Summary of Fair Submodular Cover, by Wenjing Chen et al.
Fair Submodular Cover
by Wenjing Chen, Shuo Xing, Samson Zhou, Victoria G. Crawford
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 introduces Fair Submodular Cover (FSC), a novel optimization problem that combines submodularity and fairness considerations in machine learning applications. The goal is to find a balanced subset of minimum cardinality that satisfies a threshold for a given monotone submodular function, while ensuring fair distribution with respect to sensitive attributes like gender or age. The paper proposes discrete and continuous algorithms achieving bicriteria approximation ratios for FSC, outperforming previous results for submodular cover without fairness constraints. Empirical evaluations demonstrate the effectiveness of these algorithms on maximum coverage instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that machines learn fairly. When we’re dealing with data that has sensitive information like gender or age, it’s important to make sure that the decisions made by the machine are fair and balanced. The authors introduce a new problem called Fair Submodular Cover (FSC) where they try to find the smallest group of things that meet certain criteria while also making sure that everyone is treated fairly. |
Keywords
» Artificial intelligence » Machine learning » Optimization
