Clarifying Model Transparency: Interpretability versus Explainability in Deep Learning with MNIST and IMDB Examples

Interpretability and explainability are distinct concepts, not interchangeable terms: Interpretability refers to a model's inherent capacity for global understanding of how it works overall, while explainability uses post-hoc techniques to explain individual predictions—a critical distinction for responsible AI practitioners to understand when evaluating model transparency.

Local explanations don't guarantee global transparency: The paper demonstrates through MNIST and IMDB examples that being able to explain why a model made a specific prediction (e.g., identifying which pixels led to recognizing a digit as "7") does not mean we understand how the entire model operates, which has important implications for trust in high-stakes applications.

Different XAI approaches serve different purposes: Feature attribution methods and word-level importance analysis can illuminate individual decisions, but organizations need to recognize these are complementary tools addressing different transparency needs rather than complete solutions to the "black box" problem.

High-trust domains require clarity on transparency limitations: The paper emphasizes that the opacity of deep learning models "impedes their widespread acceptance in high-trust domains," suggesting that responsible AI deployment requires honest communication about what current interpretability and explainability techniques can and cannot reveal about model behavior.