Summary of What Is Fair? Defining Fairness in Machine Learning For Health, by Jianhui Gao et al.
What is Fair? Defining Fairness in Machine Learning for Health
by Jianhui Gao, Benson Chou, Zachary R. McCaw, Hilary Thurston, Paul Varghese, Chuan Hong, Jessica Gronsbell
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 machine learning study investigates the application of fairness principles to ensure equitable healthcare decision-making. The review highlights the potential biases in ML models that can exacerbate existing health disparities. It provides an overview of fairness metrics commonly used in health, including a case study on an openly available electronic health record dataset. The paper also discusses future research directions, emphasizing challenges and opportunities in defining fairness in health. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of scientists looked at how machine learning (ML) can be unfair when it comes to healthcare decisions. They wanted to make sure that ML models are fair and treat everyone equally. They studied different ways to measure fairness and used a real-world example from electronic health records. The study found that there is still more work to do to ensure that ML models are fair and don’t make things worse for some groups. |
Keywords
» Artificial intelligence » Machine learning
