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Nura Jabagi is a PhD candidate in business-technology management at Concordia University.

Uber drivers honk in solidarity with protestors during a strike against the company's recent 25 percent wage cut outside Uber's head office in San Francisco, California.


Following the recent Uber and Lyft drivers’ global strike in protest of low wages, many would agree that it’s time for these multi-billion-dollar companies to put their money where their mouth is. But is money enough to solve this problem? I would argue that the better solution lies in humanizing the almighty algorithm.

The promise of “being your own boss” has been central to Uber and Lyft’s driver recruitment strategies. However, these recruitment strategies more closely resemble a bait-and-switch tactic. Once lured by the promise of freedom and independence, platform drivers typically face a large disconnect between their expectations of autonomy and platform algorithms that are explicitly programmed to control workers.

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Both Uber and Lyft’s platforms are governed by algocracy, a digital governance system where human managers are replaced by algorithms and codes that oversee workers. Unlike traditional work contexts, where human managers supervise and support workers often by building close, trust-based relationships, algocracy relies on continuous data streams of individual workers’ behaviour to manage them.

Although different gig organizations exercise varying degrees of algorithmic control over workers, platforms such as Uber and Lyft exert exceptionally high control over drivers.

On one hand, algorithms are necessary to manage Uber and Lyft’s large and distributed workforces. For instance, algorithmic management drives efficiency by seamlessly matching drivers with clients, and by employing dynamic pricing to manage supply and demand. Certainly, no human could conduct real-time matchmaking on this scale and, in this context, algorithmic management can be seen as supporting drivers by reducing the costs of finding clients.

On the other hand, the algorithms powering Uber and Lyft’s platforms lack transparency and, as a result, often lead to power and information asymmetries between drivers and platform operators that leave drivers disadvantaged. For example, in executing the matching process, Uber’s platform does not provide workers with much information concerning the trip duration nor the destination prior to accepting the trip, meaning that a driver could possibly be displaced quite far for a small fare. Although drivers can decline rides, given that Uber’s system deactivates drivers with low acceptance rates, a drivers’ freedom to “decide when and how you’re working” is illusory.

Well-established psychology theories have shown autonomy to be a critical precursor of workers’ well-being and motivation. Importantly, managers have been shown to play a key role in supporting workers’ autonomy. Managerial behaviours such as offering choice and empowering decision-making, providing positive and noncontrolling feedback, and acknowledging workers’ perspectives while asking for their input have all been found to support a worker’s sense of autonomy. Yet when human managers are replaced by algorithms, these managerial behaviours must be embedded in a platform’s design if gig-organizations truly want to support gig-workers’ sense of autonomy.

Unfortunately, neither Uber nor Lyft’s promises of worker autonomy are reflected in their algorithms. For instance, recent research has found that Uber’s compensation algorithms are too opaque and complex for workers to track their compensation, and to take decisions to maximize their earning potential. In terms of providing feedback, Uber has relied on automated messaging systems to coerce drivers into driving longer hours to hit certain milestones. Contrary to promoting workers’ autonomy, these messages (often sent to drivers trying to log off) seek to control workers by exploiting humans’ natural preoccupation with achieving goals.

Finally, rather than acknowledging drivers’ perspectives, ride-hailing platform algorithms tend to favour clients over drivers. As an example, when clients leave unjustified ratings drivers have had little recourse in contesting such ratings and subsequent deactivations.

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In short, although increasing drivers’ take-home pay will help to calm the tensions that prompted the recent strike, money is but one part of the solution.

The need for autonomy is universal among all humans, and arguably quite important for self-employed gig workers. Notably, by positively impacting workers’ self-motivation, perceptions of autonomy can increase workers’ engagement, performance, and commitment all of which impact an organization’s bottom line.

Research has also shown that providing workers with autonomy can do more to bolster self-motivation than financial incentives. As gig-economy leaders, Uber and Lyft have a responsibility (and strong reasons) to build better algorithms. If the recent emergence of worker-owned apps and gig-work co-ops has taught us anything, it’s that building platforms is simple; what gig workers really want are platforms that respect and empower them in the same way that a good human manager does.

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