The paper "Interpretable Machine Learning: Definitions, Methods, and Applications" by W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asi, and Bin Yu addresses the growing importance of interpretability in machine learning (ML). The authors define interpretability in the context of ML and introduce the Predictive, Descriptive, Relevant (PDR) framework to evaluate and discuss interpretations. The PDR framework consists of three main criteria: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged by a human audience. The paper categorizes existing interpretation methods into model-based and post-hoc categories, including sub-groups such as sparsity, modularity, and simulatability. Real-world examples are provided to demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations. The authors also discuss limitations of existing methods and directions for future work, aiming to provide a common vocabulary for researchers and practitioners to discuss and choose from the full range of interpretation methods. The paper highlights the role of human audiences in discussions of interpretability and emphasizes the importance of balancing predictive accuracy and descriptive accuracy in the context of interpretability.The paper "Interpretable Machine Learning: Definitions, Methods, and Applications" by W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asi, and Bin Yu addresses the growing importance of interpretability in machine learning (ML). The authors define interpretability in the context of ML and introduce the Predictive, Descriptive, Relevant (PDR) framework to evaluate and discuss interpretations. The PDR framework consists of three main criteria: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged by a human audience. The paper categorizes existing interpretation methods into model-based and post-hoc categories, including sub-groups such as sparsity, modularity, and simulatability. Real-world examples are provided to demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations. The authors also discuss limitations of existing methods and directions for future work, aiming to provide a common vocabulary for researchers and practitioners to discuss and choose from the full range of interpretation methods. The paper highlights the role of human audiences in discussions of interpretability and emphasizes the importance of balancing predictive accuracy and descriptive accuracy in the context of interpretability.