GerryFair: Auditing and Learning for Subgroup Fairness
Fairness Gerrymandering: when "a classifier appears to be fair on each individual group, but badly violates the fairness constraint on one or more structured subgroups defined over the protected attributes" (from Kearns et al., https://arxiv.org/abs/1711.05144)
This repository contains python code for:
- learning fair classifiers subject to subgroup fairness constraints (as described in https://arxiv.org/abs/1711.05144)
- auditing standard classifiers from sklearn for fairness violations
- visualizing tradeoffs between error and fairness metrics
- fairness sensitive datasets for experiments (as used in https://arxiv.org/abs/1808.08166)
To install the package and prepare for use, run:
git clone https://github.com/algowatchPenn/GerryFair.git pip install -r requirements.txt
The current iteration of the package uses the following python packages: pandas, numpy, sklearn, matplotlib If you already have these installed, you can forgo the requirements step.
Using our package
For demonstration of the GerryFair API, please see our jupyter notebook
- Maintained by: Seth Neel (email@example.com), William Brown, Adel Boyarsky, Arnab Sarker, Aaron Hallac.
- Property of: Michael Kearns, Seth Neel, Aaron Roth, Z. Steven Wu.
- For questions or concerns, contact Algowatch Project (firstname.lastname@example.org).
- Thank you to the authors of: http://fatml.mysociety.org/media/documents/reductions_approach_to_fair_classification.pdf, whose algorithm/code is in the
fairlearnfolder, and is audited in