ImageNet Roulette
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About
This tool, ImageNet Roulette, is an experimental project developed by Trevor Paglen's studio that uses an AI model trained exclusively on the problematic "Person" categories of the ImageNet dataset. Its purpose is to critically expose the inherent biases, political assumptions, and ethical issues embedded within large-scale machine learning training sets, particularly those used for object and facial recognition. It highlights how categories like "Bad Person," "Failure," or derogatory terms were used to label real people's images without consent. Users upload a picture, and the tool returns the image with the AI's classification, often revealing offensive or nonsensical labels. This experiment serves as a powerful demonstration of the "treachery of images" and the social consequences of uncritical data collection and classification in AI, advocating for greater transparency and public discourse on these systems.
Platform
Features
• designed to shed light on problematic training data and classifications
• does not store uploaded photos
• if no faces detected, sends entire scene to caffe model and returns a label
• returns original images with a bounding box showing the detected face and assigned label
• sends detected faces to the caffe model for classification
• runs a face detector to locate any faces
• trained on imagenet's "person" categories (excluding proper nouns)
• uses an open-source caffe deep-learning framework
Pricing Plans
Free
Free Plan• Demonstrates AI classification biases
• Does not store uploaded photos
• Uses ImageNet 'Person' categories for classification
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