Example Concept: One effective strategy is to use model-agnostic interpretability methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations). These techniques provide insights into individual predictions by approximating the model locally around the prediction point, highlighting which features are most influential. Additionally, using simpler models like decision trees or linear models can inherently improve interpretability, though they might require careful tuning to maintain performance.