Summary of Collective Counterfactual Explanations Via Optimal Transport, by Ahmad-reza Ehyaei et al.
Collective Counterfactual Explanations via Optimal Transport
by Ahmad-Reza Ehyaei, Ali Shirali, Samira Samadi
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 novel approach to counterfactual explanations that takes into account the underlying data distribution and individual perspectives. The traditional method of providing cost-optimal actions can lead to new competitions and unanticipated costs when substantial instances seek state modification. To address this issue, the authors suggest a collective approach that utilizes the current density of individuals to inform recommended actions. This collective method is formulated as an optimal transport problem, leveraging the extensive literature on optimal transport. The proposed approach improves upon the desiderata of classical counterfactual explanations and is supported by numerical simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Counterfactual explanations are a type of action that can change someone’s label to a desired class. However, this method might not be suitable for everyone because it doesn’t take into account how people perceive certain actions as outliers. The authors propose a new approach that looks at the current data distribution and individual perspectives when suggesting actions. This collective approach is like finding the best route between two points, which helps to make better suggestions. |
