Interpretability in machine learning refers to the extent to which a human can understand the cause of a decision made by a model. Highly complex models like deep neural networks are often considered “black boxes,” and there’s a demand in critical applications (medicine, finance, law) to have interpretable models or explanations. Interpretability techniques include feature importance measures (e.g., SHAP values, LIME) that tell which features influenced a prediction, model simplification (like approximating a complex model with a decision tree or rule set), visualization (like saliency maps for CNNs highlighting what part of an image is pertinent to classification), and inherently interpretable models (decision trees, linear models, rule-based learners). Interpretability is related to explainability, and it helps in debugging models, ensuring fairness (checking if model relies on protected attributes), and gaining user trust. There’s often a trade-off between model accuracy and interpretability, though the field of eXplainable AI (XAI) is working to mitigate that.