Definitions, methods, and applications in interpretable machine learning

Abstract

Significance The recent surge in interpretability research has led to confusion on numerous fronts. In particular, it is unclear what it means to be interpretable and how to select, evaluate, or even discuss methods for producing interpretations of machine-learning models. We aim to clarify these concerns by defining interpretable machine learning and constructing a unifying framework for existing methods which highlights the underappreciated role played by human audiences. Within this framework, methods are organized into 2 classes: model based and post hoc. To provide guidance in selecting and evaluating interpretation methods, we introduce 3 desiderata: predictive accuracy, descriptive accuracy, and relevancy. Using our framework, we review existing work, grounded in real-world studies which exemplify our desiderata, and suggest directions for future work.