Welcome to fairret’s documentation!¶
The goal of fairret is to serve as an open-source Python library for measuring and mitigating statistical fairness in PyTorch models. The library is designed to be
flexible in how fairness is defined and pursued.
easy to integrate into existing PyTorch pipelines.
clear in what its tools can and cannot do.
The central to the library is the paradigm of the fairness regularization term (fairrets) that quantify unfairness as differentiable PyTorch loss functions. These can then be optimized together with e.g. the binary cross-entropy error such that the classifier improves both its accuracy and fairness.
Note
This project is under active development!
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Citation¶
If you have found fairret useful in your research, please cite our ICLR 2024 paper:
@inproceedings{buyl2024fairret,
title={fairret: a Framework for Differentiable Fairness Regularization Terms},
author={Buyl, Maarten and Defrance, Marybeth and De Bie, Tijl},
booktitle={International Conference on Learning Representations},
year={2024}}