The Making of Performative Accuracy in AI Training: Precision Labor and Its Consequences

Accuracy and precision are central values in the AI communities and the technology sector. This paper provides empirical evidence on the construction and organizational management of technical accuracy, demonstrating how technology companies’ preoccupation with such values leads to harm. Drawing on nine months of multi-sited ethnographic fieldwork in China, we document how AI trainers’ everyday work practices, challenges, and harms stem from clients’ demands for high levels of technical accuracy. We introduce the concept of precision labor to unpack the labor dimension of constructing and performing accuracy in AI training. This concept highlights the hidden and excessive labor required to reconcile the ambiguity and uncertainty involved in this process. We argue that precision labor offers a new lens to illuminate three critical aspects of AI training: 1) the negative health and financial impacts of hidden and excessive labor on AI workers; 2) emerging harms, including workers’ subordinate roles to machines and financial precarity; and 3) a conceptual contribution to contexts beyond AI training. This contribution re-centers arbitrariness in technical production, highlights the excessive demands of precision labor, and examines the legitimization of labor and harm. Our study also contributes to existing scholarship on the prevailing values and invisible labor in AI production, underscoring accuracy as performative rather than self-evident and unambiguous. A precision labor lens challenges the legitimacy and sustainability of relentlessly pursuing technical accuracy, raising new questions about its consequences and ethical implications. We conclude by proposing recommendations and alternative approaches to enhance worker agency and well-being.