In the age of ML this is almost impossible to police and very difficult to implement even if you want to. You blind your model to gender but it learns to infer

In the age of ML this is almost impossible to police and very difficult to implement even if you want to. You blind your model to gender but it learns to infer it from names. You blind it to names, it learns to infer it from other inputs. By the time you've blinded your model to anything that gives away gender, it no longer has utility. If you instead intentionally alter the model to still have utility but impact both genders equally, it's no longer procedurally fair and does in fact discriminate. This is the trilemma mentioned above. You must choose which of those factors you care about and which one you'll sacrifice. If you're unable to make that choice and morally stand behind it, then probably you shouldn't be building said model/system in the first place. This is one of the major topics of the ML ethics conversation.