Assessing the Local Interpretability of Machine Learning Models

Understand

The increasing adoption of machine learning tools has led to calls for accountability via model interpretability.

• But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learning literature: simulatability (a user's ability to run a model on a given input) and "what if" local explainability (a user's ability to correctly determine a model's prediction under local changes to the input, given knowledge of the model's original prediction).
• Through a user study with 1,000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable.
• To track the relative interpretability of models, we employ a simple metric, the runtime operation count on the simulatability task.