Journal article
Refusal as silence: Gendered disparities in Vision-Language Model responses
New media & society
05/04/2026
DOI: 10.1177/14614448261441886
Abstract
Refusal behavior by Large Language Models (LLMs) is increasingly visible in content moderation, yet little is known about how refusals vary by the identity of the user making the request. This study investigates refusal as a sociotechnical outcome through a counterfactual persona design. Focusing on a Vision-Language Model (GPT-4V), we examine how gendered persona in prompts influence refusal in binary gender classification tasks. We vary gender identity across male, female, non-binary, and transgender personas while keeping the classification task and visual input constant. We find that transgender and non-binary personas experience significantly higher refusal rates, even in non-harmful contexts. Our findings also provide methodological implications for equity audits using LLMs. We underscore the importance of modeling identity-driven disparities and caution against uncritical use of artificial intelligence systems for content coding. This study advances algorithmic fairness by reframing refusal as a communicative act that may unevenly regulate epistemic access and participation.
Details
- Title: Subtitle
- Refusal as silence: Gendered disparities in Vision-Language Model responses
- Creators
- Sha Luo - University of Wisconsin–MadisonSang Jung Kim - University of IowaZening Duan - National University of SingaporeKaiping Chen - University of Wisconsin–Madison
- Resource Type
- Journal article
- Publication Details
- New media & society
- DOI
- 10.1177/14614448261441886
- ISSN
- 1461-4448
- eISSN
- 1461-7315
- Publisher
- Sage
- Number of pages
- 22
- Language
- English
- Electronic publication date
- 05/04/2026
- Academic Unit
- Center for Social Science Innovation; School of Journalism and Mass Communication
- Record Identifier
- 9985161340302771
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