A Formal Framework to Characterize Interpretability of Procedures

Understand

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.