Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa

Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong, Maria Nagawa, Oluwasanmi O Koyejo, Katherine A Heller

Keywords: machine learning, deep learning, fairness, distribution shifts, health, global health, Africa

TL;DR: This paper identifies fairness attributes that should be considered with respect to health in Africa, and discusses implications for machine learning applications and areas for further research

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.