Platform Precarity: Surviving Algorithmic Insecurity in the Gig Economy

Digitalization and the use of algorithms have raised concerns regarding the future of work; the gig economy being identified by some as particularly concerning. Yet academic research is inconsistent as to whether this sector constitutes precarious work. We attempt to reconcile contrasting existing accounts by developing a new model for gig economy precarity. In doing so we draw on 81 interviews in addition to participant observations to highlight the role of platform reputation in shaping experiences of traditional socio-economic insecurity. We also demonstrate that gig economy platforms produce a novel form of insecurity, which we term, ‘algorithmic insecurity’. This relates to the vulnerability and fear that workers experience as a result of working in an unstable and opaque environment in which platforms use customer-generated ratings to score workers, and algorithms to amplify the consequences of those scores. We also detail how workers respond to this capricious environment through unpaid labor, digital communities, and individual resistance. The aim of this qualitative research is to generate a model that can be tested quantitatively, as a first step towards this aim we draw on European survey data to provide tentative support for the existence of algorithmic insecurity beyond our interview participants.