These techniques are often referred to as model agnostic methods, because they can be used on any model, regardless of the level of explainability. Model agnostic methods do not access the inner workings of the AI model being explained.
Instead, they derive their explanations post hoc based on the model’s behavior: essentially, they vary inputs to the AI model, and analyze how the changes affect the AI model’s outputs.18 In effect, a model agnostic method uses this testing as data to create a model of the AI model.