Two recent papers on model interpretability

Machine learning often involves a trade-off between accuracy and interpretability. Models that are easy to understand perform poorly, while models that perform well tend to be hard to understand. This can be a show-stopper in many contexts. It can be risky or legally dubious to deploy a model you don't understand in a commercial environment. Interpreting a model is often the whole point of using machine learning in science.