Big Data Time Machines: Decolonizing the Futures of Post-Digital Histories

Abstract

This paper offers a critical analysis of “Big Data Time Machines,” platforms built on AI and Big Data that use metaphors of time travel in relation to large historical datasets and digital archives. Drawing on decolonial perspectives that aim to unsettle western power structures around Big Tech, the paper focuses on the EU-funded large-scale research initiative (LSRI) called “Time Machine Europe,” a large collaborative international alliance devoted to using machine learning to extract the “Big Data of the Past for the future of Europe.” Through a material-semiotic analysis of the discourses and design strategies that structure archival encounters with select Local Time Machine projects, the paper identifies how “archive aesthetics” are used to organize historical journeys that reinforce long-standing white settler positions, but can also be used to creatively challenge these knowledge monopolies. The paper concludes by turning to speculative fiction about time travel by Black, Latinx, Caribbean, and Indigenous storytellers whose work might help internet scholars, artists, archivists and historians to work together in rupturing the western temporal imagination and imagining alternative and more just data histories and futures.