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Learning Fair Classifiers with Partially Annotated Group Labels (CVPR 2022)

Official Pytorch implementation of <strong>Learning Fair Classifiers with Partially Annotated Group Labels</strong> | Paper

Sangwon Jung<sup>1</sup> Sanghyuk Chun<sup>2</sup> Taesup Moon<sup>1, 3</sup>

<sup>1</sup><sub>Department of ECE/ASRI, Seoul National University<br> <sup>2</sup><sub>NAVER AI LAB</sub><br> <sup>3</sup><sub>Interdisciplinary Program in Artificial Intelligence, Seoul National University

Recently, fairness-aware learning have become increasingly crucial, but most of those methods operate by assuming the availability of fully annotated demographic group labels. We emphasize that such assumption is unrealistic for real-world applications since group label annotations are expensive and can conflict with privacy issues. In this paper, we consider a more practical scenario, dubbed as Algorithmic Group <strong>Fair</strong>ness with the <strong>P</strong>artially annotated <strong>G</strong>roup labels (<strong>Fair-PG</strong>). We observe that the existing methods to achieve group fairness perform even worse than the vanilla training, which simply uses full data only with target labels, under Fair-PG. To address this problem, we propose a simple <strong>C</strong>onfidence-based <strong>G</strong>roup <strong>L</strong>abel assignment (<strong>CGL</strong>) strategy that is readily applicable to any fairness-aware learning method. CGL utilizes an auxiliary group classifier to assign pseudo group labels, where random labels are assigned to low confident samples. We first theoretically show that our method design is better than the vanilla pseudo-labeling strategy in terms of fairness criteria. Then, we empirically show on several benchmark datasets that by combining CGL and the state-of-the-art fairness-aware in-processing methods, the target accuracies and the fairness metrics can be jointly improved compared to the baselines. Furthermore, we convincingly show that CGL enables to naturally augment the given group-labeled dataset with external target label-only datasets so that both accuracy and fairness can be improved.

Updates

Dataset preparation

  1. Download dataset
  1. Locate downloaded datasets to './data' directory

How to train

We note that CGL first trains a group classifier and a proper threshold using a train set and a validation set splitted from a group-labeled training dataset. And then, the group-unlabeled training samples are annotated with pseudo group labels based on CGL's assignment rules. Finally, we can train a fair model using base fair training methods such as LBC, FairHSIC or MFD.

1. Train a group classifier

$ python main_groupclf.py --model <model_type> --method scratch \
    --dataset <dataset_name> \
    --version groupclf_val \
    --sv <group_label ratio>

In above command, the 'version' can be chosen beween 'groupclf' or 'groupclfval' that indicate whether the group-labeled data is splitted into a train/validation set or not. For CGL, you should choice 'groupclfval' and for the pseudo-label baseline, you should choice 'groupclf'.

2. Find a threshold and save the predictions of group classifier

$ python main_groupclf.py --model <model_type> --method scratch \ 
    --dataset <dataset_name> \
    --mode eval \
    --version groupclf_val \
    --sv <group_label_ratio>

3. Train a fair model using any fair-training methods

# train a scratch model
$ python main.py --model <model_type> --method scratch --dataset dataset_name 
$ python main.py --model <model_type> --method mfd \
    --dataset <dataset_name> \
    --labelwise \
    --version cgl \
    --sv {group_label_ratio} \
    --lamb 100 \
    --teacher-type mlp \
    --teacher-path <your_model_trained_from_scratch> 

$ python main.py --model <model_type> --method fairhsic \
    --dataset <dataset_name> \
    --labelwise  \
    --version cgl \
    --sv {group_label_ratio} \
    --lamb 100  

$ python main.py --model <model_type> --method reweighting \
    --dataset <dataset_name> \
    --iteration <iteration> \
    --version cgl \
    --sv {group_label_ratio} \

How to cite

@inproceedings{jung2021cgl,
    title={Learning Fair Classifiers with Partially Annotated Group Labels}, 
    author={Sangwon Jung and Sanghyuk Chun and Taesup Moon},
    year={2022},
    booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
}

License

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