How Uber Exploits Racial Hierarchies in London

The Algorithm Had a Race Problem Before It Had a Driver

In 2012, Uber launched in London with a promise of frictionless mobility. Tap a button, get a car. No cash, no hailing, no hassle. But for the migrants and racialized workers who would come to make up the vast majority of its drivers, the app delivered a different kind of friction entirely. Dalia Gebrial, a researcher at the University of Oxford, spent years embedded in London’s Uber-driving communities. What she found upends the standard story of gig work. The problem wasn’t just that Uber misclassified its drivers as independent contractors. It was that Uber actively organized a workforce along racial lines, exploiting the specific vulnerabilities of Black and brown migrants who had been left stranded by the 2008 financial crisis (Gebrial, 2022).

Gebrial’s paper, “Racial platform capitalism: Empire, migration and the making of Uber in London,” published in Environment and Planning A, is the first sustained attempt to treat race not as a demographic side note but as a structuring logic of the platform economy. She argues that the very techniques that make Uber profitable—worker misclassification, algorithmic management, and labor atomization—are not neutral tech tools. They are racial practices, honed in the crucible of British imperial history, and deployed to reorganize surplus urban labor after the crash.

This isn’t a story about a few bad drivers being treated poorly. It’s a story about how a global corporation built its business model on the backs of a racially segmented labor market, and how the app itself became a tool for managing that segmentation.

How the 2008 Crash Created Uber’s Perfect Workforce

The standard telling of Uber’s rise in London goes something like this: A tech company saw a gap in the taxi market, built a better app, and disrupted a cozy cartel. Gebrial’s account is darker. She shows that Uber’s emergence in London was made possible by a specific set of conditions that converged after the 2008 financial crisis.

The crisis hit migrant and racialized communities in London disproportionately hard. Unemployment among Black and minority ethnic workers in the UK rose faster and stayed higher than for white workers. The public sector, a major employer for these communities, was gutted by austerity. At the same time, immigration policy became more restrictive, tying migrants’ right to remain to their employment status. This created a pool of workers who were desperate, skilled (many had driving experience from their home countries), and highly vulnerable to exploitation. They couldn’t afford to say no to a job that offered no sick pay, no holiday, and no guarantee of hours.

Gebrial’s ethnographic fieldwork, conducted between 2017 and 2019, involved interviews with 40 Uber drivers in London, as well as participant observation at driver meetups and protests. She found that the vast majority of drivers were first- or second-generation migrants from South Asia, Africa, and the Middle East. Many had university degrees. Many had been professionals in their home countries. In London, they were driving for Uber.

“The conditions of minoritized workers in a global city like London post-2008, and the political economy of platform companies can be said to have co-produced one another,” Gebrial writes. In other words, Uber didn’t just stumble into a diverse workforce. It needed a workforce that was already racialized, already precarious, and already excluded from the formal labor market. The app was the final piece of the puzzle.

The Algorithm as a Racial Manager

If you ask Uber about its algorithm, they will tell you it’s a neutral tool that matches supply and demand. Gebrial shows that this is a fiction. The algorithm manages workers, and it manages them in ways that exploit racial hierarchies.

One key mechanism is what Gebrial calls “algorithmic triage.” Uber’s system tracks driver behavior in real time: acceptance rates, cancellation rates, customer ratings. Drivers who fall below certain thresholds are penalized with reduced access to trips or, eventually, deactivation. But this system doesn’t operate in a vacuum. Drivers who are already marginalized—those who live in outer boroughs with poor public transport, those who work night shifts because they have other jobs, those who face discrimination from passengers—are more likely to trigger these algorithmic penalties. The algorithm doesn’t see race. But it systematically punishes the conditions that race creates.

Gebrial documents how drivers developed strategies to game the system. Some would drive to wealthier, whiter neighborhoods where they were less likely to get low ratings. Others would refuse trips to certain postcodes where they had experienced abuse. But these strategies only worked if drivers could afford to be selective. Many couldn’t. They accepted every trip, every low rating, every risk.

“The algorithm is not just a tool for matching,” one driver told Gebrial. “It is a tool for control. It knows where you are, what you are doing, and it can punish you at any time.”

The Ghost of Empire in the Back Seat

One of the most striking arguments in Gebrial’s paper is that Uber’s model in London is not a break from the past but a continuation of it. She draws on the concept of “racial capitalism,” developed by scholars like Cedric Robinson, to argue that capitalism has always been racialized. The extraction of value from racialized bodies is not an unfortunate side effect. It is the engine.

In London, this history is written into the city’s geography. The areas where Uber drivers live and work—places like Ilford, Wembley, and Southall—are the same areas where colonial migrants were housed in the postwar period. The routes they drive trace the contours of empire: from the wealthy, white center to the diverse, underinvested periphery. The app is just the latest technology for managing this racial division of labor.

