Summary of Online Algorithmic Recourse by Collective Action, By Elliot Creager and Richard Zemel
Online Algorithmic Recourse by Collective Action
by Elliot Creager, Richard Zemel
First submitted to arxiv on: 29 Dec 2023
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 research paper explores a new dimension in algorithmic recourse, focusing on the online setting where system parameters are updated dynamically. Unlike traditional approaches that focus on individual-level recourse, this study reveals how groups can collectively shape system decisions by leveraging the parameter update rule. The findings suggest that coordinating feature perturbations can significantly improve recourse when users work together. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a world where automated decisions affect our lives, researchers are working to make sure we have a say in how those decisions are made. This paper looks at how groups of people can work together to change unfavorable decisions when the rules of the system are constantly changing. The study shows that by combining their efforts, people can actually make it harder for machines to make bad decisions. |
