LIME Explanation

LIME (Local Interpretable Model-agnostic Explanations) is a popular technique for explaining the predictions of complex machine learning models, aiming to provide understandable reasons for model outputs. Current research focuses on improving LIME's accuracy and robustness, particularly addressing issues like artifacts in image explanations and the impact of model size on explanation plausibility, often employing post-processing heuristics or stratified sampling to enhance results. These efforts are significant because reliable explanations are crucial for building trust in AI systems across diverse applications, from medical diagnosis to natural language processing, and for improving model performance through feedback mechanisms.