Understanding the difference between interpretability and explainability

Something you’ve probably noticed when reading the first few pages of this book is that the verbs interpret and explain, as well as the nouns interpretation and explanation, have been used interchangeably. This is not surprising, considering that to interpret is to explain the meaning of something. Despite that, the related terms interpretability and explainability should not be used interchangeably, even though they are often mistaken for synonyms. Most practitioners don’t make any distinction and many academics reverse the definitions provided in this book.

What is interpretability?

Interpretability is the extent to which humans, including non-subject-matter experts, can understand the cause and effect, and input and output, of a machine learning model. To say a model has a high level of interpretability means you can describe in a human-interpretable way its inference. In other words...