Interpretation Method

Interpretation methods aim to make the decision-making processes of complex machine learning models, such as deep learning models (including transformers, CNNs, and RNNs), more transparent and understandable. Current research focuses on developing and comparing various interpretation techniques, including those that decompose model behavior into contributions from individual features or internal components, and those that integrate multiple methods for more robust and reliable explanations. This work is crucial for building trust in AI systems, improving model development through enhanced understanding of their strengths and weaknesses, and enabling responsible deployment in high-stakes applications like finance and healthcare.