Summary of Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems, by Michelle R. Greene et al.
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems
by Michelle R. Greene, Mariam Josyula, Wentao Si, Jennifer A. Hart
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the socioeconomic biases present in deep convolutional neural networks (dCNNs) used for scene classification. The researchers analyzed nearly one million images from global and US sources, including user-submitted home photographs and Airbnb listings, to quantify the impact of socioeconomic indicators such as family income, Human Development Index (HDI), and demographic factors on dCNN performance. The results showed significant biases, where pretrained dCNNs demonstrated lower classification accuracy, confidence, and a higher tendency to assign offensive labels when applied to homes with lower socioeconomic status (SES). This trend was consistent across two international datasets and within the diverse economic and racial landscapes of the United States. By addressing these biases in computer vision pipelines, the study emphasizes the need for more inclusive and representative training datasets to ensure fairer and more equitable outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well computers can understand scenes and pictures from different parts of the world. They wanted to know if the computers are biased against certain neighborhoods or homes based on how rich or poor they are. The researchers used a huge collection of photos from around the world, including some from Airbnb listings, to test this idea. They found that the computers did have biases – they were less accurate when looking at pictures from poorer areas and tended to label those homes in negative ways. This is important because these computer programs can be used to make decisions about things like home values or smart home security systems. By making sure these computers are fair and unbiased, we can help ensure that technology benefits everyone equally. |
Keywords
» Artificial intelligence » Classification
