Fairkit-learn: A Python Model Fairness Evaluation 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==1.9
To use fairkit-learn, first run the following command to install necessary pacakges:
pip install -r requirements.txt
Sample code for how to use fairkit-learn can be found in the examples folder (e.g., Fairkit_learn_Tutorial.ipynb) in the repo.