Interpretability

Interpretability refers to the degree to which a human can understand the internal mechanisms of a model — why specific inputs produce specific outputs. It is a structural property of the model itself: simpler models like linear regression or shallow decision trees are inherently more interpretable than deep neural networks or large language models, whose billions of parameters resist human inspection. On AIF-C01, interpretability falls under responsible AI and governance concerns in Domain 4. The key exam distinction is that interpretability concerns the model’s inner workings, while explainability describes model behavior in human-understandable terms after the fact, often via post-hoc tools. Amazon SageMaker Clarify provides feature-attribution explanations that address explainability without making a black-box model inherently interpretable. High interpretability is often traded against model performance — watch for questions framing this as a transparency-versus-accuracy tension.