amiratag / MultiAccuracyBoost

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification

MultiAccuracyBoost

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}
}

Getting Started

Here is the tensorflow implementations of the paper Multiaccuracy: Black-Box Post-Processing for Fairness in Classification presented at NeurIPS 2019.

Prerequisites

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.

Installing

Dowanload LFW+A dataset images and put them in a "./LFWA+/lfw" directory. "dataset_description.pkl" maps each image's name to its attributes.

Authors

• Michael P. Kim - Website
• Amirata Ghorbani - Website
• James Zou - Website

License

This project is licensed under the MIT License - see the LICENSE.md file for details.