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Excavating AI / ImageNet Roulette
Expose the hidden politics and biases of machine learning training sets by visualizing how AI models categorize humans using problematic ImageNet labels.
No paid plan
Free
About Excavating AI / ImageNet Roulette
Excavating AI is an investigative project and digital tool designed to uncover the social and political implications of image-based machine learning training sets. Developed by Kate Crawford and Trevor Paglen, the platform centers on the "archeology of datasets," specifically scrutinizing ImageNet, one of the most influential benchmarks in artificial intelligence. The project includes an experimental software tool called ImageNet Roulette, which allows users to see how they would be classified by a deep-learning model trained exclusively on ImageNet's "Person" categories. By making these hidden classifications visible, the tool illustrates the shaky and often offensive assumptions that underpin how AI systems recognize and interpret human identity. Technically, the ImageNet Roulette tool utilizes an open-source Caffe deep-learning framework. When a user uploads a photograph, the application runs a face detector to locate subjects and then assigns them a label based on the thousands of categories found in the original ImageNet "Person" hierarchy. These categories include labels ranging from professions like "welders" to derogatory terms and nonsensical classifications such as "snob," "failure," or "closet queen." This process highlights the problematic nature of "supervised learning," where human laborers—often Amazon Mechanical Turk workers—are tasked with flattening complex human experiences into static, often biased, noun-based categories. This classification process is revealed to be a subjective and often ideological act rather than a purely scientific one. The project is primarily intended for researchers, AI ethicists, artists, and students of media theory who seek to understand the non-neutral nature of data. It serves as a powerful case study for those investigating the resurgence of "physiognomic" AI—the pseudoscientific attempt to derive character or morality from facial features. Unlike standard AI diagnostic tools that aim to "fix" bias through more data, Excavating AI argues that the very act of classification is a political intervention. It provides a rare forensic look at the historical and sociological layers of datasets that have already been integrated into critical social infrastructures like hiring, education, and law enforcement protocols. What makes Excavating AI unique is its critical, investigative approach to the "black box" of training data. Rather than offering a commercial service, it acts as a provocateur, revealing the "treachery of images" where labels and referents are arbitrarily linked. By highlighting datasets like ImageNet, JAFFE, and IBM’s Diversity in Faces, the project demonstrates how technical systems can replicate 19th-century phrenology and race science. It stands out as an essential resource for anyone questioning the power dynamics of who gets to decide what an image means and how those representations affect real-world rights, liberties, and forms of self-determination.
Excavating AI / ImageNet Roulette pros & cons
Pros
- Provides a rare forensic look into the hidden labels used to train major AI systems.
- Uses open-source frameworks for transparency in its experimental results.
- Highlights critical ethical issues like phrenology and race science in modern AI.
- Does not store user-uploaded images, respecting immediate privacy during the experiment.
- Documented by world-class researchers from the AI Now Institute at NYU.
Cons
- The 'Person' categories in the original dataset are currently down for maintenance or removed from public servers.
- Produces offensive and derogatory labels by design to illustrate dataset bias, which may be distressing.
- Limited to noun-based classifications derived from the historical WordNet hierarchy.
- Functionality may be limited as several major source datasets have been taken offline by their respective institutions.
Excavating AI / ImageNet Roulette use cases
- Ethical AI researchers can use the tool to demonstrate the real-world impact of biased training data to policymakers.
- Media theory students can perform an 'archeology of datasets' to trace how historical prejudices are replicated in machine vision.
- Artists and activists can visualize the 'physiognomic' assumptions of AI to create critical commentary on surveillance.
- Computer science educators can use the project as a case study to teach students about the non-neutrality of supervised learning.
Excavating AI / ImageNet Roulette features
- ethical ai research insights
- open-source software framework
- classification visualization
- imagenet 'person' category analysis
- historical dataset archeology
- deep-learning caffe framework
- automated image labeling
- face detection and bounding boxes
Excavating AI / ImageNet Roulette pricing
Is Excavating AI / ImageNet Roulette free? Yes, Excavating AI / ImageNet Roulette has a free plan.
Open Access
Free
- Image classification visualization
- Face detection output
- ImageNet 'Person' labels
- Archeological analysis findings
- Open-source Caffe framework usage
Excavating AI / ImageNet Roulette FAQs
What is the primary purpose of ImageNet Roulette?
It is an experimental tool created to show how technical systems are trained using problematic data. It classifies users based on the 'Person' category in ImageNet to reveal the biases inherent in such datasets.
Does the tool store the photos I upload?
No, the creators state that ImageNet Roulette does not store the photos people upload to the application. The tool is designed for research and demonstration purposes rather than data collection.
Why are some of the labels returned by the tool offensive or nonsensical?
The labels are drawn directly from the ImageNet 'Person' category, which contains many problematic, misogynistic, and racist terms assigned by crowdsourced workers. The tool's purpose is to make these hidden classifications visible.
What happened to the 'Person' category images on the ImageNet website?
In early 2019, approximately 1.2 million photos in the 'Person' category were taken down or moved 'under maintenance' following research into their ethical and privacy violations. The tool uses a backup of these categories.
How does the tool determine a label if no face is detected?
If the face detector fails to locate a face, the application sends the entire uploaded scene to the Caffe model. It then returns a label in the upper left corner of the processed image based on the model's classification of the background.
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