If it's interpretable it's pretty much useless.

Some days ago I was interviewing a candidate for a data-related position: after a couple of technical questions I asked him what algorithm he would have used to have a reliable starting point for a random classification problem. I was just curious to understand how used he was in doing some data science and if he knew some state-of-the-art algorithms and techniques. He told me that he would have gone with a simple decision tree because it's somehow easy to explain and interpret. That answer surprised me a little: I mean, why a decision tree in 2019 when you can get way better and, above all, more robust results using more advanced algorithms? As always happens, once you notice something you see it everywhere, and from that day I keep seeing and reading here and there blog posts about interpretability, explicability and how all of these concepts are connected to machine learning and trust.