fairlearn and AIF360
These are complementary tools that can be used together, as fairlearn focuses on fairness assessment and mitigation through constraints-based optimization, while AIF360 provides a broader toolkit of bias metrics, explainability for those metrics, and diverse mitigation algorithms that address different fairness definitions and use cases.
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 AIF360
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Provides pre- and in-processing debiasing algorithms (reweighting, disparate impact removal, adversarial debiasing) alongside 20+ fairness metrics spanning group fairness, individual fairness, and sample distortion measures. Available in both Python and R with modular dependencies, allowing users to install only required algorithm backends (TensorFlow for adversarial debiasing, CVXPY for optimization-based methods). Extensible architecture designed for research-to-practice translation across finance, HR, healthcare, and education domains.
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