ImageNet Roulette
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About
ImageNet Roulette is an open-source application developed by Leif Ryge at Paglen Studio as an experiment stemming from the "Excavating AI" project by Kate Crawford and Trevor Paglen. It utilizes a Caffe deep-learning framework trained on the "person" categories of the controversial ImageNet dataset. Users upload photos, and the application first detects faces. If faces are found, they are sent to the Caffe model for classification. If no faces are detected, the entire image is sent. The tool then returns the original image with a bounding box around detected faces and the assigned label. Its primary design goal is to highlight the problematic, offensive, and often bizarre classifications present within the ImageNet dataset, particularly concerning human categorization. It aims to make visible the often-hidden process of AI classification of people and demonstrate how technical systems can go wrong when trained with biased data. The application explicitly states that it does not store uploaded photos, ensuring user privacy for its users while still serving its critical purpose.
Platform
Features
• ensures privacy by not storing uploaded photos
• exposes biases in ai classification systems
• displays classification results with bounding boxes
• applies labels based on imagenet 'person' categories
• detects faces within uploaded images
• classifies images uploaded by users
FAQs
Does ImageNet Roulette store uploaded photos?
No, ImageNet Roulette does not store the photos people upload, ensuring user privacy for the experiment.
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