It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction.

I will discuss recent work on interpretable predictive modeling with decision lists. I will describe several approaches, including:

- an algorithm where not only the predictions, but the whole algorithm itself is interpretable to a human

- two algorithms based on Bayesian analysis

- an algorithm based on mixed-integer linear optimization

- an algorithm based on simulated annealing

Collaborators are: Ben Letham, Allison Chang, Tyler McCormick, David Madigan, Shawn Qian, and Dimitris Bertsimas