The Big Problem with Uber’s Big Data: Ethics and Regulation of Data Ownership

“Technology is neither good nor bad; nor is it neutral”

(Kranzberg 1986, p. 545)

That is why it is key to understand how we, as users and moderators, give additional meaning to technological features and participate in the complex chain of interactions they bring across society. Living in the mere beginnings of the era of “Big Data”, it is pressing to address the cultural and ethical implications of a phenomenon often idolised and seen as a universal answer by many in the business and scientific spheres (Boyd & Crawford 2012). Who should have access to big datasets? Should the use of Big Data be regulated and how to address the privacy concerns that come with mass information collection? Using the work of Danah Boyd and Kate Crawford “Critical Questions for Big Data”, we will analyse how the gig economy companies, taking Uber as an example, are handling Big Data and why it has caused a series of ethical controversies and recent legislative action.

Questioning Big Data

Boyd and Crawford define Big Data as a “cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology” (Boyd & Crawford 2012, p.663). This definition breaks away from the understanding that Big Data is just a dataset too large for human comprehension and transforms the term into a more complex phenomenon of social, not scientific, origins. Consequently, this leaves room for theorising and critiquing the role of Big Data in many social shifts. Boyd and Crawford develop six provocative claims about the influence of this phenomenon, three of which have a specific relevance to the case of Uber and will serve as the theoretical backbone of analysis – evaluating how Big Data changed the definition of work but created new ethical issues and failed to deliver on its promise of objectivity. 

Big Data Changes Definitions

Big Data is at the core of Uber’s business model. It collects, analyses, and stores huge amounts of information that is later used to fuel the algorithms of the platform and produce an “optimised” (according to pre-determined criteria in the AI) personalised service. With these abilities, Uber gained an extraordinary market-breaking advantage in the ride hailing industry (Rogers 2015). Most importantly, it redefined how “work” is perceived by introducing the “on-demand digital independent contractor” (Malin & Chandler 2017) model. 

Big Data gave Uber enough power and agency to be able to attract workers with its ease-of-use and escape the classic employee-employer relationship, defining itself as a data-powered platform that serves as a mediator between drivers and consumers (Wilhelm 2018). With this position, Uber solely relies on Big Data and the algorithms that collect and use it to balance the complex relationship between service providers and customers, an approach that seems heavily technologically deterministic. Nevertheless, for good or bad, Uber and the data-powered gig economy have irreversibly changed the way people define work in the service industry – to a point that “app workers” accounts for the majority of the ride-hailing and delivery labour force (Malin & Chandler, 2017).

Just Because it Is Accessible Does not Make it Ethical 

Boyd and Crawford make the important point that Big Data can produce “destabilising amounts of knowledge and information that lack the regulating force” (Boyd & Crawford 2012, p.666). Uber is experiencing this effect more and more recently with a growing amount of legislative action taken against the company’s data collection policies and lack of algorithmic transparency. The ethics of data ownership and availability have become the “next frontier in the fight for gig workers’ rights” (Clarke 2021). 

As Uber drivers are considered independent contractors and not employees, the company has not deemed it necessary to share with its workers the data it collects about their work and how it influences the algorithm’s opinion of individual workers. Drivers also have no way to retrieve their personal data, to erase it, or to migrate it if they decide to start working at a competitive platform (although the GigCV initiative is currently trying to make the latter possible). 

The ethics behind data ownership in the gig economy is a heavily disputed topic, but recent court decisions are turning the debate in favour of workers (Reshaping Work, 2021). In a landmark case of March 2021, Amsterdam’s District Court ruled that Uber must disclose “data used to deduct earnings, assign work, and suspend drivers” and also shed light on how driver surveillance systems are used in the Netherlands (Ongweso Jr, 2021). Similar rulings across Europe suggest that the debate around regulating Big Data is more a “when” and “how” than an “if” question at that point. 

Claims to Objectivity and Accuracy Are Misleading 

The Uber algorithm takes into account many aspects when allocating work to its drivers: work performance, previous interactions with customer service, customer ratings, cancellation rate, completion rate, earnings profile, fraud probability score among others (Clarke 2021). However, nobody truly knows the exact extend of data collection and the way algorithms utilise this information. Uber is notoriously reluctant to share such data with researchers, policymakers, or the public. Nevertheless, there are jurisdictions where Uber has been legally forced to provide certain datasets to data scientists, most notably in Chicago. This lead to the discovery of bias and racial discrimination in the company’s dynamic pricing algorithms in a study on over 68 million Uber rides in Chicago (Wiggers 2020). Critiquing Big Data with a study based on Big Datasets is exactly the kind of self-reflexivity that is often lacking in the scientific community (Boyd and Crawford 2012), but this trend can also be explained by the lack of openly accessible datasets that deem a larger territorial study on the subject impossible.

We Are Our Tools

There is a “deep industrial drive toward gathering and extracting maximal value from data” (Boyd & Crawford 2012) and that is not inherently negative. However, we should remain mindful and question the ethical implications of this new data-driven society. As the example of Uber showcased, Big Data is not a magical universal solution, and its flawed collection and interpretation can cause serious social divides and issues. “We are our tools” (Boyd and Crawford 2021, p.675) and we should be aware and responsible for the consequences they cause.