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Abstract This article explores platform workers’ strategies for producing sustainable, quality services within platform structures that simultaneously over- and under-determine their work. We present findings from interviews with U.S.-based mental health professionals (n = 48) working on teletherapy platforms. These therapists describe navigating both the presence of platformic controls and the absence of features supporting professional best practices and regulatory requirements. We describe this absence as the “empty shell” characteristic of platforms and argue that it is a central technique through which platforms create scale. Our findings detail the communicative strategies therapists employ to navigate the empty shell and provide quality care to their clients. These strategies can be seen as a form of “articulation work,” a concept drawn from the sociology of work. Attending to articulation work in an emerging platform labor context, such as teletherapy, contributes to our understanding of the politics of platforms. 

Platform labor and gig work have become key sites for understanding a nascent future of work hallmarked by informalization and digitization. A growing body of research emphasizes how experiences of platform work are mediated not only by algorithms and user interfaces, but also by gender, race, local cultures as well as labor hierarchies. Drawing from ongoing ethnographic research on the digital transformation of healthcare, we show how therapists' experiences of platform labor are centrally shaped by the historical and ongoing feminization of mental health work. Platforms reinscribe feminized labor conditions that are pervasive in the healthcare industry, and yet platform labor appears as 'useful' to some therapists as they navigate a set of precarious career choices fundamentally structured by feminization. We use the analytic of the stopgap to describe platforms' two-fold reproduction of the status quo: first by offering an approximation of freedom to individual workers, helping to forestall a crisis of unsustainable work conditions; and second by reinscribing the same logics of exploitation in order to make labor scalable. This stopgap analytic reorients the focus away from the impact of the platforms technologies as such, towards the conditions that make stopgap solutions necessary for survival. It also points towards the importance of intervening in the conditions of exclusion and exploitation that help to create a market for platform stopgaps. 

We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates' information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate's information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.