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Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias (WACV 2022)

This is the code accompanying the paper Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias published in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022.

Usage

Clone the repo:

git clone https://github.com/sumanyumuku98/contextual-bias.git

Create Conda env:

conda env create -f fair.yml

Activate the conda env:

conda activate fair.yml

Dataset

Download the COCO 2017 annotations and create a symlink:

cd data
ln -s /path to annotations/ .

Visualizing Contextually Biased Categories

Generate a barplot for the protected attribute/class w.r.t other co-occuring classes:

python genPlt.py --data_mode train --bias_class man

The above example generates co-occuring distribution for man. The generated barplots will be saved in out_figures.

Curating Contextually Fair Data

To curate contextually fair data for a protected attribute and fixed budget w.r.t to either topK, bottomK and custom co-occuring categories use:

python fairSelection.py --data_mode train --bias_class cup --budget 500 --k 10

The above example curates fair data for protected class cup with a budget of 500 and co-occuring classes being the top 10. The selected ids are saved in out_files.

Citation

If you find the work useful, do cite:

@InProceedings{Agarwal_2022_WACV,
    author    = {Agarwal, Sharat and Muku, Sumanyu and Anand, Saket and Arora, Chetan},
    title     = {Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2022},
    pages     = {3298-3307}
}

Contact

If there are any questions or concerns feel free to send a message at sharata@iiitd.ac.in