Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
Abstract
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.
Cite
Text
Asiedu et al. "Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa." ICLR 2023 Workshops: MLGH, 2023.Markdown
[Asiedu et al. "Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa." ICLR 2023 Workshops: MLGH, 2023.](https://mlanthology.org/iclrw/2023/asiedu2023iclrw-globalizing/)BibTeX
@inproceedings{asiedu2023iclrw-globalizing,
title = {{Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa}},
author = {Asiedu, Mercy Nyamewaa and Dieng, Awa and Oppong, Abigail and Nagawa, Maria and Koyejo, Oluwasanmi O and Heller, Katherine A},
booktitle = {ICLR 2023 Workshops: MLGH},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/asiedu2023iclrw-globalizing/}
}