  Predictive machine learning models often lack interpretability, resulting in
low trust from model end users despite having high predictive performance.
While many model interpretation approaches return top important features to
help interpret model predictions, these top features may not be well-organized
or intuitive to end users, which limits model adoption rates. In this paper, we
propose CrystalCandle, a user-facing model explainer that creates
user-digestible interpretations and insights reflecting the rationale behind
model predictions. CrystalCandle builds an end-to-end pipeline from machine
learning platforms to end user platforms, and provides users with an interface
for implementing model interpretation approaches and for customizing narrative
insights. CrystalCandle is a platform consisting of four components: Model
Importer, Model Interpreter, Narrative Generator, and Narrative Exporter. We
describe these components, and then demonstrate the effectiveness of
CrystalCandle through use cases at LinkedIn. Quantitative performance analyses
indicate that CrystalCandle's narrative insights lead to lifts in adoption
rates of predictive model recommendations, as well as to increases in
downstream key metrics such as revenue when compared to previous approaches,
while qualitative analyses indicate positive feedback from end users.
