A machine learning fairness toolkit
fairkit-learn fairness toolkit
Fairkit-learn is an open-source, publicly available Python toolkit designed to help data scientists evaluate and explore machine learning models with respect to quality and fairness metrics simultaneously.
Fairkit-learn builds on top of scikit-learn, the state-of-the-art tool suite for data mining and data analysis, and AI Fairness 360, the state-of-the-art Python toolkit for examining, reporting, and mitigating machine learning bias in individual models.
Fairkit-learn supports all metrics and learning algorithms available in scikit-learn and AI Fairness 360, and all of the bias mitigating pre- and post-processing algorithms available in AI Fairness 360, and provides extension points to add more metrics and algorithms.
To install fairkit-learn, run the following command:
pip install fairkit-learn==1.0
Sample code for how to use fairkit-learn can be found in the examples folder in the repo.
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