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Accepted Paper
Paper short abstract
This paper ethnographically shows how Chinese food delivery platform workers manage unreliable and messy AI-enhanced dispatching outputs and keep platform logistics running through everyday collaborations underpinned by extra immaterial labour.
Paper long abstract
Mainstream research on location-based platform work often places AI-enhanced algorithmic control at the centre of analysis, assuming its efficiency and effectiveness and lacking attention to the everyday practices required to keep these systems running smoothly. In practice, however, platform workers always encounter systematic asymmetries in workload across time, space, and accounts, which are caused by opaque AI-driven allocation rules. The dispatching mechanism often causes location-based platform workers either to undertake unbearable juxtaposed workload or to experience irregular schedules with unwanted idle time, making the labour process full of frictions. This paper empirically investigates the labour process of food-delivery riders on Meituan, China’s largest food-delivery platform, in Shenzhen. Drawing on an 11-month hybrid ethnography, including four months of participant observation as a rider on my own and 65 in-depth interviews, I explore situated collective practices rather than isolated individual ones that compensate where AI-enhanced technologies remain dysfunctional. My paper shows how workers make sense of such an unpredictable, unstable and uneven dispatching mechanism and keep it running in everyday logistics: by collaboratively re-circulating orders within groups accompanied by timely online communication and manual reallocation of human intermediaries on the backend. It further shows that constant yet hidden interpersonal negotiations on the ground, sustained by workers’ emotional and relational labour, play an indispensable role in making the high-speed delivery possible. The paper reveals the limits of AI-enabled automation and suggests that techno-economic systems operate by embedding in and entangling with sociocultural logics rather than being vacuously omnipotent.
Outlasting 'disruption': Empirical perspectives on practical reasoning with AI
Session 2