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README.md

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.

Installation

To install fairkit-learn, run the following command:

pip install fairkit_learn==1.9

Using fairkit-learn

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.

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