A Formal Framework to Characterize Interpretability of Procedures

Abstract

We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability.

presented at 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), Sydney, NSW, Australia

References in corpus (2)

• Towards A Rigorous Science of Interpretable Machine Learning
• Interpretable Two-level Boolean Rule Learning for Classification

Cited by in corpus (5)

• TED: Teaching AI to Explain its Decisions
• Teaching Meaningful Explanations
• Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
• Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
• Understanding Interpretability by generalized distillation in Supervised Classification