Alex Rosenblat, Data & Society and Tawanna Dillahunt, University of Michigan, School of Information

For Alex: 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 Wall Street Journal, MIT Technology Review, or a summary article I wrote for the Harvard Business Review. For Tawana: Uncovering the Values and Constraints of Real-time Ride Sharing for Low-resourced Populations. Real-time ride-sharing services (e.g., Uber and Lyft) are often touted as leaders in the sharing economy and have become extremely popular services. These services have been found to dramatically lower the cost of transportation. However, there are concerns about fairness and system transparency, and it is unknown how to make real-time ride-sharing services work better among low-income and transportation-scarce households, how these individuals experience these services, and whether they encounter barriers in enlisting these services. To address these gaps, we onboarded 13 low-income individuals living in transportation-scarce environments to Uber as passengers. While these services were found to be beneficial, our findings also suggest that cost, limited payment methods, and low-digital literacy can make such services infeasible. We generalize our findings to broader sharing-economy platforms and contribute design implications for establishing trust and infrastructure to aid stakeholders in making these services more inclusive.