Working with Machines

Abstract

Software algorithms are changing how people work in an ever-growing number of fields, managing distributed human workers at a large scale. In these work settings, human jobs are assigned, optimized, and evaluated through algorithms and tracked data. We explored the impact of this algorithmic, data-driven management on human workers and work practices in the context of Uber and Lyft, new ridesharing services. Our findings from a qualitative study describe how drivers responded when algorithms assigned work, provided informational support, and evaluated their performance, and how drivers used online forums to socially make sense of the algorithm features. Implications and future work are discussed.

Standing Out from the Crowd

“…This leaves all parties to the crowd labor interaction attempting to figure out what the algorithm did and why. Lee et al [25] discuss the problems that algorithmic mediation poses for drivers in ridesharing services, who need to be able to determine how the algorithm makes decisions about ride allocation, attempting to account for the decision processes that lie behind the observable actions in order to adjust their own behavior accordingly, to maximize gain, to minimize hassle, to try to control their ratings, or simply to understand the system. Ekbia and Nardi [8] coined the term heteromation to describe "technical systems that function through the actions of heterogenous actors".…”

Everything We Do, Everything We Press

“…Workplace monitoring can also impact morale [41], perception of workloads [5], and relations between staff and managers [64]. They can also raise concerns over the decontextualized and depersonalized nature of data gathered for the purposes of monitoring [40,62,70], and its value or fairness as a measure of performance and success of an individual [23]. Furthermore, as interactions between workers and management get mediated by data, pre-existing tensions or misunderstandings can be intensified [61].…”

“…Yet, as noted by Moore [53], despite the long-contested use of digital technology to monitor worker performance and productivity, quantification and algorithmic or data-driven work appear to be on the rise. Perhaps most prominently, these trends appear to clearly materialize in the case of ondemand workforces [40,51,72] emerging within the rapidly evolving landscape of 'gig' or platform economy [37].…”

“…suggesting more effective route plans to workers in warehouses [77]); it can build awareness of the practices of colleagues [9,63]; it can support decision-making on issues related to workplace comfort and the health of staff [18]; and ultimately the collection and presentation of data related to worker activity can be used for performance management [77]. At the same time questions have been raised around how the quantification of workplaces removes worker agency [61], provides new forms of controlling workers [40,62] and accounts for work in ways that are decontextualized and artificially objective [62].…”

Algorithmic Leadership: The Future is Now

“…As noted above, many organizations utilizing “gig” workers cannot command workers to engage in jobs at particular times or in particular locations. Instead, they often use financial incentives or threats of being removed from the platform in order to encourage certain behaviors (Lee et al, ; Rosenblat & Stark, ). But increasingly, such organizations are also employing targeted messages that appeal to the workers’ interests in order to persuade them to work longer or more frequently.…”