Zones of (un)certainty: studying the temporal stabilization of ground truths for medical AI
Medical data annotation is a critical yet contingent process that (re)configures ambiguous medical decisions and data representations into ground truths for artificial intelligence (AI). Extending critical data, AI, and STS research on uncertainty in data annotation, this article develops the concept of zones of (un)certainty to capture the epistemic time-spaces through which annotators transform uncertainty and dissonance into stabilized ground-truth data for medical AI. Drawing on interviews w
Record details
Published: 7 August 2026
Source: AI & Society
Category: Research
Topics: Healthcare
Retrieved: 8 August 2026
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How to cite this record
ethics.ai (7 August 2026), “Zones of (un)certainty: studying the temporal stabilization of ground truths for medical AI,” evidence record 17490, https://ethics.ai/record/17490 (originally published by AI & Society).
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