Cleaning up data work: Negotiating meaning, morality, and inequality in a tech startup

Abstract

Data work—the routinized, information-processing operations that support artificial intelligence systems—has been portrayed as a source of both economic opportunity and exploitation. Existing research on the moral economy of data work focuses on platforms where individuals anonymously complete one-off projects for as little as one cent per task. However, data work is increasingly performed inside organizational settings to promote more consistent and accurate output. How do technologists and data workers construct and morally justify these arrangements? This article is based on 19 months of participant-observation research inside a San Francisco-based startup. Drawing on theories of relational work, I show how managers in San Francisco and contractors in the Philippines collaborated to “clean up” the morally questionable status of data work. Managers attempted to engineer interactions with data workers to emphasize fun and friendship while obscuring vast inequalities. Filipino data workers framed American managers as benevolent patrons and themselves as grateful clients to reinforce managers’ sense of responsibility for their well-being. By shifting attention from the structure of roles to the structure of relationships in organization-based data work, this article demonstrates the function of culture and meaning-making in both generating reliable and accurate data and reproducing status hierarchies in the tech industry. Additionally, this article's examination of the complex and often contradictory dynamics of organizational attachment and marginalization has implications for debates about how the conditions of data work can be improved.