Summary of More Distinctively Black and Feminine Faces Lead to Increased Stereotyping in Vision-language Models, by Messi H.j. Lee et al.
More Distinctively Black and Feminine Faces Lead to Increased Stereotyping in Vision-Language Models
by Messi H.J. Lee, Jacob M. Montgomery, Calvin K. Lai
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates how Vision Language Models (VLMs) perpetuate homogeneity bias and trait associations with regards to race and gender. The research explores whether VLMs inherit biases from both text and vision modalities, making them more pervasive and difficult to mitigate. The authors find that when prompted to write stories based on images of human faces, GPT-4V describes subordinate racial and gender groups with greater homogeneity than dominant groups, relying on distinct stereotypes. Importantly, VLM stereotyping is driven by visual cues rather than group membership alone. The findings suggest that VLMs may associate subtle visual cues related to racial and gender groups with stereotypes in ways that could be challenging to mitigate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLMs are special types of computer models that can understand both text and images. They’re really good at recognizing what’s in a picture, like faces or objects. But researchers have found that VLMs might also learn biases from the pictures they see. This means they might describe certain groups of people, like women or Black people, in a way that is not fair or accurate. The study looked at how GPT-4V, a type of VLM, describes different groups of people when shown images of faces. They found that GPT-4V tends to describe certain groups as being more alike than others, and it uses stereotypes that are often positive but not always accurate. |
Keywords
» Artificial intelligence » Gpt
