Beyond Categories: A Flow-Oriented Approach To Social Justice on Online Labor Platforms

Online labor platforms (OLP) are transforming long-established employment relations, raising questions for researchers and policymakers alike as to the social justice implications of this increasingly pervasive, algorithmic, and platform-mediated form of work. In investigating this issue, this paper makes a case for complementing current category-based approaches to social justice, prevalent in literature and policy on OLPs, with a flow-oriented approach that recognizes the diversity of gig work trajectories when it comes to the situated enactment of social (in)justice. Inspired by the recent work of Tim Ingold and building upon seminal work on social justice by Rawls and Sen, we develop a synthetic framework for revealing the social justice implications of OLPs, in terms of the enactment of opportunities and/or barriers, from three perspectives: access to resources, capabilities to function, and correspondences with flows. The latter perspective temporally reinterprets the former two and offers a processual flow-oriented approach to social justice. We further substantiate and showcase the added value of our approach through an empirical investigation of different gig work stories on the Amazon Mechanical Turk (MTurk) platform and discuss how our flow-oriented approach leads to revealing social justice implications not foregrounded through other approaches. Specifically, we develop three flow-oriented social justice tests that can be applied in various OLP contexts to assess their social justice implications. Finally, we derive remedial design and policy principles that can serve to reshape dialogue about social justice on OLPs, both theoretically and practically, in a manner that is more relevant and responsive to the fluid and evolving realities faced by gig workers.