Model Interpretation (and Trust Issues) - a podcast by Ben Jaffe and Katie Malone

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

Further episodes of Linear Digressions

Further podcasts by Ben Jaffe and Katie Malone

Website of Ben Jaffe and Katie Malone