Feature Attribution Method

Feature attribution methods aim to explain the predictions of complex machine learning models by assigning importance scores to input features. Current research focuses on improving the robustness and reliability of these methods, particularly for deep learning models (including transformers and convolutional neural networks), addressing issues like inconsistency across different techniques and sensitivity to perturbations. This work is crucial for building trust in AI systems across various applications, from industrial process optimization to healthcare diagnostics, by enhancing transparency and facilitating better understanding of model behavior.