fairlearn and fairlib
Fairlearn is a mature, production-ready fairness auditing framework with broad adoption, while Fairlib appears to be an early-stage alternative implementation focusing specifically on classification tasks, making them direct competitors for the same use case of fairness assessment.
About fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
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.
About fairlib
Libr-AI/fairlib
A framework for assessing and improving classification fairness.
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