Summary of Gradient Based Feature Attribution in Explainable Ai: a Technical Review, by Yongjie Wang et al.
Gradient based Feature Attribution in Explainable AI: A Technical Review
by Yongjie Wang, Tong Zhang, Xu Guo, Zhiqi Shen
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The surge in black-box AI models has raised concerns about their internal mechanisms and reliability, particularly in high-stakes applications like healthcare and autonomous driving. As a result, the field of explainable AI (XAI) has grown exponentially, with various approaches aimed at explaining and analyzing models from different perspectives. Despite this proliferation, it’s challenging to gain a comprehensive understanding of XAI research due to the sheer volume of papers. To address this, we focus on gradient-based explanations, which can be directly applied to neural network models. This review systematically explores existing gradient-based explanation methods, introducing a novel taxonomy that categorizes them into four distinct classes. We also detail technique evolution and introduce human and quantitative evaluations to measure algorithm performance. Furthermore, we highlight the general challenges in XAI and specific challenges in gradient-based explanations. By summarizing state-of-the-art progress and its limitations, this survey aims to inspire researchers to address these issues in future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure AI models are reliable and understandable, especially when they’re used in important situations like healthcare or self-driving cars. There have been many attempts to explain how AI models work, but it’s hard to keep track of all the different approaches. The authors focus on one type of explanation method called gradient-based explanations that can be used for neural network models. They review what’s already been done in this area and introduce a new way to categorize these methods. They also show how to evaluate the performance of these algorithms and highlight some challenges with making AI more explainable. |
Keywords
» Artificial intelligence » Neural network
