Summary of Classification Under Strategic Self-selection, by Guy Horowitz et al.
Classification Under Strategic Self-Selection
by Guy Horowitz, Yonatan Sommer, Moran Koren, Nir Rosenfeld
First submitted to arxiv on: 23 Feb 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 A novel study in strategic classification investigates how users respond to learned classifiers by deciding whether to participate or not, rather than modifying features. The research examines the effects of self-selection on learning and the implications for the composition of the participating population. A differentiable framework is proposed for learning under self-selective behavior, which can be optimized effectively. Experiments on real data and simulated behavior demonstrate the utility of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When people are motivated to get certain predictions, they might take actions to make those predictions more likely. Usually, research focuses on users modifying features to influence predictions. This study looks at a new situation where users decide whether or not to participate in getting predicted outcomes. The researchers explore how self-selection affects learning and the kind of people who end up participating. They also propose a way to learn while considering this self-selective behavior. Finally, they test their ideas using real data and simulated actions. |
Keywords
* Artificial intelligence * Classification
