Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models
This work addresses the problem of normative biases in generative AI for queer artists, offering insights into their experiences and potential support mechanisms, though it is incremental in scope.
The study explored how queer artists engage with generative AI models like GPT-4 and DALL-E 3, finding they struggled due to embedded normative values such as hyper-positivity and anti-sexuality, but developed strategies to overcome limitations and still found value in the technology.
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.