Algorithmic Labor Management and Uber Drivers' Algorithmic Imaginaries

Photo by Dan Gold on Unsplash

This was a pilot research project (and my second-year project at Cornell) that examined the impacts of algorithms and digital data on Uber drivers’ work practices. I consider Uber drivers as a form of “digitally-enabled service workers” whose primary work tasks are matched via digital labor platforms (De Stefano, 2016) and involve direct social interactions with consumers. On labor platforms like Uber, workers and consumers encounter digital data before interacting with each other. On the one hand, algorithms and digital data embody Uber’s implicit assumptions about what counts in the digital workplace. As such, digital data contribute to what Couldry and Hepp (2017) call “objectivation,” which affect how workers and consumers construct social knowledge and organize social space of service encounter. On the one hand, Uber drivers must consider how they react to data in their everyday life. Accordingly, this project examined the following research questions:

  1. How do Uber drivers interpret digital data as social knowledge and for their practical purposes?
  2. How do digital data premediate Uber drivers’ interpretation of physical space and service encounters therein?
  3. What are Uber drivers’ algorithmic imaginaries (Bucher, 2017)? How do their algorithmic imaginaries mediate the power-relations between the platform, workers, and consumers?
  4. How do Uber drivers receive and share work-related information?

To address these questions, I drew on in-depth interviews with Uber drivers as well as discursive analysis of an Uber driver online forum and Uber’s corporate discourse. The findings documented (1) the surveillance functions and ramifications of Uber’s rating system; (2) the coordination and normalization of drivers’ spatial movement through location-related metrics; (3) drivers’ reactive practices which attempt to score well and circumvenet their data contributions to the platform; and (4) drivers’ information-sharing practices on YouTube.

Advisor: Lee Humphreys


Research from this project was also presented at the 2018 ICA Annual Conference and the 2019 ICA Annual Conference.

Ngai Keung Chan
Ph.D. Candidate in Communication

I’m a doctoral candidate in the Department of Communication at Cornell University. Currently, I study how algorithms and performance metrics transform and shape labor control and resistance in the digitally-enabled gig economy.