ToTs & TiPs


Monday, April 11, 2016, 2:30pm to 4:00pm


CGIS Knafel K354

A Case Study of Uber’s Drivers: How employment structures and hierarchies emerge through software

Photo: Lucy Nicholson / Reuters

Uber manages a large, disaggregated workforce that delivers a relatively standardized experience to passengers while simultaneously promoting drivers as entrepreneurs whose work is characterized by freedom, flexibility, and independence. Uber, like other companies in the on-demand economy, uses its identity as a platform and a technology company to elude legal responsibility for its drivers as a traditional employer. It claims to provide a “lead generation application” for drivers to connect with passengers, but this neutral branding of its role as an intermediary belies the important employment structures and hierarchies that emerge through its software application. Through a 9-month empirical research study of Uber driver experiences, myself and my colleague, Luke Stark, found that Uber does leverage significant control over how drivers do their jobs, but this control is structured to be indirect. The opacity of control is achieved through a range of semi-automated managerial functions, but foremost amongst these are: algorithmic labor logistics management; driver surveillance and the rating system; and performance targets and policies that limit the choices drivers can make to optimize their individual earnings on the system. Our conclusions are two-fold: first, that the information and power asymmetries and opacities produced by the Uber application are fundamental to its ability to structure indirect control over its workers; second, our analysis illustrates how the rhetorical invocation of technology and algorithms are used within the on-demand economy to structure corporate relationships to labor. Our study of the Uber driver experience points to the need for greater attention to the role of platform disintermediation in shaping power relations and communications between employers and workers.

Speakers: Alex Rosenblat is a researcher and technical writer at Data & Society. Her areas of research include socio-technical systems, the intersection of technology and labor, and the social and civil rights implications of emergent technologies, such as police body-worn cameras. She currently examines the relationship between semi-automated systems and labor management, with a particular focus on the on-demand economy. She holds an MA in Sociology from Queen’s University in Canada, and a BA in History from McGill University. Twitter: @mawnikr