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[edit] A Case Study of Uber's Drivers: How employment structures and hierarchies emerge through software---talk and brainstorming

Date: April 27, 2016, at 7:00 PM
Presenters: Alex Rosenblat
Location: 3-333
Abstract: 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 restructure its employment relationship to drivers, who are classified as independent contractors. 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 (NYU), found that Uber leverages significant control over how drivers do their jobs, but this control is structured to be indirect. The opacity and efficacy 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. For a quick synopsis, see media coverage from The Awl, WSJ, MIT Technology Review, or an article I wrote for HBR.

How can you help?

I'd like the hive mind to help think through the privacy considerations in protecting the identities of drivers in future research, and to examine the ways that platforms can leverage user data (drivers and passengers) to create targeted prices and tiered wages. I'm also interested in issues of user design and deception (see this article I wrote for Motherboard about Uber's phantom cars), and the potential for automating inequities or automating power and knowledge symmetries between workers and platforms. There are broader questions I'd like to dig into as well, such as: When technology creates new efficiencies and reduces friction in transactions between demand (customers) and supply (workers), where do latent points of friction emerge? How do the rhetorics of marketplace efficiency clash with the goals of individual earners? (Example: tipping is "inefficient.")

Bio: Alex Rosenblat is a researcher and technical writer for the Intelligence & Autonomy Initiative at Data & Society, a project supported by the John D. and Catherine T. MacArthur Foundation. She tweets @mawnikr.
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