Algorithms are changing the way people work in an increasing number of fields. This research explored the impact of algorithmic, data-driven management on human workers and work practices in the context of ridesharing services, Uber and Lyft.
Working with a team in the Social Computing Lab, we began looking at the emergence of smart, intelligent systems which track, collect data, automate, and optimize work practices. Some examples of these include algorithmic scheduling, route optimization, and work assignment. Our project aimed to understand psychological and social impacts of software algorithms on human workers to illustrate how to design software algorithms to help create a better work practices for people.
Through an iterative process, our group developed a semi-structured interview protocol to learn how independent contractors for Uber and Lyft use the application during their job. We conducted interviews with drivers, passengers, and full-time employees of these services throughout the United States over Skype and learned about people's mental models of how various algorithms and aspects of the ridesharing system work, and how these perceptions influenced their behaviors when working. Specifically, participants were asked about the algorithms invloved with passenger-driver assignment, dynamic pricing, rating, and hot-zones.
By creating a coding scheme and analyzing archival data from online forms, such as facebook groups through affinity diagramming, we came across several interesting findings:
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