The Bias and Fairness Audit Toolkit for Machine Learning
Aequitas is an open-source bias audit toolkit for machine learning developers, analysts, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive risk-assessment tools.
Sample Jupyter Notebook
Explore bias analysis of the COMPAS data using the Aequitas library.
Find documentation here.
Aequitas requires Python 3.
Install this Python library from source:
python3 setup.py install
...or named as an installation requirement, e.g. via
pip3 install git+https://github.com/dssg/aequitas.git
You may then import the
aequitas module from Python:
...or execute the auditor from the command line:
Provision your development environment via
Common development tasks, such as deploying the webapp, may then be handled via
To contact the team, please email us at [aequitas at uchicago dot edu]
© 2018 Center for Data Science and Public Policy - University of Chicago