Abstract
This paper contributes to scholarly discourse on design and AI by using queerness as a theoretical grounding to explore potentialities for design to interface with and imagine artificial intelligence (AI) differently. The paper does so by reporting on an autotheoretical experiment in which I pose the questions: What if we understood AI as queer, a kind of mutant, in a state of becoming; a dynamic, relational, non-binary gender variant? How then might AI show up in and act on the world (with us humans) differently? The experiment uses a Generative Adversarial Network (GAN) to unsettle how AI is understood today, and to allow for new AI propositions to take root. The work provides a glimpse into forms of design refusal that might illuminate designers to cultural computability and self-determination when designing with AI systems.
Keywords
queerness, co-performativity, artificial intelligence, autotheory
DOI
https://doi.org/10.21606/drs.2022.782
Citation
Turtle, G.L. (2022) Mutant in the mirror: Queer becomings with AI, in Lockton, D., Lenzi, S., Hekkert, P., Oak, A., Sádaba, J., Lloyd, P. (eds.), DRS2022: Bilbao, 25 June - 3 July, Bilbao, Spain. https://doi.org/10.21606/drs.2022.782
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Research Paper
Included in
Mutant in the mirror: Queer becomings with AI
This paper contributes to scholarly discourse on design and AI by using queerness as a theoretical grounding to explore potentialities for design to interface with and imagine artificial intelligence (AI) differently. The paper does so by reporting on an autotheoretical experiment in which I pose the questions: What if we understood AI as queer, a kind of mutant, in a state of becoming; a dynamic, relational, non-binary gender variant? How then might AI show up in and act on the world (with us humans) differently? The experiment uses a Generative Adversarial Network (GAN) to unsettle how AI is understood today, and to allow for new AI propositions to take root. The work provides a glimpse into forms of design refusal that might illuminate designers to cultural computability and self-determination when designing with AI systems.