Fairlearn

Fairlearn is an open-source Python toolkit developed by Microsoft Research for assessing and improving the fairness of machine learning models. Originally created as an internal Microsoft project, Fairlearn was open-sourced in 2020 and has since grown into a community-driven project with contributions from researchers and practitioners worldwide. It is now one of the most popular and accessible fairness assessment tools in the Python data science ecosystem. The toolkit is organized around two core capabilities: fairness assessment and bias mitigation. The assessment module provides a rich set of fairness metrics that can evaluate model performance across different demographic groups defined by sensitive features such as race, gender, age, or disability status. Fairlearn's MetricFrame class makes it straightforward to disaggregate any standard ML metric by group, enabling practitioners to quickly identify performance disparities that may indicate unfair outcomes. For mitigation, Fairlearn provides several algorithms including Exponentiated Gradient, Grid Search, and Threshold Optimizer. These algorithms work by applying fairness constraints during or after model training, allowing practitioners to find models that balance predictive accuracy with fairness objectives. The Exponentiated Gradient algorithm is particularly powerful, as it can enforce a wide variety of fairness constraints including demographic parity, equalized odds, and bounded group loss. A major strength of Fairlearn is its seamless integration with the Python data science stack. The library works natively with scikit-learn estimators and follows familiar fit/predict/transform patterns, making it easy for data scientists to incorporate fairness assessment into their existing workflows. Fairlearn also provides interactive visualizations through its Fairlearn Dashboard, which can be used in Jupyter notebooks to explore fairness metrics interactively. Fairlearn benefits from excellent documentation, including detailed user guides, API references, case studies, and example notebooks that cover common fairness assessment scenarios. Microsoft's backing ensures ongoing maintenance and development, though the project operates as a genuine open-source community effort with its own governance structure. The toolkit is licensed under MIT, making it permissive for both commercial and non-commercial use.