Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa

TL;DR

This paper explores fairness in machine learning applications for healthcare in Africa, proposing context-specific fairness attributes and highlighting their importance in addressing global health inequities.

Contribution

It introduces fairness attributes tailored to the African healthcare context and discusses their application across various ML-enabled medical modalities.

Findings

Proposes context-specific fairness attributes for Africa

Identifies key ML modalities where fairness considerations are critical

Serves as a foundation for future research in global health fairness

Abstract

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.