Year: 2025
Type: conference-paper
Abstract: GenderCrusher is a hybrid interactive experience that queers facial recognition technologies through playful critique. Drawing on queer theory, it challenges machine learning's reliance on fixed identity categories, particularly in gender classification. By inviting players to "assist" an AI in diversifying gender recognition, the experience exposes the limitations and biases of facial recognition systems and questions whether inclusivity can be achieved through data expansion alone. The GenderCrusher eventually breaks down or can be aborted, symbolising refusal to comply with restrictive norms. Built using Twine, Arduino, and image generative AI tools, GenderCrusher operationalizes queer critique into a tangible, engaging format that opens discourse to wider audiences. It demonstrates how playful, critical design can destabilise binary structures, resist normativity, and illuminate the socio-technical complexities of identity in and with AI. Ultimately, it offers space to reimagine AI through queer, non-normative lenses beyond representational fixes.
Authors: Lina Eklund, Jon Back
Institution: Uppsala University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: gold
Funder: Vetenskapsrådet
Award: G7780190055
OpenAlex ID: W4413844230