Reductions for Fair Machine Learning
A Python package that implements the black-box approach to fair classification described in the paper A Reductions Approach to Fair Classification.
The package can be installed via
pip install fairlearn. To verify that it works, download
test_fairlearn.py from the repository and run
Instead of installing the package, you can clone the repository locally via
git clone firstname.lastname@example.org:Microsoft/fairlearn.git. To verify that the package works, run
python test_fairlearn.py in the root of the repository.
expgrad in the module
fairlearn.classred implements the reduction of fair classification to weighted binary classification. Any learner that supports weighted binary classification can be provided as input for this reduction. Two common fairness definitions are provided in the module
fairlearn.moments: demographic parity (class
DP) and equalized odds (class
EO). See the file
test_fairlearn.py for example usage of
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
fairlearn is maintained by:
If you are the current maintainer of this project:
- Create a branch for the release:
git checkout -b release-vxx.xx
- Ensure that all tests return "ok":
- Bump the module version in
- Make a pull request to Microsoft/fairlearn
- Merge Microsoft/fairlearn pull request
- Tag and push:
git tag vxx.xx; git push --tags
Reporting Security Issues
Security issues and bugs should be reported privately, via email, to the Microsoft Security Response Center (MSRC) at email@example.com. You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Further information, including the MSRC PGP key, can be found in the Security TechCenter.