Summary of Feature Responsiveness Scores: Model-agnostic Explanations For Recourse, by Seung Hyun Cheon et al.
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
by Seung Hyun Cheon, Anneke Wernerfelt, Sorelle A. Friedler, Berk Ustun
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 paper proposes a novel approach to provide explanations for machine learning models used in decision-making applications, such as lending and hiring. It highlights the importance of ensuring that these explanations align with consumer protection rules, which require providing “principal reasons” to individuals who receive adverse decisions. The authors show that standard attribution methods can mislead consumers by highlighting features that cannot be changed to achieve a desired outcome. They propose scoring features based on responsiveness, i.e., the probability that an individual can attain a desired outcome by changing a specific feature. The paper develops efficient methods for computing responsiveness scores and presents an empirical study demonstrating how this approach can lead to recourse and mitigate harm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make machine learning models more fair and transparent. They want to help people understand why they were rejected for a loan or job, so they can try again. Right now, the explanations we get from these models might not be very helpful because they just point out things that didn’t work in our favor. The authors are trying to change this by coming up with new ways to explain how these models work and what we can do to improve our chances of getting accepted. |
Keywords
* Artificial intelligence * Machine learning * Probability
