IBM AI Fairness 360 (AIF360)
IBM AI Fairness 360 (AIF360) is an open-source Python toolkit that helps detect, measure, and mitigate bias in machine learning datasets and models. Developed by IBM Research, it provides fairness metrics, explainers, and mitigation algorithms to support the development of equitable and transparent AI systems.
Key Features
Fairness Metrics: Supports over 70 metrics to measure bias across datasets and model predictions.
Bias Mitigation Algorithms: Pre-processing, in-processing, and post-processing methods to reduce bias.
Dataset Support: Includes built-in datasets and tools to assess real-world fairness issues.
Visualization & Explainability: Tools to analyze disparities and model behavior across groups.
Flexible Integration: Works with scikit-learn, TensorFlow, and Jupyter Notebooks.
Example Use Cases
Evaluating fairness of credit scoring, hiring, or lending algorithms
Reducing demographic bias in healthcare and public services models
Auditing enterprise ML pipelines for responsible AI governance
Research and education on AI ethics and fairness