Beware of the Uber Algorithm....what you see is NOT what you get.

Much of the usefulness of the Uber drivers and customer is the data they both generates for Uber—not solely when the driver is transporting a passenger, but even when the driver is waiting to be summoned and isn't making any money. Uber drivers call that time without fares “dead miles.” Drivers may spend that time roaming around waiting for their next job from the Uber app. Or they may drive from a low-density area where they dropped off their last passenger back to a high-density area where they are more likely to find a new passenger.

While those "dead miles" seem to be of no value, the data it generates for Uber during that time is immensely valuable to Uber. Uber drivers continue to generate useful data for Uber even when they are not transporting anyone because they relay data back to the central platform from which inferences can be drawn about traffic patterns, and which feed into supply and demand algorithmic analyses. 

That dataset feeds into the company's algorithms for understanding traffic patterns as well as for estimating—and manipulating—supply and demand through surge pricing and other techniques, such as "phantom cabs" where the app shows a car nearby, especially in a remote area, but there is actually none.   All the data that Uber drivers produce is an invaluable business asset, helping Uber develop new partnerships with both municipalities and other corporations, and for maintaining its competitiveness.

It is not just that Uber does not care about driver who drive with "dead time" it is simply that they even benefit from that, and the driver certainly gets nothing from Uber in return for this exchange. Drivers’ data also helps Uber determine surge pricing, a feature that has drawn particular scrutiny from riders and drivers alike. Although the company claims that surge pricing is just a reflection of supply in demand, research suggests that the surge is not straightforward: Uber creates the mirage of a marketplace that obscures how its algorithms manipulate supply and demand.

The app, in short, looks like a market and quacks like a market. But companies like Uber merely adopt tropes of a marketplace. The apps’ user interface suggests a reality that doesn’t exist in practice.In a real marketplace, supply responds directly to the pressures of demand. This isn’t the case with Uber where the supply of drivers is instead mobilized to meet predicted passenger demand, as through surge pricing.

Drivers are shown a map of “surge zones,” which ostensibly reflect the demand for rides in different parts of the city at a given time. While this is how Uber frames it, this actually isn’t the case in practice. The suppliers (drivers) get to see only what the Uber system expects the state of the market to be, and not the market itself. Demand is also walled off from supply. When you open the Uber app as a rider, you see a map of your local pickup area, with little sedans around that appear to be drivers available for a request. While you might assume these reflect an accurate picture of market supply, the way drivers are configured in Uber’s marketplace can be misleading.  The presence of those virtual cars on the passenger’s screen does not necessarily reflect an accurate number of drivers who are physically present or their precise locations. Instead, these phantom cars are part of a “visual effect” that Uber uses to emphasize the proximity of drivers to passengers. Not surprisingly, the visual effect shows cars nearby, even when they might not actually exist. Demand, in this case, sees a simulated picture of supply. Whether you are a driver or a rider, the algorithm operating behind the curtain at Uber shows a through-the-looking-glass version of supply and demand.

What Uber has produced is a mirage of a marketplace—an app experience that produces the sensation of independent riders and drivers responding to the natural fluctuations of supply and demand. But a look underneath the hood reveals a system that intermediates and influences more than it facilitates free exchange.

As the uses of artificial intelligence continue to broaden, society will increasingly confront questions around the power these technologies can and should have. As we move toward regulation, we need to question the narratives offered by companies and make sure that policy reflects reality. In the case of Uber, what you see, is not what you actually get.