  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.
