Trusted-AI/AIF360

Health v2 · maintenance only

Adoption not part of the score

2857 stars · 909 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AI Fairness 360 is an open-source Python and R library from IBM Research providing fairness metrics, explanations, and bias mitigation algorithms for datasets and machine learning models. It supports the full AI lifecycle with pre-processing, in-processing, and post-processing debiasing techniques.

Use cases

• detect bias in a machine learning model
• measure fairness metrics on a dataset
• mitigate discrimination in training data
• check disparate impact of model predictions
• apply debiasing algorithms to a classifier
• audit model fairness for regulated domains like finance or healthcare

When to choose

• you need a comprehensive, research-backed toolkit of fairness metrics and mitigation algorithms
• you work in Python or R and want extensible bias testing integrated into your ML pipeline
• you need documented, citable fairness techniques from academic literature

When to avoid

• you need real-time or production-scale low-latency fairness monitoring
• you want a simple one-click solution without understanding fairness metrics
• your project is not Python or R based

Facets

library · maturity active

machine-learning testing data-science machine-learning artificial-intelligence data-science python cross-platform fairness bias-detection bias-mitigation responsible-ai explainability r-package algorithms

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