The paper "Gradient based Feature Attribution in Explainable AI: A Technical Review" by Yongjie Wang, Tong Zhang, Xu Guo, and Zhiqi Shen provides a comprehensive overview of gradient-based feature attribution methods in the context of explainable AI (XAI). The authors aim to address the growing need for understanding the internal mechanisms of black-box AI models, particularly in critical applications like healthcare and autonomous driving. They categorize XAI into *ante-hoc explanation* and *post-hoc explanation*, with the latter further divided into *model explanation*, *outcome explanation*, and *model inspection*. The paper focuses on gradient-based explanations, which are particularly useful for neural network models due to their seamless integration and ability to satisfy axiomatic properties. The authors propose a novel taxonomy that categorizes gradient-based feature attribution into four groups: *vanilla gradients based explanation*, *integrated gradients based explanation*, *bias gradients based explanation*, and *post-processing for denoising*. Each group is detailed with specific techniques and their evolution over time. Key contributions of the paper include: 1. A novel taxonomy for gradient-based feature attribution. 2. Detailed explanations of various techniques, including their motivation and algorithmic details. 3. Evaluation metrics for measuring the performance of different explanation methods. 4. Identification of general and specific challenges in XAI, particularly in gradient-based explanations. The paper also discusses the limitations of vanilla gradients and integrated gradients, such as noise in irrelevant features and discontinuous gradients, and proposes solutions to address these issues. The authors conclude by highlighting the importance of further research to develop more effective and robust methods for gradient-based feature attribution.The paper "Gradient based Feature Attribution in Explainable AI: A Technical Review" by Yongjie Wang, Tong Zhang, Xu Guo, and Zhiqi Shen provides a comprehensive overview of gradient-based feature attribution methods in the context of explainable AI (XAI). The authors aim to address the growing need for understanding the internal mechanisms of black-box AI models, particularly in critical applications like healthcare and autonomous driving. They categorize XAI into *ante-hoc explanation* and *post-hoc explanation*, with the latter further divided into *model explanation*, *outcome explanation*, and *model inspection*. The paper focuses on gradient-based explanations, which are particularly useful for neural network models due to their seamless integration and ability to satisfy axiomatic properties. The authors propose a novel taxonomy that categorizes gradient-based feature attribution into four groups: *vanilla gradients based explanation*, *integrated gradients based explanation*, *bias gradients based explanation*, and *post-processing for denoising*. Each group is detailed with specific techniques and their evolution over time. Key contributions of the paper include: 1. A novel taxonomy for gradient-based feature attribution. 2. Detailed explanations of various techniques, including their motivation and algorithmic details. 3. Evaluation metrics for measuring the performance of different explanation methods. 4. Identification of general and specific challenges in XAI, particularly in gradient-based explanations. The paper also discusses the limitations of vanilla gradients and integrated gradients, such as noise in irrelevant features and discontinuous gradients, and proposes solutions to address these issues. The authors conclude by highlighting the importance of further research to develop more effective and robust methods for gradient-based feature attribution.