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：随着机器学习在医疗保健领域的应用日益增长，人们呼吁在机器学习中引入公平性，以理解和缓解这些系统可能带来的伦理问题。公平性对非洲的全球卫生具有重要影响——全球北方与南方之间本已存在不平等的权力失衡。本文旨在以非洲为案例，探索全球卫生中的公平性问题。我们提出在非洲背景下需考虑的一系列公平性属性，并阐明它们可能在不同机器学习驱动的医疗模式中产生作用。本研究为推进全球卫生公平性研究提供了基础与行动呼吁。