Ryan Steed, Aylin Caliskan

Read the paper here. View code and data here.

.. info:: iEAT @ FAccT 2021 - Slides - Paper

# gather the dependencies for running scripts in this repo
conda env update environment.yml
conda activate ieat
# install the weat package locally
pip install -e weat
# install the ieat package locally
pip install -e .

The ieat package does not have a CLI. Use it programmatically by accessing the API module (ieat.api).

To run a basic test on a set of images, use the test function in ieat.api. SimCLR is downloaded automatically - but you must download a pre-trained version of iGPT yourself.

For an example of how to use the API programmatically, see the documentation and tutorials.

This repo uses Colab scripts in the notebooks/ directory. Check out notebooks/README.md for a full description.

To open a .ipynb file in Colab, navigate to Colab's Github Interface and search for this repo.

Documentation for the ieat API is published at rbsteed.com/ieat.

To generate the documentation, use pdoc3:

pdoc3 --html --output-dir docs --force ieat --template-dir docs/templates
git subtree push --prefix docs/ieat origin gh-pages

• data/ - images and other data used for bias tests in the paper
• embeddings/ - location for caching computed embeddings - includes pre-computed embeddings for convenience; to generate your own, use the from_cache=False option
• ieat/ - software package for generating image embeddings and testing for bias
• notebooks/ - Colab notebooks containing tutorials and data exploration
• output/ - location for storing results tables
• environment.yml - Conda environment file with dependencies for Jupyter, etc.
• docs/ieat - source for documentation