Summary of Fair Minimum Representation Clustering, by Connor Lawless et al.
Fair Minimum Representation Clustering
by Connor Lawless, Oktay Gunluk
First submitted to arxiv on: 6 Feb 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 challenge of performing k-means clustering while ensuring demographic groups have a minimum level of representation in each cluster. The authors demonstrate that the traditional Lloyd’s algorithm can lead to unfair outcomes where certain groups lack sufficient representation, motivating the need for fairer clustering approaches. To address this issue, they formulate the problem through a mixed-integer optimization framework and propose a variant called MiniReL that directly incorporates fairness constraints. Experimental results show that incorporating fairness criteria leads to more representative clusters with minimal increase in computational cost on standard benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to group people together based on their characteristics, but with an important rule: each group should be big enough to make a difference. Right now, the most popular way of doing this (called k-means) can sometimes create groups that are too small and don’t count for much. The authors want to fix this by creating new rules for grouping people together that ensure everyone’s voice is heard. |
Keywords
* Artificial intelligence * Clustering * K means * Optimization
