> there are actually a lot of applications where people do not try to construct an interpretable model, because they might believe that for a complex data set,

> there are actually a lot of applications where people do not try to construct an interpretable model, because they might believe that for a complex data set, an interpretable model could not possibly be as accurate as a black box. Or perhaps they want to preserve the model as proprietary. I think they missed an important reason here. It's why I decided to study AI and Machine Learning: The ability to "just throw some data at it and see if it figures it out" is just so exciting, mysterious and intriguing. I remember actually being a bit disappointed when I learned how (classic, 3 layer, early 2000s) neural networks worked. That it was just something super simple with derivatives, and resulted in something that seemed a lot like a more complex form of statistical regression. Kind of took the magic out of it for me, a little (didn't stop me of course, it was and still is an exciting field of research). I know it's not a good reason, but surely I'm not the only one who thinks it's extremely cool that one is even able to build a black box that does useful things with what you feed it, but you don't know how it works, and yet you built it. It's just something mystifying, and I believe there is also a tinge of fear for that disappointment, to figure it out and find out it's not really doing as clever things as you hoped it might.