'Algorithmic Domination in the Gig Economy' by James Muldoon and Paul Raekstad in (2022) European Journal of Political Theory 1–21 comments
Digital platforms and application software have changed how people work in a range of industries. Empirical studies of the gig economy have raised concerns about new systems of algorithmic management exercised over workers and how these alter the structural conditions of their work. Drawing on the republican literature, we offer a theoretical account of algorithmic domination and a framework for understanding how it can be applied to ride hail and food delivery services in the on-demand economy. We argue that certain algorithms can facilitate new relationships of domination by sustaining a socio-technical system in which the owners and managers of a company dominate workers. This analysis has implications for the growing use of algorithms throughout the gig economy and broader labor market.
The authors argue
Algorithmic decision-making is increasingly deployed in a variety of important contexts from criminal justice and policing to credit scoring and healthcare (Kitchin, 2017). The proliferation of algorithms throughout society has led to the growth of a large body of literature in science and technology studies, legal studies, computer science, sociology, geography and media studies, among others (Beer, 2017; Striphas, 2015; Ziewitz, 2016). Corresponding to this growth in the use of algorithms has been an explosion of app-mediated platform labor (Graham et al., 2017). In the UK, the number of adults who undertook tasks obtained through a digital platform doubled from 2016 to 2019 (Huws 2020: 4). This has also resulted in the rapid spread of digital management practices throughout different parts of the workforce.
Algorithms are employed because they promise to make processes more efficient, accurate, and unbiased. However, an emerging critical literature has called into question the idea that algorithms can evade human bias in decision making. There is a range of evidence suggesting that algorithms can often reproduce and exacerbate structural inequalities, injustices, and forms of unfreedom, rather than alleviate them (Benjamin, 2019; Noble, 2018; O’Neil, 2016). Recent discussions of algorithmic injustice have contributed to calls for greater attention to questions of fairness and accountability including issues of procedural fairness and more substantive approaches focused on interventions into decision outcomes and their social impact (Janssen and Kuk, 2016; Pasquale, 2015; Zimmerman et al., 2020).
While questions of algorithmic injustice have received widespread consideration, political philosophers have so far paid less attention to the question of how algorithms impact our freedom. In this article, we develop the concept of algorithmic domination to address these concerns and provide an account of the dominating effects of algorithms used as tools of worker control. Algorithmic domination can occur in a variety of different domains, but we focus here on the role of algorithms as a tool by companies to manage contract workers involved in app-work in the gig economy (Duggan et al., 2020).
Consider the following examples. Amazon warehouse employees report working under constant surveillance with timed toilet breaks and just nine seconds to process a package (Selby, 2017). Uber drivers must work during peak periods to chase ‘surge pricing,’ often earning less than the minimum wage. A hidden army of ‘microworkers’ labor on platforms such as Amazon Mechanical Turk and Clickworker, receiving as little as US$2–3 an hour for monotonous piece-rate tasks with no employment benefits or protections (Jones, 2021). What these examples have begun to point to is the potential negative impact of the deployment of algorithms in the gig economy and other sectors impacted by the introduction of digital technology (Rosenblat and Stark, 2016).
For contractors of companies such as Uber and Deliveroo, the tasks, time to complete, rate of pay, and delivery route can all be automatically assigned through the protocols of the company’s software. Within such socio-technical systems, it can appear as if workers are no longer instructed by a human manager but by an automated computer algorithm. This raises the question of whether certain precarious workers could be said to be governed – and perhaps even dominated – by a non-human computer system. Does a company’s ability to nudge, incentivize, manipulate, and control workers’ behavior through algorithmic management constitute an objectionable form of uncontrolled power?
We argue that algorithmic domination occurs when an individual is subjected to an uncontrolled power, the operations of which are determined by an algorithm. The particular case study we focus on in this article is gig workers in the food delivery and ride hail sectors, but the concept of algorithmic domination can, in principle, be applied much more broadly to other workers in the gig economy and in standard employment contracts where algorithms are also employed to manage workers (Huws, 2020). In the case of the gig economy, we argue that the use of this software for managing workers facilitates a power structure and social relationship of domination between bosses and workers. Algorithms are deployed by bosses as part of a broader socio-technical system designed and implemented in order to create and sustain a specific regime of labor control (Kitchin, 2017; Lee et al., 2015). Our analysis emphasizes that beneath the appearance of automatic decision making and neutral service delivery lies the recognizable exercise of social power. These systems can increase the capacity for bosses to dominate workers by providing new tools for them to exercise uncontrolled power and weaken the ability of workers to organize and resist This is not a radically new form of power, but an augmentation of existing capacities and their formalization in new socio-technical systems that embed certain patterns of labor management and work relationships as the new normal.
Understanding how these forms of algorithmic domination operate in practice is important due to how tech companies often employ the language of worker flexibility, freedom, and autonomy as key benefits of their business models. Revealing the sham behind their claims of self-entrepreneurship and empowerment helps us understand the realities of work in the platform economy (Ahsan, 2020). Technology companies claim their algorithmic forms of management offer greater freedom. However, if we are right, what they can enable is the increased domination of workers.
Algorithmic domination can give rise to distinctive relationships between bosses and workers mediated through digital technology. Workers taking commands generated by an algorithm may have less room to negotiate specific aspects of their work schedule and may be subject to more stringent and demanding forms of workplace control. The affordances of the new technology increase computational asymmetries between bosses and workers and allow the former to intervene at a more minute level in ways that are not feasible if required to be undertaken by a human supervisor. Algorithmic domination is also distinctive in adopting new systems of gamification and incentive schemes administered through software that has been specifically designed to induce certain responses from workers.
This article proceeds as follows. First, we draw on the writings of labor and socialist republicans to identify how workers are subjected to distinct forms of domination in the capitalist workplace. We then put this literature into conversation with an emerging body of empirical studies of algorithmic management to show how republican theories of non-domination can address cases of work in the gig economy involving systems of algorithmic management. In the following section, we define algorithmic domination and explain how it could be applied to case studies in the food delivery and ride hail sectors. We then argue that the dominating aspect of algorithms in the workplace is not intrinsic to the technology itself, but is part of the power relations established within capitalist enterprises. As a result, we briefly examine an alternative possibility of algorithms utilized by platform co-operatives, which we argue could potentially involve a non-dominating use of algorithms in work processes. Finally, we conclude by establishing a framework for how algorithmic domination could be applied to other cases.
