What Uber Did To Cities
Uber normalized data-driven surveillance for urban services. At what cost?
In the summer of 2010 TechCrunch co-founder and investor Michael Arrington used a new app called UberCar. He loved it. “The service…eliminates everything bad about a taxi experience,” Arrington wrote, before envisioning a beautiful future.
“I can imagine it now – click a button and see a variety of options,” he predicted. “A five star rated driver 15 minutes away in a late model Prius at 2x taxi rates, or a 1975 Camero [sic] 1 minute away with a three star rating for .5x taxi rates. Choose your car, driver and price and get exactly what you pay for.”
Nearly a decade later, Uber doesn’t operate quite as Arrington envisioned – Uber vehicles are more likely Camry than Prius; more likely Malibu than Camaro – but his early excitement has been echoed over and over across the globe as Uber has rapidly expanded. The significant scandal and backlash that’s come with Uber’s aggressive devouring of ride-hail service market share (it controls perhaps as much as 70%) over how it treats its drivers, its competition, or municipal laws has dented its reputation. Yet, its IPO filing this week suggests a lot of people still believe, as Arrington did, that Uber is the future. What kind of future is that? One which moves from the roads to engulf entire cities.
Above all, Uber’s model – as the company has stated – “is designed around efficiency.” To maximize its efficiencies, every aspect of Uber relies upon data extraction and analysis. Uber collects and analyzes data at every juncture as it gamifies labor and customer service through its app. It knows who drivers are, where they’re going, how long it takes them to get around, how long they’ve been on the road, and what people think of them. It learns similar things about its customers, too – where people go and sometimes with whom, where they live or work, and perhaps even what they like to eat. The more it knows, the stronger its network becomes, and the more likely people are to see it not as an optional service, but a necessary one.
In its approach, Uber is representative of many tech companies and platforms of the last decade. Facebook and Google have deployed the approach most notoriously, but while our worries about things like disinformation or personal data occupy our attention, the world beyond our screens has been subjected to similar algorithmic interpretation. Our cities are at the cusp of rapid and significant change, as we move from merely being surrounded by surveillance infrastructure like CCTV – as we were by the end of the last century – to living amid infrastructure that is surveillant.
Just like Facebook and Google changed the way we thought about information rather than just communication, Uber has made the case for more than just disruption of transportation. Uber has helped to alter the way we think about cities, entirely. By virtue of its consumer success, Uber has prompted a desire for everything to be fit to its mold – that all aspects of municipalities be determined more by stark proprietary data-driven efficiency analysis, and less by ephemeral human needs.
You can hear echoes of Uber’s logic in the marketing language of a project like Alphabet-owned Sidewalk Labs’ Toronto Quayside development pitch. Alphabet-owned Sidewalk Labs promises a futuristic neighbourhood built on data, described with quasi-programming language. The new Toronto Quayside will have things like “all-weather infrastructure and data-driven management tools” and “better data integration” for a more “comprehensive approach to social and community services that delivers better outcomes.”
Concerns about Sidewalk Labs’ data mining prompted the organization in October to suggest that all data be controlled by an “independent Civic Data Trust,” that “would approve and control the collection of, and manage access to, urban data originating in Quayside.” The proposal was quickly denounced by Ann Cavoukian, the former privacy commissioner for the province of Ontario, who was serving as an advisor to Sidewalk Labs. “They said ‘We, Sidewalk Labs, we’ll embed privacy by design proactively, but we have no control over what third parties do and others involved in the trust,’” Cavoukian told BNN Bloomberg. “I said, ‘Stop. I need to leave. I need to resign.’”
Cavoukian’s departure was highly symbolic, but possibly too late, because de-idenficiation is actually beside the point. If we’re at the stage of discussing how all the data collected about us will be thoroughly anonymized, the fight for privacy is already over, and we’ve lost it. And it means we’ve already accepted that mass data gathering of all facets of city life – so much so that individual people, not to mention what they do and where they go, might be easily identified – is the correct approach to urban planning. Concerns for personal privacy are still warranted, but those concerns would be more manageable if the streets we walk on aren’t already tracking our steps to begin with.
This is the result of following Uber’s logic. I encourages us to work from the wrong assumption – that is, that the primacy of data and its ability to create perfection via newfound efficiencies, whether in transport or in cities writ large. As Ben Green writes in his new book, The Smart Enough City, “Efficiency is a normative goal: it favors particular principles and outcomes at the expense of others, typically altering how status and resources are distributed across society.” Figuring out “what should be made efficient… requires the inherently political task of mediating between competing normative visions,” Green writes.
For all the consumer convenience offered by Uber and inherent in its approach to cities, it’s data – not people – that sits at the center of Uber’s ideology. The logic that drives Uber is one that assumes cities are a collection of markets to be exploited, cornered, and monopolized. Efficiencies in the world of Uber are created as determined by technology which treats human behavior in much the same way it does the weather or the day of the week – as pure information to be analyzed to maximize desired outputs like, well, high share prices for Uber’s investors.
“The architecture of the smart city is a fundamentally undemocratic one: many technologies operate by collecting unchecked data about individuals and using opaque, often proprietary, algorithms to make life- altering decisions,” Green writes. “In the process, they create massive information and power asymmetries that favor governments and companies over those they track and analyze, breeding impotence and subjugation within society. In this way, the smart city is a covert tool for increasing surveillance, corporate profits, and social control.”
Even more than that, a smart city – one whose design is reliant on and emerges from automatic mass data collection, and where interactions are under constant surveillance – is fundamentally less human.
Back in 2010, Michael Arrington clarified that the bad things Uber eliminated from the cab riding experience were (in order) “flagging one down, finding cash to pay, and being in a sometimes disgusting car,” as well as “negotiating whether or not I get air conditioning in the summer in NY.” And he was right in a way. Uber is indeed different than taxis, but not really for the reasons he listed. It’s much simpler: Uber cars are not taxis. The two services are not the same, and not just because one was created to destroy the other. The real difference is with the data and the surveillance logic that Uber has infused into mainstream thinking, and which now informs common imaginings of how entire cities should be re-built.
Ultimately, what Uber eliminates most of all is the very thing that makes taking a cab such a quintessential city activity, and what makes cities what they are: the feeling of being lost in a sea of humanity. It eliminates anonymity.