Summary of Info-cels: Informative Saliency Map Guided Counterfactual Explanation, by Peiyu Li et al.
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation
by Peiyu Li, Omar Bahri, Pouya Hosseinzadeh, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a novel approach to Explainable Artificial Intelligence (XAI) by enhancing the Counterfactual Explanation Learning System (CELS). CELS learns saliency maps for instance-based decision-making and generates counterfactual explanations guided by these maps. While CELS demonstrates promising results in terms of sparsity and proximity, it faces limitations in validity. The authors address this limitation by removing mask normalization to provide more informative and valid counterfactual explanations. This approach is evaluated on datasets from various domains, outperforming the original CELS model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a system that helps people understand why artificial intelligence (AI) makes certain decisions. Right now, AI systems are good at making predictions, but they don’t explain how they came to those conclusions. This lack of transparency can make it hard for people to trust AI systems. The new approach uses something called “counterfactual explanations” which helps us see what would have happened if a different choice had been made. The authors test their approach on many datasets and show that it performs better than the original system, providing more accurate and helpful explanations. |
Keywords
* Artificial intelligence * Mask
