  Machine learning (ML) depends on data to train and verify models. Very often,
organizations outsource processes related to data work (i.e., generating and
annotating data and evaluating outputs) through business process outsourcing
(BPO) companies and crowdsourcing platforms. This paper investigates outsourced
ML data work in Latin America by studying three platforms in Venezuela and a
BPO in Argentina. We lean on the Foucauldian notion of dispositif to define the
data-production dispositif as an ensemble of discourses, actions, and objects
strategically disposed to (re)produce power/knowledge relations in data and
labor. Our dispositif analysis comprises the examination of 210 data work
instruction documents, 55 interviews with data workers, managers, and
requesters, and participant observation. Our findings show that discourses
encoded in instructions reproduce and normalize the worldviews of requesters.
Precarious working conditions and economic dependency alienate workers, making
them obedient to instructions. Furthermore, discourses and social contexts
materialize in artifacts, such as interfaces and performance metrics, limiting
workers' agency and normalizing specific ways of interpreting data. We conclude
by stressing the importance of counteracting the data-production dispositif by
fighting alienation and precarization, and empowering data workers to become
assets in the quest for high-quality data.
