Summary of Globalizing Fairness Attributes in Machine Learning: a Case Study on Health in Africa, by Mercy Nyamewaa Asiedu et al.
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
by Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong, Maria Nagawa, Sanmi Koyejo, Katherine Heller
First submitted to arxiv on: 5 Apr 2023
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 The proposed paper explores the concept of fairness in machine learning (ML) applications in healthcare, particularly in the African context where power imbalances between the Global North and South already exist. The authors propose specific fairness attributes relevant to Africa and identify potential areas where they can be applied in different ML-enabled medical modalities. This work aims to provide a foundation for further research into fairness in global health. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making sure machine learning tools used in healthcare are fair. It’s important because some systems might make unfair decisions, especially when it comes to people in Africa who already face many challenges. The researchers suggest ideas for what “fairness” means in this context and where it can be applied in different medical areas that use machine learning. The goal is to encourage more research on making these tools fair. |
Keywords
» Artificial intelligence » Machine learning
