"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Authors: Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
Year: 2016
Summary: This paper introduces LIME (Local Interpretable Model-agnostic Explanations). The key idea is to explain the prediction of any complex, black-box classifier by learning a simpler, interpretable model (e.g., a linear model) in the local neighborhood of the prediction. It provides intuitive, human-understandable explanations for individual predictions, answering the question of why a specific decision was made.
Link: https://arxiv.org/abs/1602.04938