AIF360 and responsibly
AIF360 is a mature, production-ready fairness auditing framework that ResponsiblyAI complements by offering specialized bias detection and mitigation techniques, making them best used together rather than as direct alternatives.
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 responsibly
ResponsiblyAI/responsibly
Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰
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