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.

翻译：随着机器学习应用于医疗保健的增长，人们呼吁对机器学习进行公平性考虑，以了解和减轻这些系统可能带来的伦理问题。公平性对非洲的全球健康产生影响，因为非洲已经存在着全球南北不平等的权力失衡。本文旨在探讨全球健康的公平性，以非洲为案例研究。我们提出了考虑非洲背景下的公平性属性，并界定它们在不同机器学习医疗模态中可能发挥作用的地方。本研究为深入研究全球健康公平性提供依据和呼吁。