Summary of Robust Fair Clustering with Group Membership Uncertainty Sets, by Sharmila Duppala et al.
Robust Fair Clustering with Group Membership Uncertainty Sets
by Sharmila Duppala, Juan Luque, John P. Dickerson, Seyed A. Esmaeili
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Data Structures and Algorithms (cs.DS)
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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 paper tackles the problem of fair clustering with incomplete knowledge about group membership. The current approach to addressing this issue has been superficially treated, and the authors propose a new framework that handles noisy group assignments. They introduce a simple noise model requiring only a few parameters from the decision maker. The proposed algorithm for fair clustering provides provable robustness guarantees, allowing the decision maker to balance between robustness and clustering quality. Unlike previous work, this paper’s algorithms come with worst-case theoretical guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making sure that different groups are fairly represented in a cluster. Right now, there isn’t a great way to handle situations where we don’t know which group someone belongs to. The authors of this paper developed a new method for clustering that can deal with noisy information (some mistakes) about which group someone belongs to. Their approach allows the person making the decisions to balance between being fair and getting good results. |
Keywords
» Artificial intelligence » Clustering
