Countering the digital growth machine with Slow AI
Topics: Urban Geography
, Digital Geographies
, Political Geography
Keywords: Degrowth, counter-AI, digital growth machine, innovation
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 09:40 AM (Eastern Time (US & Canada)) - 3/1/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 62
Authors:
Jeremy Crampton, Newcastle University
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Abstract
In this presentation I examine how digital spaces form “privately owned public spaces” (POPS) that have the appearance of being commons-like, but are owned, operated, and largely governed by private interest and which depend on public infrastructures. Digital POPS most clearly include platforms such as Uber or Facebook. However, here I wish to invoke other forms that monetize or create value from physical spaces; specifically, geofences and digital twins. These two forms of value (or asset) creation operate at different levels of technology and for very different purposes, yet both illustrate the flexibility of the digital growth machine described by Rosen and Alvarez-León (xxxx). Both innovations not only create and extract value, but also destroy it, thereby creating loss of trust and public alienation as geoprivacy is left unprotected, and corporate data models are black-boxed. These very forms may provide openings for alternatives not predicated around growth. I examine the possibilities of true innovation, one that questions, and which incumbent forms of technology cannot predict because it is uncorrelated with those forms. Slow AI is transdisciplinary, local, and temporary. It is bottom-up and co-designed with citizens, not data subjects. As a “counter-AI” it is situated in a long tradition of rerouting circuits of power.
Yet the question lingers: what points of leverage exist for degrowth innovations: privacy, trust, transparency, consent, regulation, or critique (of capitalism, ecology)?
Countering the digital growth machine with Slow AI
Category
Virtual Paper Abstract
Description
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