Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models
arXiv:2503.09805 · doi:10.1145/3715275.3732061
Abstract
Queer people are often discussed as targets of bias, harm, or discrimination in research on generative AI. 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. We found our participants struggled to use these models due to various normative values embedded in their designs, such as hyper-positivity and anti-sexuality. We describe various strategies our participants developed to overcome these models' limitations and how, nevertheless, our participants found value in these highly-normative technologies. Drawing on queer feminist theory, we discuss implications for the conceptualization of "state-of-the-art" models and consider how FAccT researchers might support queer alternatives.
References in corpus (8)
- Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
- Evaluating the Experience of LGBTQ+ People Using Large Language Model Based Chatbots for Mental Health Support
- Queer In AI: A Case Study in Community-Led Participatory AI
- Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCI
- Copyright Protection and Accountability of Generative AI:Attack, Watermarking and Attribution
- AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks
- Machine Learning Processes as Sources of Ambiguity: Insights from AI Art
- 'Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion