Un-Straightening Generative AI: How Queer Artists Surface and Challenge Model Normativity
Jordan Taylor, Joel Mire, Franchesca Spektor, Alicia DeVrio, Maarten Sap, Haiyi Zhu, Sarah Fox
Abstract
Queer people are often discussed as targets of bias, harm, or discrimination in generative AI research.However, the specific ways that queer people engage with generative AI, and thus possible uses that support queer people, have yet to be explored.We conducted a workshop study with 13 queer artists, during which we gave participants access to GPT-4 and DALL-E 3 and facilitated group sensemaking activities.Our participants struggled to use these models FAccT '25, June 23-26, 2025, Athens, Greece
Topics & Concepts
QueerGenerative grammarComputer scienceSurface (topology)Visual artsArtArtificial intelligenceSociologyMathematicsGeometryGender studiesInnovative Human-Technology InteractionEthics and Social Impacts of AIAesthetic Perception and Analysis