A machine learning model consists of taking all your inputs, applying several pages worth adding them together and multiplying by arbitrary constants, and getting a number out. Machine learning itself consists of methods for rejecting piles of arbitrary constants until you get one that outputs results similar to your training data. Nobody knows what's going on inside, because it's a pile of very arbitrary math, chosen automatically because it mostly does the right thing.

(There are other branches of machine learning that are more insightful, understandable, and explicable. But they're also more limited, and not the stuff that the last several years of ML hype has been about.)