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Paper short abstract

Drawing on ethnography in Czechia, this paper shows how AI-driven diabetes technologies selectively translate metabolic knowledge into algorithmic temporalities, sidelining menstrual hormonal rhythms and producing gendered care labor that reveals epistemic inequalities in automated healthcare.

Paper long abstract

This paper explores how AI-driven diabetes technologies produce gendered inequalities through the selective epistemic incorporation of bodily temporalities into algorithmic care. Drawing on ethnographic research with patients, diabetologists, and open-source developers conducted in Czechia—a post-socialist healthcare context shaped by public provision, marketized medical technologies, and strong patient communities—I analyze how automated insulin delivery systems translate metabolic knowledge into algorithms and how cyclical hormonal rhythms related to menstruation remain only partially legible within them.

These systems rely on continuous glucose monitoring data to model metabolic responsiveness over time, privileging linear temporal patterns that implicitly reflect a non-menstruating, male-coded metabolic norm. Although clinical knowledge about hormonal effects on insulin sensitivity exists, it is unevenly operationalized: menstrual hormonal cycles introduce recurring yet variable changes that exceed the temporal “learning horizons” of many commercial algorithms. As a result, users who menstruate must perform additional temporal labor—resetting profiles, or adjusting sensitivities—to maintain metabolic stability.

Rather than framing this as an absence of knowledge, the paper argues that menstrual hormonal rhythms are epistemically marginalized through algorithmic design choices that shape what counts as actionable data, redistributing responsibility and labor along gendered lines. Open-source systems developed within the Czech diabetes community partially counter these dynamics through transparency and collective tinkering.

Methodologically, the paper thinks with temporality as an ethnographic and conceptual lens for examining epistemic inequalities in AI healthcare, contributing to the anthropology of AI by tracing how care, knowledge, and responsibility are unevenly reconfigured through the temporal infrastructures of algorithmic systems.