Event
Weathering Big Data
The weather has long been regarded as a prophetic medium: clouds, winds, sunshine, and fog circulate as foreboding signs within religious ceremonies, agriculture, and warfare. This talk interrogates the way in which meteorological information is instrumentalized within big data predictive analytics. Drawing upon theories of climatic determinism within nineteenth century statistics, I track the way in which the weather is rendered predictive in two modes of contemporary algorithmic governance: predictive policing and social media mood analytics. In each case, the weather is imagined as an impartial and impressing force that simultaneously buttresses claims to computational objectivity and effaces the way in which prediction is sustained by the technical operation of racial difference.
Gary Kafer is a PhD Candidate at the University of Chicago in the Department of Cinema and Media Studies. His research interests include surveillance, media materialisms, biopolitics, and theories of race, gender, and sexuality. His research has appeared in numerous journals and books, including Surveillance & Society, qui parle, Contemporaneity, Jump Cut, Digital Culture & Society, and the edited volume From Self-Portrait to Selfie: Representing the Self in the Moving Image (Peter Lang, 2019). He is also the co-editor of a special issue of Surveillance & Society on “Queer Surveillance.”