Welcome to error-parity’s documentation!

The error-parity package allows you to easily achieve error-rate fairness between societal groups. It’s compatible with any score-based predictor, and can map out all of its attainable fairness-accuracy trade-offs.

Full code available on the GitHub repository, including various jupyter notebook examples .

Check out the following sub-pages:

• Readme file
• API reference
• Example notebooks

Citing

The error-parity package is the basis for the following publication:

@inproceedings{
cruz2024unprocessing,
title={Unprocessing Seven Years of Algorithmic Fairness},
author={Andr{\'e} Cruz and Moritz Hardt},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=jr03SfWsBS}
}

All additional supplementary materials are available on the supp-materials branch of the GitHub repository.

Indices

• Index

• Module Index