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Beyond the Data: A Conversation with Alex Rosenblat, Author of Uberland

Alex Rosenblat is a technology ethnographer and the author of Uberland: How Algorithms Are Rewriting the Rules of Work. She works as a Researcher at the Data & Society Research Institute. Uberland examines the role that algorithmic technologies play in managing workers, like when a mobile app matches a ridehail driver with a passenger. Below are some insights from Rosenblat on that process and the ways in which these sorts of algorithms are changing the nature of work.

What is Uberland about? What was your research process for the book?

Starting in 2014, I did nearly four years of ethnographic research with ridehail drivers across more than 25 cities in the U.S. and Canada, observing and interviewing hundreds of drivers. Because Uber is a central topic of debate across so many domains, my qualitative research takes me into many directions, and that journey was part of the research process for the book, too. The experiences drivers have with their working conditions might be implicated in debates about anti-trust, labor law, algorithmic bias, sexual harassment, transportation, regulation—you name it.  

Often, my job was to assess the claims Uber made to drivers, to the public, to cities, and to other stakeholders against the realities that drivers faced on the ground. As part of my research process, I sourced journalistic accounts, scholarly analyses, lawsuits, and other sundry materials of a debate over the role of technology in society that was simultaneously fast-moving and archival. For many drivers I met or observed in my research, it was a good bad job compared to their alternatives, but it was nowhere near the headier claims Uber and other sharing economy boosters narrated over what Uber would mean for society.

Uber’s biggest promise to society is that drivers are entrepreneurs who can be their own boss—and that Uber had the technology to scale entrepreneurship for the masses. One of my earlier findings was that drivers do, in fact, have a boss—an algorithmic one. The algorithmic boss makes suggestions about how drivers should behave on the job and if they don’t comply with many of those suggestions, they risk deactivation. And they often don’t have the information or power they need to make informed decisions.

Why rideshare drivers? What does this population tell us about the changing nature of work?

Uber drivers do not dominate the workforce. They are a small part of the labor pool. But Uber is so well known that Uber’s drivers always symbolize more than their job functions and create room for debate over all the ways that technology affects society. Silicon Valley companies shape American culture in expansive and powerful ways, and Uber’s drivers provide a lens for assessing the dynamics of technology and power in American society.  

One key question for the future of work, of course, is, if you’re managed and disciplined by an algorithm, how does that affect employment classification? But another question that follows is, if Uber treats drivers more like consumers, is consumer protection law the redress drivers should seek for remedies to labor issues, like wage theft?

What were you most surprised by while researching and writing this book?

In some regions, Uber is perceived to be a force of Silicon Valley political power, and in other regions, it's simply another layer of the service economy.

For me, the key insight of Uberland is that drivers are treated like consumers of Silicon Valley technology. By blurring the line between work and consumption, Uber opens up a window for assessing the broader dynamics of power between individual users and powerful platforms. Perhaps the framework of consumption can provide a pathway forward for bargaining with algorithms. For example, consumers of algorithmic technology should be empowered collectively. Thinking about protections for users of technology is one pathway for thinking about how disaggregated workers can bargain with algorithms, too.   

You interviewed many rideshare drivers while researching this book. What are some of the most common challenges you heard about from these workers?

Some of the most common challenges facing drivers include frustration with pay cuts and unfairness that is exacerbated by the opacity of Uber’s algorithmic management. But there is no singular challenge. Uberland shows us the cultural dynamics that twist and turn us in contradictory directions.

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