Would the Trees Dim the Lights? Adopting the Intentional Stance for More-Than-Human Participatory Design
March 27, 2023 Β· Declared Dead Β· π Participatory Design Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ned Cooper
arXiv ID
2303.14914
Category
cs.HC: Human-Computer Interaction
Citations
6
Venue
Participatory Design Conference
Last Checked
4 months ago
Abstract
The 2019/20 Black Summer bushfires in Australia demonstrated the brutal and disastrous consequences of changing the technological world without considering linkages with the biophysical, ecological or human worlds. An emerging more-than-human design philosophy encourages designers to consider such interrelations between humans and non-human entities. Yet, the design research community has focused on situated or embodied experiences for designers, rather than developing processes to legitimate the perspectives of non-human entities through participatory design. This paper explores how adopting the `intentional stance', a concept from philosophy, might provide a heuristic for more-than-human participatory design. Through experimentation with the intentional stance in the context of smart lighting systems, the paper demonstrates that the approach has potential for non-human entities from the ecological world, but less so for the biophysical world. The paper concludes by encouraging critique and evolution of the intentional stance, and of other approaches, to legitimate the perspectives of non-human entities in everyday design.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted