Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models

TL;DR

This study investigates how queer artists encounter generative AI models (GPT-4 and DALL-E 3), revealing normative safety and aesthetic constraints that constrain queer representation. Through a five-week workshop with 13 queer artists and analysis grounded in Sara Ahmed's concepts of straightening and queer use, the authors document practical strategies artists adopt to break or work with these straight models. Qualitative findings show that safety moderation, social and stylistic biases shape outputs, while a quantitative analysis of 2,092 prompts corroborates moderation trends and bias signals. The work argues for plural, community-centered GenAI design, consent-based moderation, and tools to protect artists' rights, reframing state-of-the-art models as potentially misaligned with queer futures and informing FAccT discussions on safety and design.

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.