Abstract
The Sensing in the Wild Lab is a speculative experiment in designing a de- centralised urban sensing system from a more-than-human perspective. It is part of DCODE, an H2020-ITN project that explores the future of designing with AI. During the Lab participants assume different identities – roleplaying as children but also as moss, as municipal authorities, as CCTV cameras, as pigeons, and as undocumented immigrants trying to evade the authorities – and are asked to feed into the sensing system data that reflects their particular perspectives and interests. The data partici- pants share, in the form of an image and text uploaded to a dedicated WhatsApp channel, helps to reveal both frictions and alignments among actors. In this, the Lab offers municipalities an opportunity to shift their thinking about the future smart city from a “system of systems” that is optimised for a few city dwellers to a much more distributed, inclusive meshwork in which data is contributed, circulated, and negoti- ated by humans and nonhumans alike.
Keywords
smart city, speculative design, more than human, creative methods
DOI
https://doi.org/10.21606/drs.2022.912
Citation
Turtle, G.L., Guerrero Millan, C., Özçetin, S., Patil, M., and Bendor, R. (2022) Sensing in the wild: A DCODE DRS Lab exploring a more-than-human approach to distributed urban sensing, in Lockton, D., Lloyd, P., Lenzi, S. (eds.), DRS2022: Bilbao, 25 June - 3 July, Bilbao, Spain. https://doi.org/10.21606/drs.2022.912
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
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Sensing in the wild: A DCODE DRS Lab exploring a more-than-human approach to distributed urban sensing
The Sensing in the Wild Lab is a speculative experiment in designing a de- centralised urban sensing system from a more-than-human perspective. It is part of DCODE, an H2020-ITN project that explores the future of designing with AI. During the Lab participants assume different identities – roleplaying as children but also as moss, as municipal authorities, as CCTV cameras, as pigeons, and as undocumented immigrants trying to evade the authorities – and are asked to feed into the sensing system data that reflects their particular perspectives and interests. The data partici- pants share, in the form of an image and text uploaded to a dedicated WhatsApp channel, helps to reveal both frictions and alignments among actors. In this, the Lab offers municipalities an opportunity to shift their thinking about the future smart city from a “system of systems” that is optimised for a few city dwellers to a much more distributed, inclusive meshwork in which data is contributed, circulated, and negoti- ated by humans and nonhumans alike.