With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces 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 conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.

翻译：随着机器学习技术在医疗保健领域的广泛应用，人们呼吁开发相关技术以理解和减轻这些系统可能存在的偏差。在基于机器学习的健康解决方案开发过程中，公平性考量对非洲具有特殊意义，该地区本就面临全球南北之间不公平的权力失衡。本文以非洲为案例，探索全球健康领域的公平性问题。我们通过范围综述提出非洲背景下公平性考量所需关注的差异轴，并阐明这些差异在不同机器学习支持的医疗模式中可能如何发挥作用。随后，我们对672名普通民众和28名聚焦非洲的机器学习、健康及政策领域专家开展定性研究，以获取关于所提出差异轴的佐证证据。分析聚焦殖民主义这一关键属性，探讨人工智能、健康与殖民主义之间的相互作用。在预先识别的属性中，我们发现殖民历史、原籍国和国民收入水平是参与者认为会导致人工智能系统产生偏差的具体差异轴。然而，专家与普通民众之间存在意见分歧：尽管专家普遍认为殖民历史对人工智能技术在非洲的开发与实施具有相关性，但多数受访的普通民众认为人工智能与殖民主义之间不存在直接关联。基于这些发现，我们为在非洲开发具有公平意识的机器学习健康解决方案提供了实用建议。