Model Interpretability

Model interpretability aims to make the decision-making processes of complex machine learning models transparent and understandable. Current research focuses on developing both inherently interpretable models, such as generalized additive models and rule-based systems, and post-hoc methods that explain the predictions of black-box models, often using techniques like SHAP values, Grad-CAM, and various attention mechanisms applied to architectures like transformers and neural networks. This field is crucial for building trust in AI systems, particularly in high-stakes domains like healthcare and finance, and for facilitating the responsible development and deployment of machine learning technologies.