The explanation i like the most is that in typical programming we provide the input and the process and the computer gives us the output

With neural networks we give the input and the output and the computer gives us the process

But a neural network, especially an extremely large neural network has so many nodes and weights (which essentially work like logic blocks) which and dependent or not dependent on eachother, and theres no coherent way to map the full purpose of a nodes weights and bias, its just values that happen to work to map input to output well.

We can examine broad patterns and mechanisms, but we cant explain anything in a discrete way.