Reprograming the Narrative Machine: Toward a Decolonial Ethics of Artificial Intelligence in Global Health

Abstract

Contemporary uses of artificial intelligence (AI) in global health are shaped not only by technical expertise but also by embedded narrative logics such as assumptions about whose experiences count, whose perspectives define the problem, and whose futures are imagined in algorithmic attempts to provide solutions. This paper examines the narrative machine embedded within AI-driven health technologies and argues that the epistemological foundations of such systems are deeply entwined with colonial-era patterns of knowledge extraction, abstraction, and representation. Through a theoretical lens informed by postcolonial and decolonial studies as well as narrative ethics, this paper proposes a decolonial analytic of AI systems as narrative machines: tools that not only process data but also inscribe particular worldviews. I explore how these systems often exclude or distort local health epistemologies, particularly in the Global South, leading to interventions that are technologically sophisticated but culturally disembedded and ethically fraught. In practical terms, the paper examines case studies of AI-enabled diagnostic platforms, epidemiological modeling tools in the Caribbean and Africa. It identifies three domains where decolonial intervention is possible: (1) Participatory design methodologies that center narrative sovereignty; (2) Ethical audit frameworks that account for epistemic inclusion; and (3) Policy structures that resist data extractivism in favor of relational, consent-based data practices. This paper contends that addressing global health inequities through AI demands not just better data or fairer algorithms, but a transformation of the narrative structure through which technological futures are conceived and operationalized.

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