XGBoost models are often interpreted with SHAP (Shapley Additive eXplanations): Each of e.g. 1000 randomly selected predictions is fairly decomposed into contributions of the features using the extremely fast TreeSHAP algorithm, providing a rich interpretation of the model as a whole. TreeSHAP was introduced in the Nature publication by Lundberg and Lee (2020).
Can we do the same for non-tree-based models like a complex GLM or a neural network? Yes, but we have to resort to slower model-agnostic SHAP algorithms:
Read on for examples of those algorithms and an example of interpretation and analysis.