Collective machine teaching: rehearsing non-extractivist situated approaches to AI

Amid overwhelming evidence from the social sciences about the inherently extractive nature of predominant machine learning imaginaries, and visceral designerly and artistic explorations of how algorithmic governance of urban resources consumes our labour and bodies, calls to put people back into AI are growing. This paper describes a participatory experimental design project that adopts a critical making approach to rehearse collective machine teaching to distribute rescued food. It argues that collective training data generation is key to translating the affective and convivial aspects of the community’s deliberations into the algorithmic c of the distribution infrastructure. By creating a blueprint for community-based machine teaching while shedding light on the data-labour-related costs of optimisation and the limitations of scaling, it opens pathways to counter extractivist machine learning paradigms with just community- and commoning-based alternatives.