Summary of Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-language Ai, by Robert Wolfe et al.
Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI
by Robert Wolfe, Aayushi Dangol, Alexis Hiniker, Bill Howe
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 paper investigates 43 multimodal AI models, specifically the CLIP vision-language models, to determine whether they learn human-like biases in facial impressions. The study finds that these biases are reflected across three distinct CLIP model families and that the degree of bias shared across a society predicts the extent it is reflected in the models. Moreover, the paper shows that larger datasets lead to more subtle social biases being reproduced, while smaller datasets result in less nuanced representations. Additionally, the research demonstrates that Stable Diffusion models employing CLIP as a text encoder learn facial impression biases and that these biases intersect with racial biases in certain cases. The study highlights the importance of dataset curation when using CLIP models for general-purpose applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well AI models can recognize human-like biases in pictures and words. It tests 43 different AI models, called CLIP models, to see if they learn these biases from the data they’re trained on. The study finds that many of these models do pick up on these biases, especially when using large datasets. This is concerning because it means that AI systems may be perpetuating our own biases and stereotypes. The research also shows that smaller datasets can lead to less nuanced representations, which could have negative consequences. Overall, the study emphasizes the need for careful curation of data when creating AI models that can learn from and replicate human behavior. |
Keywords
* Artificial intelligence * Diffusion * Encoder
