AIF360 and FairAI
AIF360 provides actionable bias detection and mitigation implementations that practitioners can apply, while FairAI serves as a curated knowledge base of fairness research papers and concepts that informs the theoretical foundation underlying tools like AIF360—making them complements in a learning-to-implementation pipeline.
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.
About FairAI
yongkaiwu/FairAI
This is a collection of papers and other resources related to fairness.
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