Work and the Data Economy
A joint meeting of the Society for Economic Anthropology and the Society for the Anthropology of Work
The production, distribution, and consumption of digital data has become an important domain of economic activity. Data is said to upend conventional economic thinking, as a resource that can be transported at negligible cost and used without being depleted. Yet the enactment of the data economy depends on more and less familiar forms of human labor, from the waged work of analysts, modelers, and technicians to the uncompensated and often nonconsensual generation of trace data in everyday life. Sensor networks gathering real-time data have permeated industries from agriculture to shipping, while the digitization of museum holdings and the massification of genetic sequencing have given rise to new value chains that cut across boundaries of public and private. The consequences of these developments are still coming into focus, promising greater efficiency and access but also compounding issues of equity and control. How, we might ask, does data capitalism stand to reinforce inequality along lines of race, gender, class, and disability?
While the advent of the platform-based gig economy has been the object of scholarship and activism in recent years, less attention has been paid to how datafication—broadly defined as the transformation of subjects, objects, and processes into digital data—has influenced more traditional forms of work and economic life. Yet anthropologists of these domains are increasingly finding their own roles recast as chroniclers and practitioners of diverse types of data work. This meeting seeks to thematize and build on such scenes of recognition by exploring emergent data practices, ideologies, and valuation regimes, especially in settings not conventionally associated with high-tech or knowledge work. The meeting will foster interdisciplinary exchange by placing the insights of anthropologists in dialogue with local discussants from fields like information science and critical data studies.
- Marcel LaFlamme, Worcester Polytechnic Institute
- Alex Blanchette, Tufts University
- Karen Levy, Cornell University