Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.08222 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915577883262976 |
|---|---|
| author | Luo, Sha Kim, Sang Jung Duan, Zening Chen, Kaiping |
| author_facet | Luo, Sha Kim, Sang Jung Duan, Zening Chen, Kaiping |
| contents | Refusal behavior by Large Language Models 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 that varies gender identity--including male, female, non-binary, and transgender personas--while keeping the classification task and visual input constant. Focusing on a vision-language model (GPT-4V), we examine how identity-based language cues influence refusal in binary gender classification tasks. 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 and content analysis using LLMs. Our findings underscore the importance of modeling identity-driven disparities and caution against uncritical use of AI systems for content coding. This study advances algorithmic fairness by reframing refusal as a communicative act that may unevenly regulate epistemic access and participation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Refusal as Silence: Gendered Disparities in Vision-Language Model Responses Luo, Sha Kim, Sang Jung Duan, Zening Chen, Kaiping Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Human-Computer Interaction Refusal behavior by Large Language Models 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 that varies gender identity--including male, female, non-binary, and transgender personas--while keeping the classification task and visual input constant. Focusing on a vision-language model (GPT-4V), we examine how identity-based language cues influence refusal in binary gender classification tasks. 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 and content analysis using LLMs. Our findings underscore the importance of modeling identity-driven disparities and caution against uncritical use of AI systems for content coding. This study advances algorithmic fairness by reframing refusal as a communicative act that may unevenly regulate epistemic access and participation. |
| title | Refusal as Silence: Gendered Disparities in Vision-Language Model Responses |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.08222 |