Globalizing fairness attributes in machine learning: A case study on health in Africa

As machine learning (ML) systems see far-reaching applications in healthcare, there have been calls for fairness in machine learning to understand and mitigate ethical concerns these systems may pose. Fairness has thus far mostly been defined from a Western lens, and 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 serves as a basis and call for action for furthering research into fairness in global health.