AIF360 and AI_fairness
These are competitors offering overlapping fairness auditing capabilities, with AIF360 being the mature, production-ready option (comprehensive metrics, mitigation algorithms, active maintenance) while AI_fairness appears to be an educational or experimental resource with minimal adoption.
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 AI_fairness
Ali-Alameer/AI_fairness
This GitHub repository offers resources to create fair and unbiased AI systems, including libraries, tools and tutorials on identifying and mitigating bias in machine learning models and implementing fairness in AI.
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