Model Agnostic Explanation

Model-agnostic explanation (MAE) methods aim to understand the decision-making processes of complex machine learning models without requiring access to their internal workings. Current research focuses on improving the efficiency and accuracy of existing techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), exploring alternative approaches based on kernel methods, Bayesian inference, and counterfactual generation, and addressing challenges like instability and robustness to out-of-distribution data. MAE's significance lies in its ability to enhance trust and transparency in AI systems across diverse applications, from medical diagnosis to cybersecurity, by providing human-understandable explanations for model predictions.