Random forests are very difficult to interpret especially once you start having lots of trees.
>While it's hard to interpret a random forest, it's still much more interpretable than a neural network
Depends on the domain. The first layer or two of a neural network can be fairly interpretable on image or spectrogram (sound) data, for example.
While it's hard to interpret a random forest, it's still much more interpretable than a neural network. Maybe this model is also easier to interpret by humans.