Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers

Authors

  • Alex Rosenblat Data & Society Research Institute
  • Luke Stark New York University & Dartmouth College

Keywords:

on-demand economy, Uber, design, platform, ridesharing, ridehailing, algorithm, data, labor, management, rating, surge pricing, entrepreneurship, independent contractor, sharing economy

Abstract

Uber manages a large, disaggregated workforce through its ridehail platform, one that delivers a relatively standardized experience to passengers while simultaneously promoting its drivers as entrepreneurs whose work is characterized by freedom, flexibility, and independence. Through a nine-month empirical study of Uber driver experiences, we found that Uber does leverage significant indirect control over how drivers do their jobs. Our conclusions are twofold: First, the information and power asymmetries produced by the Uber application are fundamental to its ability to structure control over its workers; second, the rhetorical invocations of digital technology and algorithms are used to structure asymmetric corporate relationships to labor, which favor the former. 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.

Author Biographies

Alex Rosenblat, Data & Society Research Institute

Alex Rosenblat is a Researcher and Technical Writer at the Data & Society Research Institute.

Luke Stark, New York University & Dartmouth College

Luke Stark is a Postdoctoral Scholar in the Department of Sociology at Dartmouth College, and holds a PhD from the Department of Media, Culture, and Communication at New York University. His research explores the emotional politics of interfaces in digital media.

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Published

2016-07-27

Issue

Section

Articles