Summary of Are Objective Explanatory Evaluation Metrics Trustworthy? An Adversarial Analysis, by Prithwijit Chowdhury et al.
Are Objective Explanatory Evaluation metrics Trustworthy? An Adversarial Analysis
by Prithwijit Chowdhury, Mohit Prabhushankar, Ghassan AlRegib, Mohamed Deriche
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper contributes to the field of Explainable AI (XAI) by introducing a novel explanatory technique called SHifted Adversaries using Pixel Elimination (SHAPE). SHAPE is designed to provide accurate and reliable explanations for neural network models, building upon causal notions of necessity and sufficiency. The authors demonstrate that SHAPE outperforms existing popular importance-based visual XAI methods like GradCAM, GradCAM++, and RISE in terms of robustness and reliability when evaluated using adversarial metrics. This work highlights the need for human involvement in evaluating XAI methods and raises questions about the efficacy of existing evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making artificial intelligence (AI) more transparent and trustworthy. The authors created a new way to explain how AI models make decisions, called SHAPE. They tested this method and found that it’s better than other methods at providing accurate explanations. The paper also questions whether the current ways we evaluate these explanations are good enough, suggesting that humans should be involved in evaluating them. |
Keywords
» Artificial intelligence » Neural network
