Ethical and Operational Approaches for Addressing Gender Bias in AI Health Technology in African Contexts

Abstract An urgent need exists to define comprehensive ethical and operational frameworks designed to address gender bias in artificial intelligence–driven health applications within unique healthcare environments of Africa. These frameworks would address the critical challenge of gender disparities that exist within AI systems, which can worsen health outcomes for women. This article outlines proactive ethical and operational approaches to mitigate gender bias in AI health applications deployed in African settings. It expands on ethical complexities surrounding consent in deployment of AI applications and highlights the need for transparency about AI’s role in patient care and strict data governance policies that are sensitive to vulnerabilities of women. The article examines key challenges of gender bias related to AI in health technologies and how existing ethical and operational frameworks may not be suited for the African context. With this background, it advocates for gender-sensitive and gender-transformative design frameworks, mandating multidisciplinary teams with gender studies, and Afro-feminist expertise. Additionally, data collected from FemTech applications in Africa can offer complementary solutions by integrating this data into African AI health systems to improve accuracy and inclusivity. This article also draws attention to the need for gender-differentiated data in health datasets within African health systems to ensure AI algorithms are trained on context-specific data. The article further proposes establishment of oversight committees with strong gender representation and deep expertise in AI ethics. These committees would enforce compliance with ethical standards and promote accountability. If efforts are made to address and mitigate gender biases in the design and implementation of AI systems, these technologies have the potential to contribute to improved health outcomes in Africa. However, this impact depends on the responsible ethical development of AI, robust data governance, and consideration of local contexts.