Summary of The Fairness-quality Trade-off in Clustering, by Rashida Hakim et al.
The Fairness-Quality Trade-off in Clustering
by Rashida Hakim, Ana-Andreea Stoica, Christos H. Papadimitriou, Mihalis Yannakakis
First submitted to arxiv on: 19 Aug 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 proposed paper introduces novel algorithms to trace the trade-off curve between quality and fairness in clustering problems. It aims to find all clusterings that are not dominated by others in both objectives. Unlike previous work, this study deals with general classes of fairness and quality objectives, encompassing most special cases addressed before. The algorithm’s time complexity is exponential in the worst case, but a polynomial-time solution is presented when cluster centers are fixed and the fairness objective is to minimize imbalance between groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to balance clustering quality and fairness. It develops new algorithms to find all solutions that aren’t outperformed by others in both aspects. This research focuses on general classes of fairness and quality objectives, which covers most specific cases studied before. The algorithm’s time is exponential in the worst case, but a faster solution is possible when cluster centers are fixed and the goal is to minimize group imbalance. |
Keywords
» Artificial intelligence » Clustering
