Summary of Evaluating Fair Feature Selection in Machine Learning For Healthcare, by Md Rahat Shahriar Zawad et al.
Evaluating Fair Feature Selection in Machine Learning for Healthcare
by Md Rahat Shahriar Zawad, Peter Washington
First submitted to arxiv on: 28 Mar 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 A novel algorithmic fairness approach for feature selection is proposed to mitigate the risk of exacerbating health disparities through automated decision making in healthcare. The method considers equal importance across demographic groups, jointly optimizing a fairness metric with an error metric during feature selection. This approach is evaluated on three publicly available healthcare datasets, demonstrating improvements in fairness metrics while maintaining balanced accuracy. The significance of this work lies in its potential to address both distributive and procedural fairness in the context of fair machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to make sure that machines don’t unfairly decide things based on who someone is. This is important because computers are now making decisions about healthcare, which can affect people’s lives. The idea is to treat everyone equally by choosing features (like medical test results) in a way that doesn’t favor one group over another. The method was tested on three different sets of healthcare data and showed that it worked well without sacrificing accuracy. |
Keywords
* Artificial intelligence * Feature selection * Machine learning
