Summary of The Benefits and Risks Of Transductive Approaches For Ai Fairness, by Muhammed Razzak et al.
The Benefits and Risks of Transductive Approaches for AI Fairness
by Muhammed Razzak, Andreas Kirsch, Yarin Gal
First submitted to arxiv on: 17 Jun 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 In this paper, researchers investigate the impact of compositional changes in holdout sets on fairness metrics in machine learning models. They experiment with different balance ratios of sensitive sub-groups on CIFAR and CelebA datasets, finding that imbalanced holdout sets can exacerbate existing disparities while balanced ones can mitigate issues introduced by imbalanced training data. This study highlights the importance of carefully constructing holdout sets that are both diverse and representative. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be unfair or biased if trained on unbalanced data. This paper looks at how holding out some examples during training, called a holdout set, affects model fairness. The authors tried different types of holdout sets on two datasets: CIFAR and CelebA. They found that if the holdout set has too many or too few examples from certain groups, it can make the model more unfair. On the other hand, if the holdout set is balanced, it can help reduce bias in the model. |
Keywords
» Artificial intelligence » Machine learning
