Explanation Method

Explanation methods aim to make the decision-making processes of complex machine learning models more transparent and understandable. Current research focuses on improving the faithfulness, stability, and user-friendliness of explanations, exploring various approaches including SHAP, LIME, gradient-based methods, and the use of large language models to generate more natural and engaging explanations. This work is crucial for building trust in AI systems, particularly in high-stakes applications like healthcare and finance, and for facilitating better model debugging and design. A key challenge remains developing robust evaluation metrics that capture the multifaceted nature of explanation quality and its impact on human understanding.