Standing Out from the Crowd

Abstract

CSCW researchers have become interested in crowd work as a new form of collaborative engagement, that is, as a new way in which people's actions are coordinated in order to achieve collective effects. We address this area but from a different perspective -that of the labor practices involved in taking crowd work as a form of work. Using empirical materials from a study of ride-sharing, we draw inspiration from studies of the immaterial forms of labor and alternate analyses of political economy that can cast a new light on the context of crowd labor that might matter for CSCW researchers.

Effective? Ethical? Behavior Control & Algorithmic Management in Virtual Environments

“…Issues like these are partially why retention rates are so low; only slightly over half the drivers who on-boarded in 2013 remained active a year later [19]. Workers that remain on this platform find that the temporal demand of riders and working on their own time do not always go hand in hand [26]. Drivers can have flexible hours unlike the fixed schedule many taxi drivers see, but rides are not always available outside of set times.…”

“…They control and manage drivers, instead of serving as a tool that drivers can leverage to help make decisions [25]. As managerial decisions are made in the background, the involved parties are left trying to figure why the algorithm did what it did, so they may adjust their own behavior accordingly [26]. There are 3 main behavior control tools Uber uses to influence the relationship between supply and demand: driver assignment, dynamic surge pricing, and data driven evaluations (ratings).…”

“…This acts as a form of surveillance and performance rating on drivers, forcing them to adapt to customers social and emotional needs, adding a form of emotional labor to the driver's responsibilities [24]. Drivers and Uber emphasize the importance of building a good relationship with passengers [26]. This rating system empowers passengers to act as middle managers over drivers [19], effectively crowdsourcing control and supervision of drivers [25].…”

“…This rating system empowers passengers to act as middle managers over drivers [19], effectively crowdsourcing control and supervision of drivers [25]. However, most passengers do not understand how the rating system works [26]. Drivers are aware of this, 45 percent of respondents in a survey agreed the rating system does a poor job of promoting trust between passengers and drivers [26].…”

“…However, most passengers do not understand how the rating system works [26]. Drivers are aware of this, 45 percent of respondents in a survey agreed the rating system does a poor job of promoting trust between passengers and drivers [26]. Uber will send out routine messages that recommend specific ways to act to get high ratings, and actions that will get low ratings [19].…”