The Taking Economy: Uber & Co

Research. Preliminary evidence suggests that the so-called sharing economy firms may be leveraging their access to data about users and their control over the user experience to mislead, coerce, or otherwise disadvantage sharing economy participants.

A new paper from Ryan Calo from University of Washington – School of Law; Stanford University – Law School; Yale Law School and Alex Rosenblat from Data & Society Research Institute lists all the good and even more bad there has been said about the ‘sharing economy’ but their paper focuses on the fundamental critique that has seen little attention to date. “Put simply, platforms like Airbnb, Lyft, and Uber possess deeply asymmetric information (data) about and power over consumers and other participants in the sharing economy. And they are beginning to leverage that power in problematic ways. The sharing economy seems poised to do a great deal of taking—extracting more and more value from participants while continuing to enjoy the veneer of a disruptive, socially-minded enterprise,” they write.

Part of their conclusion sounds: “Although difficult to verify without behind-the-scenes access, there is evidence that sharing economy firms are already taking advantage of their power over participants. Uber sometimes operates in a legal gray area such that drivers or the company risk citation by local authority for operating without a taxi license. In March of 2017, the New York Times revealed that Uber systematically targets law enforcement officers—identified by the phones they use, their location, and other factors through a tool called “Greyball”—and purposely makes it difficult for those riders to find Uber drivers to whom to issue a citation.  The company went so far to create a “fake version of the app “populated by ghost cars.”

Other examples are mentions such as manipulation of the perceptions of consumers. Some users report opening the application on their phone and seeing plenty of cars driving around their pick-up location, visualized with icons. But after the user clicks to request an Uber, these “phantom cars” disappear and the consumer faces a wait. Or the experiments Uber is running on what ride-hailers might be willing to pay. Apparently, in studying its consumers, the Uber data science team discovered that people whose phone batteries are low are more willing to pay inflated or “surge” pricing—leading to concerns that the company is interested in what amounts to contextual or individualized price-gauging.

Read the whole paper here

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