Summary of Fairgridsearch: a Framework to Compare Fairness-enhancing Models, by Shih-chi Ma et al.
FairGridSearch: A Framework to Compare Fairness-Enhancing Models
by Shih-Chi Ma, Tatiana Ermakova, Benjamin Fabian
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Machine learning models are becoming crucial in decision-making processes, but they can also perpetuate or amplify biases found in real-world data. To address this issue, researchers have proposed various bias mitigation methods and base estimators. However, choosing the most effective model for a specific application remains a significant challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In critical applications like decision-making, machine learning models can be both powerful tools and sources of bias. The goal is to create fair and accurate predictions that don’t reproduce existing biases in data. This paper doesn’t directly address this problem but provides context about the importance of addressing biases in machine learning. |
Keywords
* Artificial intelligence * Machine learning
