What are post-hoc explanation methods in Explainable AI?

Post-hoc explanation methods in Explainable AI (XAI) are techniques used to interpret the decisions of machine learning models after they have made a prediction. Unlike inherently interpretable models (e.g., decision trees), post-hoc methods work with “black-box” models (e.g., neural networks or ensemble methods) to provide insights into how specific inputs lead to outputs. These methods do not alter the model itself but instead analyze its behavior by probing inputs and outputs, or by approximating its decision logic. Their primary goal is to help developers and stakeholders understand, trust, and debug complex models by making their predictions more transparent.

Common post-hoc techniques include feature importance analysis, surrogate models, and example-based explanations. For instance, LIME (Local Interpretable Model-agnostic Explanations) creates a simplified, interpretable model (like linear regression) that approximates the black-box model’s behavior around a specific prediction. This helps identify which input features (e.g., pixel values in an image or words in a text) were most influential for that prediction. Another method, SHAP (SHapley Additive exPlanations), uses game theory to assign a contribution score to each input feature, ensuring consistency across explanations. Visualization tools like saliency maps (highlighting important regions in an image) or attention maps (showing which words a language model focused on) are also widely used. These methods are model-agnostic, meaning they can be applied to any architecture without requiring internal access.

However, post-hoc explanations have limitations. They often provide local (per-instance) insights rather than global explanations of the entire model, which can lead to incomplete understanding. For example, a saliency map might highlight edges in an image as important for a classification task, but it won’t explain how the model generalizes across datasets. Additionally, some methods rely on approximations that may not fully capture the model’s true behavior, especially in cases of complex interactions between features. Developers should validate explanations against domain knowledge and use multiple techniques to cross-check results. While post-hoc methods are practical for auditing models, they should complement—not replace—efforts to build inherently interpretable systems where possible.