Algorithmic Paranoia: Gig Workers' Affective Experience of Abusive Algorithmic Management

Abstract

Amidst the rapid rise of gig economy platforms, gig workers increasingly report feelings of mistrust, anxiety, and profound fear under opaque and abusive algorithmic management. This article introduces the concept of ‘algorithmic paranoia’ to capture the negative affective experiences stemming from workers' perceptions of algorithmic management as non-transparent, arbitrary, and retaliatory. Drawing on the concept of organisational paranoia from sociology and organisation studies, we theorise how workers' adverse experiences breed mistrust and suspicion toward both human and nonhuman actors on the platform. This culminates in intense feelings of persecution and anticipations of harm, which workers strive to cope with through hypervigilance and self-protective actions aimed at pre-empting anticipated threats. Our study contributes to existing literature by emphasising the role of affect in workers' responses to algorithmic management, highlighting the self-reinforcing dynamics among perceptions of abusive management, negative affective experiences, and preventive, self-preserving actions. We base our findings on an abductive analysis of data from 53 in-depth interviews with creative freelancers on gig economy platforms and conversations from an online community forum.