2020
Interpretable Companions for Black-Box Models
AISTATS 2020poster
We present an interpretable companion model for any pre-trained black-box classifiers. The idea is that for any input, a user can decide to either receive a prediction from the black-box model, with high accuracy but no explanations, or employ a \emph{companion rule} to obtain an interpretable predi…