The paper "The Mythos of Model Interpretability" by Zachary C. Lipton explores the concept of model interpretability in supervised learning. It argues that while models are often claimed to be interpretable, the term lacks a universally agreed definition. The paper examines the diverse motivations for interpretability, such as trust, causality, transferability, informativeness, and fairness, and highlights the ambiguity in what constitutes an interpretable model. It distinguishes between transparency, which refers to understanding how a model works, and post-hoc explanations, which provide insights after the fact. The paper challenges the common belief that linear models are more interpretable than deep neural networks, noting that while linear models may be simpler, deep models can offer richer, more interpretable representations. It also warns against the potential for post-hoc interpretations to be misleading, especially when they are tailored to subjective demands. The paper calls for a more rigorous definition of interpretability and emphasizes the need for critical analysis in the field to address real-world challenges. It concludes that interpretability is not a single concept but a set of distinct ideas, and that future research should focus on developing more robust and meaningful interpretations.The paper "The Mythos of Model Interpretability" by Zachary C. Lipton explores the concept of model interpretability in supervised learning. It argues that while models are often claimed to be interpretable, the term lacks a universally agreed definition. The paper examines the diverse motivations for interpretability, such as trust, causality, transferability, informativeness, and fairness, and highlights the ambiguity in what constitutes an interpretable model. It distinguishes between transparency, which refers to understanding how a model works, and post-hoc explanations, which provide insights after the fact. The paper challenges the common belief that linear models are more interpretable than deep neural networks, noting that while linear models may be simpler, deep models can offer richer, more interpretable representations. It also warns against the potential for post-hoc interpretations to be misleading, especially when they are tailored to subjective demands. The paper calls for a more rigorous definition of interpretability and emphasizes the need for critical analysis in the field to address real-world challenges. It concludes that interpretability is not a single concept but a set of distinct ideas, and that future research should focus on developing more robust and meaningful interpretations.