Together with my team of PhDs and Postdocs, I am currently undertaking a multi-sited ethnography of the evolving revolution in global health systems: AI (Artificial Intelligence)-informed healthcare. As health data is increasingly viewed as countries’ ‘future oil’, concerns about ‘algorithmic ethics’ are emerging among scholars and the public. Previous research has shown that datasets in AI (re)produce social biases, discriminate and limit personal autonomy. Nevertheless, such literature has mainly focused on AI design and institutional frameworks, examining the subject via legal, technocratic and philosophical perspectives, while disregarding the socio-cultural context in which AI systems operate, especially in organizations where human agents collaborate with Algorithms. This limitation is problematic because frameworks for ‘ethical AI’ currently regard human oversight as crucial, assuming that humans will correct or resist AI when needed. However, empirical evidence for this assumption is scarce. Little is known about when and why people intervene or resist AI. Previous research is confined to single, primarily Western studies, which precludes generalisation of findings. Our research is innovative on four fronts: 

1. To empirically analyze decisive moments in which data-analysts follow or deviate AI – moments that are deeply impacting national health policies and individual human lives.
2. To do research in six national settings with various governmental frameworks and in different organizational contexts, enabling us to contrast findings, eventually leading to a theory on the contextual and organizational factors underlying ethical AI.
3. To use innovative anthropological methods of future-scenarioing, which will enrich the anthropological discipline by developing and finetuning future-focused research. For this methodology we collaborate with data-scientists, programmers, artists, AI experts, social scientists and physicians.
4. To connect our anthropological insights with the expertise of AI-developers, and partners with relevant health decision-makers and policy-institutions, allowing to both analyze and contribute to fair AI.