Summary of Rethinking Distance Metrics For Counterfactual Explainability, by Joshua Nathaniel Williams et al.
Rethinking Distance Metrics for Counterfactual Explainability
by Joshua Nathaniel Williams, Anurag Katakkar, Hoda Heidari, J. Zico Kolter
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a new framework for generating counterfactual explanations in machine learning. The authors challenge the traditional approach of treating counterfactuals as independent draws from a region around the reference data point, instead suggesting that they should be jointly sampled with the reference from the underlying data distribution. This leads to the development of a distance metric tailored for counterfactual similarity that can be applied to various settings. Through both quantitative and qualitative analyses, the authors demonstrate that this framing allows for more nuanced dependencies among covariates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines explain their decisions better. Right now, there are ways to make models show how they arrived at a certain answer, but they’re not very good. The idea behind this research is to create new data points that are similar to the original one, but with a different outcome. The goal is to understand why a model made a particular decision. By looking at the data in a new way, this paper shows how to make counterfactual explanations better by considering all the variables together. |
Keywords
* Artificial intelligence * Machine learning
