Deblinder is a visual analytics system for exploring failure reports, descriptions from end-users of how or why a model failed. Deblinder consists of a word embedding visualization for making sense of the reports, and a sidebar for validating hypotheses of systematic failures. To learn more about Deblinder and failure reports, check out the full paper:

Discovering and Validating AI Errors With Crowdsourced Failure Reports
Ángel Alexander Cabrera, Abraham J. Druck, Jason I. Hong, Adam Perer
Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2

1. Install Python dependencies with pip install -r requirements.txt
2. Install JS dependencies and compile the frontend with cd client; yarn; yarn build
3. Run the server with python app.py

Use the following instructions to use a new dataset and model:

1. Follow preprocessing.ipynb to generate the embedding vectors for a new dataset.
2. Copy the metadata table with the vectors as data.csv in client/public/
3. Copy the static images to img/
4. Update the model calls in app.py to the new model.