This paper introduces LIME, a novel explanation technique that provides interpretable and faithful explanations for any classifier's predictions. LIME works by learning a locally interpretable model around the prediction. It also proposes SP-LIME, a method that selects representative instances with explanations to address the "trusting the model" problem through submodular optimization. The authors demonstrate the flexibility of these methods by explaining different models for text and image classification. They show the utility of explanations through experiments with simulated and human subjects, highlighting how explanations help users decide whether to trust a prediction, choose between models, improve an untrustworthy classifier, and identify why a classifier should not be trusted. The paper discusses the importance of explanations in building trust in machine learning models. It argues that explanations are crucial for understanding how models make decisions, especially when the model is used for decision-making in critical areas like medical diagnosis or terrorism detection. The authors also note that explanations can help users evaluate models before deployment, as real-world data often differs from validation data, and evaluation metrics may not reflect the model's true performance. The paper outlines desired characteristics for explanation methods, including interpretability, local fidelity, and the ability to explain any model. It presents LIME as a model-agnostic approach that approximates the original model locally with an interpretable model. The method works by sampling instances around the prediction, weighting them by proximity, and learning a linear model to approximate the original model's behavior. The paper also discusses the importance of selecting representative instances for model explanation, using submodular optimization to ensure diversity and non-redundancy. The authors show that explanations can help users understand model behavior, identify issues like data leakage or dataset shift, and improve model performance through feature engineering. The paper presents experiments showing that LIME provides faithful explanations and helps users assess trust in predictions and models. It also demonstrates that explanations can help users identify and correct issues in models, such as data leakage or spurious correlations. The authors conclude that explanations are essential for building trust in machine learning models and that LIME is a valuable tool for achieving this goal.This paper introduces LIME, a novel explanation technique that provides interpretable and faithful explanations for any classifier's predictions. LIME works by learning a locally interpretable model around the prediction. It also proposes SP-LIME, a method that selects representative instances with explanations to address the "trusting the model" problem through submodular optimization. The authors demonstrate the flexibility of these methods by explaining different models for text and image classification. They show the utility of explanations through experiments with simulated and human subjects, highlighting how explanations help users decide whether to trust a prediction, choose between models, improve an untrustworthy classifier, and identify why a classifier should not be trusted. The paper discusses the importance of explanations in building trust in machine learning models. It argues that explanations are crucial for understanding how models make decisions, especially when the model is used for decision-making in critical areas like medical diagnosis or terrorism detection. The authors also note that explanations can help users evaluate models before deployment, as real-world data often differs from validation data, and evaluation metrics may not reflect the model's true performance. The paper outlines desired characteristics for explanation methods, including interpretability, local fidelity, and the ability to explain any model. It presents LIME as a model-agnostic approach that approximates the original model locally with an interpretable model. The method works by sampling instances around the prediction, weighting them by proximity, and learning a linear model to approximate the original model's behavior. The paper also discusses the importance of selecting representative instances for model explanation, using submodular optimization to ensure diversity and non-redundancy. The authors show that explanations can help users understand model behavior, identify issues like data leakage or dataset shift, and improve model performance through feature engineering. The paper presents experiments showing that LIME provides faithful explanations and helps users assess trust in predictions and models. It also demonstrates that explanations can help users identify and correct issues in models, such as data leakage or spurious correlations. The authors conclude that explanations are essential for building trust in machine learning models and that LIME is a valuable tool for achieving this goal.