justicia 0.0.5

This library can be used formally verify machine learning models on multiple fairness definitions.

Justicia

This is the implementation of our AAAI 2021 and 2022 papers where we have proposed a formal approach to verify the fairness of machine learning classifiers.

Documentation

Python tutorials are available in doc.

Install

• Install python dependencies (prerequisite) pip install -r requirements.txt
• Install the python library pip install justicia

Other dependencies

• PGMPY

• SSAT solver. Checkout to the compatible version.

  git clone https://github.com/NTU-ALComLab/ssatABC.git
  cd ssatABC
  git checkout 91a93a57c08812e3fe24aabd71219b744d2355ad
  

• PySAT

Citations

Please cite following papers.

@inproceedings{ghosh2022algorithmic,
author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.},
title={Algorithmic Fairness Verification with Graphical Models},
booktitle={Proceedings of AAAI},
month={2},
year={2022},
}

@inproceedings{ghosh2021justicia,
author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.},
title={Justicia: A Stochastic {SAT} Approach to Formally Verify Fairness},
booktitle={Proceedings of AAAI},
month={2},
year={2021},
}

Contact

Bishwamittra Ghosh ([e-mail])

Issues, questions, bugs, etc.

Please click on "issues" at the top and create a new issue. All issues are responded to promptly.