fairlearn/fairlearn

Provides dual assessment and mitigation tools: metrics for identifying which demographic groups experience allocation or quality-of-service harms, and algorithms for reducing unfairness across multiple fairness definitions. Implements group fairness constraints that enforce comparable model behavior across specified demographic groups, enabling data scientists to quantify fairness trade-offs against accuracy. Integrates with standard ML workflows through scikit-learn-compatible APIs and includes Jupyter notebooks demonstrating real-world applications in hiring, lending, and admissions scenarios.

2,213 stars and 170,696 monthly downloads. Used by 9 other packages. Actively maintained with 2 commits in the last 30 days. Available on PyPI.