Interpretability

Interpretability refers to the degree to which a human can understand the cause of a decision made by a machine learning model. It encompasses techniques that elucidate how input features contribute to model outputs, often employing methods such as saliency maps, feature importance scores, and attention mechanisms. Mathematically, interpretability can be approached through feature attribution methods, which quantify the contribution of each feature to a specific prediction. Techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) are commonly used to provide local interpretability by approximating model behavior around specific instances. The importance of interpretability is underscored in contexts where model decisions have significant ethical implications, necessitating transparency and understanding of the underlying processes.