Ya, I agree with you that it's definitely tricky business. Including features like zipcode in something like a loan payment predictor is a good example of a sloppy proxy for the underlying and relevant features. Especially when certain data becomes hard to resist using, when something like zipcode of residence might have a 90% overlap with income range and be extremely trivial to acquire, whereas getting detailed and accurate income information from an applicant might be much harder. Tricky stuff.
Machine learning doesn't necessarily do what you want it to do; it probably will "cheat" and use an easier proxy variable, unless you watch out for it.
There is the urban legend about the machine learning program that learned to "recognize" tanks based on time of day in the training set. It probably never happened [1] but it gets the point across.
[1] https://www.gwern.net/Tanks