The paper "Explaining Explanations: An Overview of Interpretability of Machine Learning" by Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal from MIT's Computer Science and Artificial Intelligence Laboratory provides a comprehensive overview of the field of explainable artificial intelligence (XAI). The authors highlight the importance of creating explanations for complex machine and algorithmic decisions to ensure algorithmic fairness, identify biases, and enhance user trust. They discuss the limitations of current approaches, particularly for deep neural networks, and propose a taxonomy to classify existing literature and future research directions. The paper is structured into several sections, including an introduction that emphasizes the need for explainability in autonomous systems, a background section that defines key concepts like "explanation," "interpretability," and "explainability," and a review of various methods for explaining deep network processing, representations, and generating explanations. The authors also discuss related work in different domains such as human-computer interaction (HCI) and explainable planning. A key contribution of the paper is a taxonomy that categorizes approaches into three main types: explanations of data processing, explanations of internal representations, and explanation-producing systems. The taxonomy aims to promote research and evaluation across these categories, addressing the lack of standardized evaluation metrics. The paper concludes with a discussion on the challenges and future directions in XAI, emphasizing the need for diverse metrics and collaborative efforts to advance the field. The authors advocate for the development of methods that provide behavioral extrapolation, build trust, and offer usable insights into deep network operations.The paper "Explaining Explanations: An Overview of Interpretability of Machine Learning" by Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal from MIT's Computer Science and Artificial Intelligence Laboratory provides a comprehensive overview of the field of explainable artificial intelligence (XAI). The authors highlight the importance of creating explanations for complex machine and algorithmic decisions to ensure algorithmic fairness, identify biases, and enhance user trust. They discuss the limitations of current approaches, particularly for deep neural networks, and propose a taxonomy to classify existing literature and future research directions. The paper is structured into several sections, including an introduction that emphasizes the need for explainability in autonomous systems, a background section that defines key concepts like "explanation," "interpretability," and "explainability," and a review of various methods for explaining deep network processing, representations, and generating explanations. The authors also discuss related work in different domains such as human-computer interaction (HCI) and explainable planning. A key contribution of the paper is a taxonomy that categorizes approaches into three main types: explanations of data processing, explanations of internal representations, and explanation-producing systems. The taxonomy aims to promote research and evaluation across these categories, addressing the lack of standardized evaluation metrics. The paper concludes with a discussion on the challenges and future directions in XAI, emphasizing the need for diverse metrics and collaborative efforts to advance the field. The authors advocate for the development of methods that provide behavioral extrapolation, build trust, and offer usable insights into deep network operations.