Summary of Lookism: the Overlooked Bias in Computer Vision, by Aditya Gulati and Bruno Lepri and Nuria Oliver
Lookism: The overlooked bias in computer vision
by Aditya Gulati, Bruno Lepri, Nuria Oliver
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 A recent surge in computer vision advancements has led to widespread deployment of image recognition and generation systems in socially relevant applications. However, biases within these systems have raised significant ethical concerns. While gender, race, and age biases are well-studied, lookism – preferential treatment based on physical appearance – remains under-explored, perpetuating harmful societal stereotypes and undermining AI fairness and inclusivity. This paper advocates for the systematic study of lookism as a critical bias in computer vision models, reviewing existing literature, illustrating findings with examples and user studies, and calling for an interdisciplinary approach to develop equitable systems that respect human diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Computer vision has made huge progress, but it’s not perfect. There are biases in these systems that can be really harmful. People worry about things like gender, race, and age, but there’s another problem – lookism. That’s when people prefer certain looks or physical appearances over others. This can make AI systems unfair and even promote bad stereotypes. The paper thinks we should study this bias more and find ways to fix it. It looks at what other researchers have said about the issue, shows some examples, and asks for help from experts in many fields. |
