Machine learning algorithms can be black boxes--inputs go in, outputs come out, and what happens in the middle is anybody's guess.  But understanding how a model arrives at an answer is critical for interpreting the model, and for knowing if it's doing something reasonable (one could even say... trustworthy).  We'll talk about a new algorithm called LIME that seeks to make any model more understandable and interpretable.

Relevant Links:
http://arxiv.org/abs/1602.04938
https://github.com/marcotcr/lime/tree/master/lime

Model Interpretation (and Trust Issues)

Linear Digressions