Crafting computer vision through human eyes: An AI laboratory ethnography

This article investigates the emergence of epistemic uncertainty in computer vision (CV), an AI subfield that equips machines with visual capabilities. Drawing on 9 months of ethnographic fieldwork in an AI laboratory, I trace the knowledge production in CV models across training, validation, and review processes, identifying three key sources of uncertainty: flashing data affordances, ambiguous validation standards, and contested knowledge translations. To address these “invisibility problems,” scientists “operate” on image data, transforming raw image datasets into entities through sensory rather than purely quantitative metrics. By conceptualizing machine learning as a sensory, interactive, and processual knowledge system, this paper highlights the role of visual communication in shaping the epistemological foundations of AI.