The Center for Data Science (CDS) at NYU has a weekly lunch seminar series. Each Monday, one speaker gives an (informal) presentation on any topic she/he wants to talk about, or at least so I thought. Anyways, I thought it would be a good chance to discuss with people (students, research fellows at CDS as well as faculty members from various departments all over NYU) what the interpretability of machine learning models means. I prepared a set of slides based on an excellent article <Statistical Modeling: The Two Cultures> by Leo Breiman.
Instead of trying to write what I've talked about here, I'll put a link to my slides:
Oh, right, there were a lot of wonderful discussions on this topic during and after the talk.