We introduce a novel model-agnostic system that explains the
behavior of complex models with high-precision rules called
anchors, representing local, “sufficient” conditions for predictions. We propose an algorithm to efficiently compute these
explanations for any black-box model with high-probability
guarantees. We demonstrate the flexibility of anchors by explaining a myriad of different models for different domains
and tasks. In a user study, we show that anchors enable users
to predict how a model would behave on unseen instances
with less effort and higher precision, as compared to existing
linear explanations or no explanations.
https://homes.cs.washington.edu/~marcotcr/aaai18.pdf
Abstract
We introduce a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, “sufficient” conditions for predictions. We propose an algorithm to efficiently compute these explanations for any black-box model with high-probability guarantees. We demonstrate the flexibility of anchors by explaining a myriad of different models for different domains and tasks. In a user study, we show that anchors enable users to predict how a model would behave on unseen instances with less effort and higher precision, as compared to existing linear explanations or no explanations.