#uberpitch

For example, Judge Jed Rakoff of the United States (of America) District Court in Manhattan is currently hearing an antitrust case against Uber that argues the company conspires to fix prices (in the form of their surge pricing policy). In denying a motion to dismiss the case, Judge Rakoff acknowledges the power of the algorithm, citing its genius in his argument:

Defendant argues, however, that plaintiff's alleged conspiracy is "wildly implausible" and "physically impossible," since it involves agreement "among hundreds of thousands of independent transportation providers all across the United States." Yet as plaintiff's counsel pointed out at oral argument, the capacity to orchestrate such an agreement is the "genius" of Mr. Kalanick and his company, which, through the magic of smartphone technology, can invite hundreds of thousands of drivers in far-flung locations to agree to Uber's terms. The advancement of technological means for the orchestration of large-scale price-fixing conspiracies need not leave antitrust law behind.

Judge Rakoff then proceeds to cite the case of the Silk Road and how the design of such systems, while facilitating automation, do not absolve the creator of agency or responsibility:

Cf. Ulbricht, 31 F. Supp. 3d at 559 ("if there were an automated telephone line that 15 Case 1:15-cv-09796-JSR Document 37 Filed 03/31/16 Page 15 of 27 offered others the opportunity to gather together to engage in narcotics trafficking by pressing "l," this would surely be powerful evidence of the button-pusher's agreement to enter the conspiracy. Automation is effected through a human design; here, Ulbricht is alleged to have been the designer of Silk Road ."). The fact that Uber goes to such lengths to portray itself - one might even say disguise itself - as the mere purveyor of an "app" cannot shield it from the consequences of its operating as much more.

Meanwhile a federal court in San Francisco will be hearing a case in June that argues Uber drivers should receive all the benefits and protections of employees, rather than be merely designated as contractors who are “sharing” their vehicles.

There’s interesting research by Alex Rosenblat and Luke Stark that contributes to the argument that Uber drivers are not contractors or at least not in a position to negotiate a proper contractual relationship. Their argument focuses on the persuasive if not coercive role of the Uber algorithm:

This empirical study explores labor in the on-demand economy using the rideshare service Uber as a case study. By conducting sustained monitoring of online driver forums and interviewing Uber drivers, we explore worker experiences within the on-demand economy. We argue that Uber’s digitally and algorithmically mediated system of flexible employment builds new forms of surveillance and control into the experience of using the system, which result in asymmetries around information and power for workers. In Uber’s system, algorithms, CSRs, passengers, semiautomated performance evaluations, and the rating system all act as a combined substitute for direct managerial control over drivers, but distributed responsibility for remote worker management also exacerbates power asymmetries between Uber and its drivers. Our study of the Uber driver experience points to the need for greater attention to the role of platform disintermediation in shaping power relations and communications between employers and workers.

Alex elaborates on this research in a recent post for the Harvard Business Review titled “The Truth About How Uber’s App Manages Drivers”, as well as her thoughts on the price-fixing trial.

The Guardian in January reported that Uber is using driver’s smartphones to monitor their driving habits, without their knowledge or explicit consent.

Research conducted on human subjects is something that raises significant ethical considerations, and yet this is a clear example of that, and suggests the company is probably conducting other research and analysis of their drives and passengers.

Which is why the issue of Uber’s power is so important. The data that the company is able to collect and correlate is substantive. On a surface level this includes data about traffic, about urban usage patterns, and about personal preferences, whether where you go (home, work, entertainment) or what you eat (UberEats). However it can also include much more valuable data, especially when it comes to facilitating or enabling marketplaces.

Take for example the company’s latest initiative, UberPITCH. Described as “a collaborative project that facilitates innovation within local startup communities” the service involves matching up a prospective investor with an entrepreneur who wants to pitch them an investment idea.

For several hours on a specific day in specific cities, Uber users are invited to try and catch a ride with a notable investor or venture capitalist who is open to hearing their pitch. You’re given 15 minutes, 7 to pitch, and 7 to discuss (1 minute to say hi I guess), before you’re dropped off and the next person gets in to make their pitch.

While UberPITCH is arguably a marketing stunt to get people signing up and using the service (not to mention talking about Uber as I am doing now) it also speaks to a potential diversification of Uber’s services based on data profiling. After all, Uber knows who uses the service, and they have rankings for all those users based both on driver/user reviews but also any other data they can get their digital hands on.

For example what if Uber got into the financial matchmaking service and was able to secure a small commission from any deal that they helped facilitate. What if Uber paid certain investors just to hear pitches, who after all are always looking for the next thing to invest in. What if they matched them up while they went about their usual commutes? What about dating in general? Too risky? Or can the algorithms adapt to that kind of sorting and ranking as well?

Given Uber’s growing knowledge of our urban environments and the people who move through them, there’s considerable potential for the company to facilitate all sorts of social structures, relationships, and transactions that potentially give them a sort of power that neither corporate conglomerates nor political parties or associations have traditionally possessed.

Let’s also not forget Uber’s track record of asking for forgiveness rather than asking for permission. Instead of working with public policy planners and elected representatives the company has consistently invaded a market, defied rules, and waited until their power and presence was unquestionable before sitting down to negotiate terms.

Philip Napoli, a Professor and Associate Dean for Research at the School of Communication and Information at Rutgers University, argues that institutional theory can help us understand and appreciate the role and power that algorithms play in our society.

With that in mind, what if Uber, as a collection of algorithms, is itself a new kind of growing institution? An emerging state that challenges the power and ability of existing nation states? A new society growing within the shell of the old?