Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
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arXiv
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| Format: | Preprint |
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2023
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| author | Asiedu, Mercy Nyamewaa Dieng, Awa Oppong, Abigail Nagawa, Maria Koyejo, Sanmi Heller, Katherine |
| author_facet | Asiedu, Mercy Nyamewaa Dieng, Awa Oppong, Abigail Nagawa, Maria Koyejo, Sanmi Heller, Katherine |
| contents | With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_02190 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa Asiedu, Mercy Nyamewaa Dieng, Awa Oppong, Abigail Nagawa, Maria Koyejo, Sanmi Heller, Katherine Machine Learning Artificial Intelligence Computers and Society With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health. |
| title | Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2304.02190 |