The paper "Why Should I Trust You? Explaining the Predictions of Any Classifier" by Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin addresses the issue of trust in machine learning models, particularly in the context of understanding and explaining predictions. The authors propose Local Interpretable Model-agnostic Explanations (LIME), a novel technique that explains the predictions of any classifier by learning an interpretable model locally around the prediction. They also introduce SP-LIME, a method to select representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. The paper highlights the importance of trust in both individual predictions and the overall model, emphasizing that trust is crucial for effective use of machine learning models. LIME and SP-LIME are evaluated through simulated and human-subject experiments, demonstrating their effectiveness in explaining predictions, selecting models, and improving untrustworthy classifiers. The experiments show that LIME provides faithful explanations and helps users make informed decisions about trust, even in complex scenarios such as image classification and text classification. The authors also discuss related work and compare their approach with existing methods, emphasizing the unique contributions of LIME and SP-LIME in providing interpretable and faithful explanations.The paper "Why Should I Trust You? Explaining the Predictions of Any Classifier" by Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin addresses the issue of trust in machine learning models, particularly in the context of understanding and explaining predictions. The authors propose Local Interpretable Model-agnostic Explanations (LIME), a novel technique that explains the predictions of any classifier by learning an interpretable model locally around the prediction. They also introduce SP-LIME, a method to select representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. The paper highlights the importance of trust in both individual predictions and the overall model, emphasizing that trust is crucial for effective use of machine learning models. LIME and SP-LIME are evaluated through simulated and human-subject experiments, demonstrating their effectiveness in explaining predictions, selecting models, and improving untrustworthy classifiers. The experiments show that LIME provides faithful explanations and helps users make informed decisions about trust, even in complex scenarios such as image classification and text classification. The authors also discuss related work and compare their approach with existing methods, emphasizing the unique contributions of LIME and SP-LIME in providing interpretable and faithful explanations.