Interpretability, also called model explainability or explainable AI (XAI), is the ability to understand a model’s decisions and predictions in human-understandable terms. Monitoring can show whether a deployed model is performing well; interpretability helps answer why it made a particular decision. section 11.3
Use explanations to debug and validate behavior: inspect whether decisions rely on relevant features rather than spurious correlations, identify possible biases, and understand a model’s strengths and weaknesses. In production, this supports stakeholder trust and accountability as well as regulatory requirements. Credit-risk decisions are a concrete case where explaining an adverse lending decision matters. section 11.3
Choose an approach that fits the model and the need for an explanation:
- During evaluation, inspect and visualize feature importances, decision boundaries, or activation layers; LIME and SHAP are other techniques for investigating behavior and limitations. section 2.1.4
- For object detection, Eigen-CAM can provide insight into predictions; recommender systems can use model-based explanations. section 11.4
- Decide early whether the benefit warrants the implementation effort. Interpretability can be complex to add, even when an organization requires it during evaluation. section 2.1.4
Treat interpretability as a complement to operational monitoring and data-drift detection, not a replacement for them. Metrics and drift checks help flag a service or data problem; explanations help investigate model decisions once behavior needs closer scrutiny. section 11.0 section 11.3