Gebrial points to a specific historical parallel: the “lascar” system of the British East India Company. Lascars were Indian sailors who were employed on British ships under conditions that amounted to indentured labor. They were paid less than white sailors, housed in separate quarters, and denied basic rights. The system was justified by a racial ideology that portrayed them as naturally suited to the work. Uber’s model, Gebrial argues, is a digital version of this. Migrant drivers are portrayed as “entrepreneurs” who choose flexibility. In reality, they are trapped in a system that extracts maximum value while minimizing responsibility.

The Myth of the Entrepreneurial Migrant

Uber has always been good at telling stories. The company’s official narrative is that it empowers drivers to be their own bosses. Drivers are “partners,” not employees. They choose when to work and how much to earn. This narrative is particularly potent when applied to migrants, who are often portrayed in popular discourse as enterprising strivers.

Gebrial’s research blows this apart. She found that most drivers did not choose Uber out of a desire for flexibility. They chose it because they had no other options. The formal labor market was closed to them, either because of their immigration status, their lack of British qualifications, or straightforward discrimination. Uber offered a way to earn money without a formal job interview, without references, without a permanent address. But this “freedom” came at a cost.

“The rhetoric of flexibility masks a reality of intense precarity,” Gebrial writes. Drivers reported working 60-80 hour weeks just to make ends meet. They had no access to sick pay, holiday pay, or pension contributions. They bore all the costs of the vehicle, including fuel, maintenance, and insurance. And they were constantly at risk of deactivation for reasons they could not predict or control.

One driver, a former engineer from Nigeria, told Gebrial: “They call it flexibility. I call it being disposable. If I get sick, I don’t get paid. If my car breaks down, I don’t get paid. If a passenger complains about me because of my accent, I don’t get paid. There is no safety net.”

The Limits of the Law

In 2021, the UK Supreme Court ruled that Uber drivers are workers, not independent contractors, and are therefore entitled to minimum wage and holiday pay. This was hailed as a landmark victory for gig workers. Gebrial is more cautious.

She points out that the legal category of “worker” in UK law is weaker than “employee.” Workers are entitled to some protections but not others. They can still be fired without cause. They still have no right to collective bargaining. And the burden of enforcement falls on individual drivers, who must take Uber to court to claim their rights. Most drivers, Gebrial found, were unaware of their legal entitlements or were too exhausted to pursue them.

“The law is a tool, but it is not a solution,” one driver told her. “Even if I win in court, I still have to drive tomorrow. The system does not change.”

Gebrial also notes that the legal victory has not addressed the racial dynamics of the platform. Black and minority ethnic drivers are still overrepresented in the workforce. They still face discrimination from passengers and the algorithm. And they are still excluded from the formal labor market that offers better conditions. The legal framework treats all workers as equal, but the labor market is not equal.

What the Research Does Not Prove

Gebrial’s paper is a powerful indictment of Uber, but it is not a complete theory of everything. She is careful to note what her research does not show.

First, the study is based on a relatively small sample of 40 drivers in London. While Gebrial’s ethnographic approach allows for deep insight, it cannot tell us whether her findings apply to other cities or other platforms. Uber in Nairobi or São Paulo may operate differently.

Second, the paper does not prove that Uber’s executives consciously designed the algorithm to exploit racial hierarchies. Gebrial argues that the system produces racialized outcomes, not that it was intended to. This is a subtle but important distinction. Racism can be structural without being intentional.

Third, the research does not offer a simple solution. Gebrial is skeptical of reforms that leave the platform model intact. She suggests that any meaningful change would require a fundamental restructuring of the labor market, including stronger immigration protections, public investment in transport, and a universal basic income. These are big asks.

What This Actually Means

• ▸Platform companies do not just exploit workers. They exploit racialized labor markets. Uber’s model in London was made possible by the specific vulnerabilities of migrant and minority workers after the 2008 crisis. Any attempt to regulate gig work must address the racial dimensions of the labor market, not just the legal classification of workers.

• ▸Algorithms are not neutral. Uber’s algorithm systematically penalizes drivers who are already marginalized. Regulators should require platform companies to audit their algorithms for racial bias, just as they would for any other form of discrimination.

• ▸The narrative of “flexibility” is a tool of control. The idea that drivers choose Uber for the freedom to set their own hours masks the reality of 80-hour weeks and no safety net. Policymakers should be skeptical of claims that gig work empowers workers.

• ▸Legal victories are necessary but not sufficient. The Supreme Court ruling that Uber drivers are workers was a step forward, but it has not changed the underlying racial dynamics of the platform. Enforcement is weak, and the labor market remains segmented.

• ▸The ghost of empire is still in the machine. The racial hierarchies that structured colonial labor markets are being reproduced in the digital economy. Understanding this history is essential to building a fairer future.