Fairness

Aequitas is an open-source bias and fairness audit toolkit for machine learning models, developed by the Center for Data Science and Public Policy at the University of Chicago. Its docs cover installing the Python package, using its CLI, and reading the fairness metrics it generates across subgroups to support equitable decision-making.

Fairlearn is an open-source Python toolkit for assessing and improving fairness in machine learning systems. It pairs an interactive dashboard with unfairness-mitigation algorithms for navigating trade-offs between fairness and model performance.