Intersectional Fairness (ISF)

Description

Intersectional Fairness (ISF) is a bias detection and mitigation technology for intersectional bias, which combinations of multiple protected attributes cause.
ISF leverages the existing single-attribute bias mitigation methods to make a machine-learning model fair regarding intersectional bias.
Approaches applicable to ISF are pre-, in-, and post-processing. For now, ISF supports Adversarial Debiasing, Equalized Odds, Massaging, and Reject Option Classification.

Supported Python Configurations:

Item	Version
Python	3.7 - 3.11

Setup

The ISF setup will install resources and patch AIF360.

Install with pip

pip install git+https://github.com/intersectional-fairness/isf.git

Patch AIF360 for Intersectional Fairness

Apply a patch to AIF360 to work with ISF.

The patch contents are as follows.

file	method/class	fixes
datasets/structured_dataset.py	validate_dataset	* Changed the generating condition of 'Value Error' condition to support multiple protected attributes
algorithms/postprocessing/ reject_option_classification.py	RejectOptionClassification	* Added "F1 difference" to corresponding metric * Defined "Balanced Accuracy" as default value for accuracy_metric_name

To apply the patches, run the following command:

apply-patch-to-aif360-for-isf

The above command equivalents to the following command. So you can apply the patches with the following command instead of the above:

patch {aif360 installed directory path}/datasets/structured_dataset.py structured_dataset.patch
patch {aif360 installed directory path}/algorithms/postprocessing/reject_option_classification.py reject_option_classification.patch

Run the Examples

The examples directory contains a diverse collection of jupyter notebooks that use Intersectional Fairness in various ways.

If you use open data supported by AIF360, you need to download the datasets and place them in their respective directories as described in aif360/data/README.md in AIF360.

Citing Intersectional Fairness

A technical description of Intersectional Fairness is available in this paper (or this preliminary version).
The followings are the bibtex entries for these papers.

@InProceedings{Kobayashi2021-tf,
      title={{One-vs.-One} Mitigation of Intersectional Bias: A General Method for Extending {Fairness-Aware} Binary Classification},
      booktitle={New Trends in Disruptive Technologies, Tech Ethics andArtificial Intelligence},
      author={Kenji Kobayashi and Yuri Nakao},
      publisher={Springer International Publishing},
      pages={43--54},
      year={2021},
      conference={DiTTEt 2021}
}
@misc{kobayashi2020onevsone,
      title={One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification},
      author={Kenji Kobayashi and Yuri Nakao},
      year={2020},
      eprint={2010.13494},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
      url = {https://arxiv.org/abs/2010.13494}
}

Support

If you have any questions or problems, please contact us.

License

Intersectional Fairness and the OSS licenses it uses is here.