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

2025-03-12

AI Summary

This study critically examines how normative values embedded in generative AI systems—such as technological optimism and hetero-ciscentrism—in GPT-4 and DALL-E 3 structurally constrain queer artistic practice. Employing participatory workshops, collective meaning-making, and qualitative thematic analysis, it pioneers the systematic integration of queer feminist theory into AI critical studies through the lens of queer artists’ lived practices. The research identifies three categories of institutional normative biases and distills five forms of resistant AI usage strategies. Building on these findings, it reconceptualizes evaluation criteria for “state-of-the-art” models and advocates non-normative AI alternatives. Extending the FAccT (Fairness, Accountability, Transparency) framework, the work proposes a new direction for AI design that centers queer subjectivity—shifting AI ethics from representational inclusion toward structural intervention in power relations.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Explores how queer artists interact with generative AI models.
• Identifies normative biases in AI like hyper-positivity and anti-sexuality.
• Proposes strategies for queer engagement with normative AI technologies.

Innovation

• Workshop with queer artists using GPT-4 and DALL-E 3
• Identified normative biases in generative AI models
• Developed strategies to overcome model limitations