Local Interpretable Model Agnostic Explanation

Local Interpretable Model-Agnostic Explanations (LIME) aim to make the predictions of complex "black box" machine learning models more understandable. Current research focuses on improving LIME's stability, fidelity (how well explanations reflect the model's behavior), and applicability to various data types (images, text, time series) and model architectures (including deep learning models like transformers and convolutional neural networks). This work is significant because it addresses the critical need for transparency and trust in AI systems, particularly in high-stakes domains like healthcare and finance, by providing more reliable and insightful explanations of model decisions.