Which statement best distinguishes interpretability from explainability in AI?

Which statement best distinguishes interpretability from explainability in AI?

• A. Interpretability refers to how easily a model's behavior can be understood; explainability refers to post-hoc or model-agnostic explanations of outputs.
• B. Explainability refers to understanding the model's internals; interpretability refers to post-hoc explanations.
• C. They are synonyms.
• D. Interpretability is about post-hoc explanations, while explainability is about understanding the internal model structure.

Answer: (A)

Interpretability is about how easily you can understand a model’s behavior from its own structure and parameters. If a model is designed so you can trace how inputs map to outputs, or you can inspect the weights, splits, or rules directly, it’s considered interpretable. Explainability, on the other hand, focuses on providing understandable reasons for a model’s predictions, often after the fact and sometimes without exposing the internals. That means generating explanations that describe why a particular output was produced, which can be done with model-agnostic or post-hoc methods even for complex, opaque models. This distinction matters because a model can be hard to interpret internally but still offer useful explanations for its decisions, and conversely, a simple model can be easy to understand without needing extra explanations. In practice, interpretable models (like linear models or simple trees) give transparent behavior by design, while explainability methods (like SHAP or LIME) help communicate the reasoning behind specific predictions for more complex models.

Interpretability is about how easily you can understand a model’s behavior from its own structure and parameters. If a model is designed so you can trace how inputs map to outputs, or you can inspect the weights, splits, or rules directly, it’s considered interpretable.

Explainability, on the other hand, focuses on providing understandable reasons for a model’s predictions, often after the fact and sometimes without exposing the internals. That means generating explanations that describe why a particular output was produced, which can be done with model-agnostic or post-hoc methods even for complex, opaque models.

This distinction matters because a model can be hard to interpret internally but still offer useful explanations for its decisions, and conversely, a simple model can be easy to understand without needing extra explanations. In practice, interpretable models (like linear models or simple trees) give transparent behavior by design, while explainability methods (like SHAP or LIME) help communicate the reasoning behind specific predictions for more complex models.