Summary of A Sociotechnical Lens For Evaluating Computer Vision Models: a Case Study on Detecting and Reasoning About Gender and Emotion, by Sha Luo et al.
A Sociotechnical Lens for Evaluating Computer Vision Models: A Case Study on Detecting and Reasoning about Gender and Emotion
by Sha Luo, Sang Jung Kim, Zening Duan, Kaiping Chen
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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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 The paper investigates the limitations of traditional evaluation metrics in computer vision (CV) models for detecting and interpreting gender and emotion in images. The authors propose a sociotechnical framework that incorporates both technical performance measures and considerations of social fairness. The study compares the performance of various CV models, including DeepFace, FER, and GPT-4 Vision, using a dataset of 5,570 images related to vaccination and climate change. While GPT-4 Vision outperforms other models in gender classification, it exhibits discriminatory biases towards transgender and non-binary personas. The authors emphasize the necessity of developing comprehensive evaluation criteria that address both validity and discriminatory biases in CV models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computer programs can understand what’s happening in pictures, like if someone is happy or sad. But right now, these programs are not very good at understanding things like gender or emotions because they’re based on old ideas. The researchers want to change this by creating a new way of testing these programs that looks at both how well they work and how fair they are. They used a big group of pictures to test some different kinds of computer programs, including ones that can recognize faces and ones that can generate images. One of the programs was really good at recognizing gender, but it was also biased against people who don’t fit traditional ideas of male or female. The researchers think this is important because these programs could be used in ways that are not fair or helpful. |
Keywords
» Artificial intelligence » Classification » Gpt
