Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Please cite the following work if you use this benchmark or the provided tools or implementations:
@inproceedings{kim2019multiaccuracy,
title={Multiaccuracy: Black-box post-processing for fairness in classification},
author={Kim, Michael P and Ghorbani, Amirata and Zou, James},
booktitle={Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},
pages={247--254},
year={2019},
organization={ACM}
}
Here is the tensorflow implementations of the paper Multiaccuracy: Black-Box Post-Processing for Fairness in Classification presented at NeurIPS 2019.
Required python libraries:
Scikit-learn: https://scikit-learn.org/stable/
Tensorflow: https://www.tensorflow.org/
Facenet: https://github.com/davidsandberg/facenet
Also the LFW+A dataset images.
Dowanload LFW+A dataset images and put them in a "./LFWA+/lfw" directory. "dataset_description.pkl" maps each image's name to its attributes.
This project is licensed under the MIT License - see the LICENSE.md file for details.