Min’enhle Ncube

Doctoral Research Fellow

​Min’enhle holds an Advanced MSc in Cultural and Development Studies from KU Leuven and a MSocSci from the University of Cape Town.

Her research background comprises medical anthropology, migration, developmental policy, paradigms and frameworks in development, the First 1000 Days (early childhood), chronic illness, urbanity, housing and postcolonial neoliberalism. She has researched in Maphisa, Zimbabwe and Brussels, Belgium.

She works as an applied anthropologist for two technology startups, one for cleaner environments and another for educational gaming. Min’enhle is interested in using her anthropological background to analyse digital technologies’ efficacy and ethics in developing communities in Africa and to use this knowledge to further develop useful technology on the continent.

mimincube.com | HUMA, University of Cape Town

Artificial intelligence (AI) in the transition towards automated health diagnostics has severe consequences in people’s lives. There has been an insufficient investigation of the socio-political issues that cause certain groups of people to be harmed rather than advantaged by AI’s rapid permeation. In the Sub-Saharan setting, a range of ethical themes across healthcare applications impacts more impoverished populations’ marginalization, complicating AI’s social responsibility.

For instance, one theme regards the bias within data used to train algorithms, posing a tremendous challenge in applying AI to healthcare with a dearth of large clinical datasets for training AI models, which also require specific medical expertise that is costly and time-consuming. There is a general lack of locally produced data for AI systems building because of minimal digitisation and electronic medical record use on the continent.

Other ethical themes include bias outcomes within the algorithm and accountability in AI. With limited data, AI is as good as the data on which it is trained, leading to algorithmic bias. On trust and accountability, the central question is the dependency of health care practitioners on AI models, whether machines replace human judgement instead of speeding up the process leading to valid conclusions.

As in many Sub-Saharan African regions, digital health systems in Zambia are relatively new, with clinics adopting emerging software developments from startups. The area poses an excellent opportunity to examine these emerging developments, ethical frameworks they adopt or fabricate and what future these technologies might take.

This presentation uses an anthropological approach to examine the efficacy of AI usage in healthcare in Zambia, in the context of automated inequality resulting from AI algorithms that serve the Southern African demographic.