Denise only got hired 6/100 times? Hiring practices as expressed in the data get picked up by the machine and applied accordingly. As such, David is predicted to be a better hire than Denise. This is not about "David" vs. "Denise", but how the machine learning process will aggregate and classify names. David and David-like names will come out on top while obscure names it has no idea how to deal with (0/0 historically) will probably be given no weighting at all. Sorry "Daud!" Our algorithm says David is better.