Summary of Fairness in Ranking: Robustness Through Randomization Without the Protected Attribute, by Andrii Kliachkin and Eleni Psaroudaki and Jakub Marecek and Dimitris Fotakis
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
by Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek, Dimitris Fotakis
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 machine learning framework is proposed for optimizing fairness in ranking-related problems. The approach focuses on post-processing rankings without requiring protected attribute information. The method is shown to be robust against P-Fairness and effective with respect to Normalized Discounted Cumulative Gain (NDCG) in extensive numerical studies, outperforming previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make online ads, job listings, or music recommendations fair has been developed. It works without needing information about sensitive attributes like age or gender. This method is tested and shown to be good at making sure people are treated equally while also giving them what they want. It does a better job than other methods in this area. |
Keywords
* Artificial intelligence * Machine learning
