Summary of Synthetic Data Generation For Intersectional Fairness by Leveraging Hierarchical Group Structure, By Gaurav Maheshwari et al.
Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure
by Gaurav Maheshwari, Aurélien Bellet, Pascal Denis, Mikaela Keller
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper introduces a novel data augmentation approach designed to enhance intersectional fairness in classification tasks. By leveraging the hierarchical structure of intersectionality, the method learns a transformation function that combines parent categories to augment data for smaller groups. The authors’ empirical analysis on four diverse datasets shows that classifiers trained with this approach achieve superior intersectional fairness and are more robust against “leveling down” compared to traditional group fairness methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves how machines learn to be fair when making decisions about people who belong to different groups, like gender, race, or disability. The idea is to help smaller groups that don’t have as much data by using information from bigger groups related to them. This helps the machine make more fair decisions and not just ignore smaller groups. |
Keywords
» Artificial intelligence » Classification » Data augmentation
