Summary of Robust Counterfactual Explanations in Machine Learning: a Survey, by Junqi Jiang et al.
Robust Counterfactual Explanations in Machine Learning: A Survey
by Junqi Jiang, Francesco Leofante, Antonio Rago, Francesca Toni
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 surveys recent advancements in counterfactual explanations (CEs) that provide algorithmic recourse for individuals affected by machine learning model predictions. While CEs can benefit those impacted, recent studies have highlighted concerns about the robustness of current methods. To address these issues, researchers have developed techniques to mitigate risks and ensure CE validity. This survey reviews existing works on robust CEs, analyzing various forms of robustness considered, as well as existing solutions and their limitations. The goal is to provide a solid foundation for future developments in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to get a loan, but the bank says no based on a computer model’s prediction. You want to know why they said no so you can try again. Counterfactual explanations (CEs) are like a report card that explains how different your situation would have had to be in order for the loan to be approved. But some recent studies found that most CEs aren’t very reliable. To fix this problem, researchers are working on making CE methods more robust, so people can trust them. This paper looks at what’s being done to improve CE reliability and how it might help people get a fairer chance in the future. |
Keywords
* Artificial intelligence * Machine learning
