Summary of Beyond the Veil Of Similarity: Quantifying Semantic Continuity in Explainable Ai, by Qi Huang et al.
Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI
by Qi Huang, Emanuele Mezzi, Osman Mutlu, Miltiadis Kofinas, Vidya Prasad, Shadnan Azwad Khan, Elena Ranguelova, Niki van Stein
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 The proposed metric aims to quantify semantic continuity in Explainable AI (XAI) methods and machine learning models. The authors posit that interpretable and trustworthy models should provide consistent explanations for similar inputs, reflecting a deep understanding of the input’s meaning. To achieve this, they leverage XAI techniques to assess semantic continuity in image recognition tasks by analyzing how incremental changes in input affect the provided explanations. This approach helps evaluate models’ ability to generalize and abstract semantic concepts accurately and compares different XAI methods in capturing their internal reasoning processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to measure how well AI systems can explain themselves. The idea is that if an AI system is good at understanding what it sees, it should give similar explanations for similar images. The authors tested this idea by looking at how different AI techniques change their explanations when the input image changes slightly. This helps us understand how well these AI techniques can capture the underlying meaning of an image and provide more reliable and transparent AI systems. |
Keywords
* Artificial intelligence * Machine learning
