Local Interpretability

Local interpretability in machine learning focuses on explaining individual predictions made by complex models, such as deep neural networks and time series transformers, to enhance trust and facilitate understanding. Current research emphasizes developing methods that provide faithful and robust explanations, often using techniques like SHAP values, LIME, and rule-based ensembles, and exploring how to effectively evaluate the quality of these explanations. This area is crucial for deploying machine learning models in high-stakes domains like healthcare and finance, where understanding model decisions is paramount for responsible use and building trust.