SFB 1187 ›Medien der Kooperation‹ an der Universität Siegen
Lecture Series “Interrogating Data Practices” – Hannah Knox (University College London): “Practice of Futuring: Reckoning with Climate Data”
Wednesday, 03 February 2021, 06:00-07:00 pm
The lecture series will be held digitally until further notice. A Zoom-link for the lecture will be made available in advance via the SFB mailing list. Guests are welcome to register via Mail with Damaris Lehmann Send an email
 

In this talk I turn attention to the 'data practice' of predictive analytics, to explore how predictive climate models work to demarcate futures and with what effects. A crucial aspect of the allure of contemporary data is its capacity to create a picture of the future – whether of the economy, consumption behaviour or the climate. But the future that modelled predictions generate are not straightforward. Located in computational networks, such futures are not simply the plans, imaginaries or indeed practices of ideologues or engineers but the effects of an autopoetic unfolding from contingent material inputs that render traces of the present into plausible stories of what might happen. These futures are neither fictions nor realities, but sit somewhere between the two, describing what is to come whilst also undoing themselves in their injunction to change the present with a view to altering the trajectories that they imagine. 
 
While anthropologists have developed a sophisticated vocabulary for talking about the past (tradition, genealogy, inheritance, myth, totem) and the present (culture, relationality, kinship, exchange), we have a less developed set of conceptual resources for understanding the futures of predictive analytics, or participating in the reimagination of their form. Our current methods (oral history, archival research, ethnography, practice-focused research) are arguably ill-equipped to address the implications of futurities produced by computational models. How then might we gain a better handle on the futures that predictive analytics are generating? And how might this help us, as critical scholars, to participate more effectively in redirecting the now often apocalyptic trajectories revealed by data science?