This is a pointless article. Obviously everyone would prefer fully interpretable models if it was possible to get comparable performance with the leading black box/deep learning approaches. No one chooses to make black box, non-interpretable models, it's simply the fact that the models that give the best performance tend to be black box. There is a lot of work happening on trying to interpret deep learning models, because no one prefers non-interpretable models and being unable to pin point and explain why some prediction was wrong.
The article explains that their fully interpretable model actually got within 1% accuracy of the best black box model. Which was within cross validation error. That was at a data science competition whose explicit goal was to encourage people to explain black box models. They also give other examples of cases where people have used black box models unnecessarily.
> The article explains that their fully interpretable model actually got within 1% accuracy of the best black box model. Which was within cross validation error. That was at a data science competition whose explicit goal was to encourage people to explain black box models.
That's awesome, but this was a single model for a single application being measured against presumably by a limited test criteria/benchmarks. The point still stands that the recent narrow AI "renaissance" is largely due to deep learning, which is inherently black box. There is a lot of work going on in making deep learning more interpretable precisely because it's so prominent today and because its lack of interpretability is a huge con.
Machine vision for example has come a long way due to deep learning. A lot of autonomous car companies are relying on it for perception. All of these companies hate the fact that there is no way to tell when the classifier will fail or why it'd fail. When using deep learning in finance setting, its lack of interpretability is a huge downside.
Despite this deep learning is being used because it provides an appreciable improvement in performance over the alternatives. Certainly there could be alternatives to deep learning that may perform just as well or even better. But finding those alternatives for the vast number of applications deep learning is being used for today is much easier said than done.
