Gig platforms are already reinventing management in real-time
Automated platforms define autonomy and manage labor in startling new ways that may soon crossover and impact how we all lead and work
For many senior human resources executives I know, Uber’s arrival into the labor ecosystem is a remarkable crystal ball in which they see future working models evolving before their eyes. Uber and its fellow platform companies are developing labor models in which the traditional middle-management command and control layer is replaced by algorithms that closely monitor and control what workers can and can’t do. With AI-based systems more and more common across large corporate workforces, researchers are eager to understand the models and techniques platform companies are developing today, since their migration into “mainstream” work settings may only be a matter of time.
If the preceding claim seems exaggerated, keep in mind that algorithm-driven platforms already manage the schedule of most large-scale factory production, most large-scale warehousing activities, most retail worker shifts, airline crew assignments, cargo ship navigation, and port operations, and endless other work settings. Algorithms are also involved in an increasing number of recruiting, hiring and performance evaluation processes, their gateway into the “white-collar” working world. Since the dawn of the industrial age, there have always been control systems in corporations; however, these new AI-based systems have raised new alarms for many reasons, from their mechanistic nature to the opaqueness of their coding and decision-making logic.
The concerns about algorithm managers are spurring a lot of research into the subject, and a new paper from Wharton’s Lindsey Cameron is representative of this line of inquiry. She conducted a four-year study of workers in the largest sector of the platform economy, ride-hailing services. During this time, she observed and interviewed drivers and worked as a driver herself. Her specific focus is what she calls “consent,” which refers to workers’ continuing acceptance of the way in which the platforms define, manage, censure, and compensate their work. Her paper, she notes, “goes beyond the ‘carrots and sticks’ metaphors used to describe how consent is generated in traditional workplace and, instead, considers how platforms, through algorithmic management, have renewed and repurposed notions of workplace consent to create this impression of freedom within a presumably even tighter iron cage.”
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