CrystalCandle: A User-Facing Model Explainer for Narrative Explanations

TL;DR

CrystalCandle is a user-friendly platform that generates narrative explanations for machine learning models, improving interpretability, user trust, and adoption rates through customizable insights and end-to-end integration.

Contribution

It introduces a comprehensive platform with four components that produce intuitive, narrative-based explanations for model predictions, enhancing interpretability and user engagement.

Findings

Increased model adoption rates with CrystalCandle's explanations

Higher downstream revenue metrics observed

Positive user feedback on interpretability improvements

Abstract

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.