aiqbench analysis
This study investigates how Large Language Models systematically marginalize queer identities by generating narrow, stereotyped narratives rather than reflecting the full complexity of human experience. The research identifies specific failure modes—including harmful representations and discursive othering—that limit the utility of current AI systems for inclusive discourse. These findings are critical for developers aiming to align model outputs with diverse social realities and mitigate bias in generative applications.
Unequal Voices: How LLMs Construct Constrained Queer Narratives
arXiv:2507.15585v2 Announce Type: replace-cross Abstract: One way social groups are marginalized in discourse is that the narratives told about them often default to a narrow, stereotyped range of topics. In contrast, default groups are allowed the full complexity of human existence. We describe the constrained representations of queer people in LLM generations in terms of harmful representations, narrow representations, and discursive othering and formulate hypotheses to test for these phenomena. Our results show that LLMs are significantly limited in their portrayals of queer personas.
Abstract:One way social groups are marginalized in discourse is that the narratives told about them often default to a narrow, stereotyped range of topics. In contrast, default groups are allowed the full complexity of human existence. We describe the constrained representations of queer people in LLM generations in terms of harmful representations, narrow representations, and discursive othering and formulate hypotheses to test for these phenomena. Our results show that LLMs are significantly limited in their portrayals of queer personas.
Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
arXiv:2507.15585 [cs.CY]
(or arXiv:2507.15585v2 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2507.15585 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 175 words
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