Robots that Look Like Us - How Deceptive Design Can Shape the Value of Human Labor
Version
Published
Date Issued
2025-09
Author(s)
Bern University of Applied Sciences
Editor(s)
Type
Conference Paper
Language
English
Abstract
Recent developments in artificial intelligence have renewed in terest in humanoid robots, which are increasingly portrayed as potential substitutes for human labor-despite their clear technological immaturity for realworld applications. This contribution questions the symbolic de sign choice to endow robots with deceptively realistic human features and examines its impact on how we perceive human work. Building on broader sociopolitical debates around labor, power, and technological agency, I ar gue that designing robots to emulate human traits perpetuates a technocra tic vision of progress-one that prioritizes centralized control and efficiency over human empowerment and ultimately seeks to render human labor obsolete. In response to the pressing challenges facing today's industries, I advocate for a functional approach to technology design-one that centers on human-machine collaboration to foster more resilient, adaptable, and inclusive production systems.
Publisher DOI
Journal or Serie
Socal Design Network Conference 2025
Publisher URL
Related URL
Organization
Conference
Reassessing the Social – Understanding Transformation. Proceedings of the Social Design Network Conference 2025
Citation
Dégallier Rochat, S. (2025). Robots that look like us: How deceptive design can shape the value of human labor. In A. Unteidig, B. Herlo, & P. Pierri (Eds.), Reassessing the social – Understanding transformation: Proceedings of the Social Design Network Conference 2025 (pp. xx–xx). Lucerne University of Applied Sciences and Arts.
Publisher
Lucerne University of Applied Sciences and Arts
Submitter
Dégallier Rochat, Sarah
Citation apa
Dégallier Rochat, S. (2025). Robots that Look Like Us - How Deceptive Design Can Shape the Value of Human Labor. In A. Unteidig, B. Herlo, & P. Pierri (Eds.), Socal Design Network Conference 2025 (pp. 386–399). Lucerne University of Applied Sciences and Arts. https://doi.org/10.24451/arbor.13005
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