Summary of Fair Active Ranking From Pairwise Preferences, by Sruthi Gorantla and Sara Ahmadian
Fair Active Ranking from Pairwise Preferences
by Sruthi Gorantla, Sara Ahmadian
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 a problem in ranking items fairly and accurately. It focuses on finding a ranking that is probably approximately correct and fair (PACF) using adaptive pairwise comparisons. The goal is to develop an objective function that minimizes the error of groups, which generalizes previous work on best-ranking. The approach involves accessing an oracle that provides stochastic feedback for each query pair. The proposed method can be applied to various domains, including education, healthcare, and finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to find a way to rank things fairly and correctly. It does this by asking questions about which items are better than others. The goal is to make sure the ranking is good enough most of the time, but not necessarily perfect. This method can be used in many areas where we need to compare different things. |
Keywords
* Artificial intelligence * Objective function
