Summary of Stability and Multigroup Fairness in Ranking with Uncertain Predictions, by Siddartha Devic et al.
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
by Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan
First submitted to arxiv on: 14 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 The paper explores the relationship between ranking functions and predictor uncertainty in classification tasks. It proposes a novel approach to ranking functions that combines stability, fairness, and predictor uncertainty. The authors show that uncertainty-aware ranking functions can achieve both individual and multigroup fairness while maintaining stability. This work has implications for applications where rankings are used, such as search engines or hiring committees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to rank things when the predictions aren’t perfect. Right now, many ranking systems use a single prediction from a model. But what if that model is uncertain about its answers? The researchers propose new ways of making rankings that take into account this uncertainty. They show that these methods can make fair and stable rankings, which is important for things like hiring or search results. |
Keywords
* Artificial intelligence * Classification
