This is a pytorch implementation of FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels. To do that, FLAC proposes a sampling strategy that highlights underrepresented samples in the dataset, and casts the problem of learning fair representations as a probability matching problem that leverages representations extracted by a bias-capturing classifier. It is theoretically shown that FLAC can indeed lead to fair representations, that are independent of the protected attributes.

python = 3.6 pytorch = 1.10.1

We also provide the .yml file of the environmnent we used (pytorch36_environment.yml). You can create an identical environment by running

conda env create -f pytorch36_environment.yml

1.Download and extract UTKFace, CelebA, Imagenet, and Imagenet-A to a data folder.

2.Download the provided bias-capturing classifiers and put them under the bias_capturing_classifiers folder.

3.For Biased-MNIST run:

python train_biased_mnist.py --corr 0.99 --alpha 110
python train_biased_mnist.py --corr 0.995 --alpha 1500
python train_biased_mnist.py --corr 0.997 --alpha 2000
python train_biased_mnist.py --corr 0.999 --alpha 10000

4.For UTKFace run:

python train_utk_face.py --task race
python train_utk_face.py --task age

5.For CelebA run:

python train_celeba.py --task blonde --alpha 30000
python train_celeba.py --task makeup --alpha 20000

6.For Imagenet run:

python get_imagenet_bias_features.py
python train_imagenet.py

If you find this code useful in your research, please consider citing:

@article{sarridis2023flac,
  title={FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations},
  author={Sarridis, Ioannis and Koutlis, Christos and Papadopoulos, Symeon and Diou, Christos},
  journal={arXiv preprint arXiv:2304.14252},
  year={2023}
}