This open-sourced bias testing python library ‘audit AI’ can be used for measuring and mitigating the effects of discriminatory patterns in training data and the predictions made by machine learning algorithms. This tool is built over pandas and sklearn by pymetrics.
Github Link: https://github.com/pymetrics/audit-ai
Related Paper: https://arxiv.org/pdf/1906.09208.pdf
Installation/Process
You can install the latest released version with pip
# pip
pip install audit-AIIf you install with pip, you’ll need to install scikit-learn, numpy, and pandas with either pip or conda. Version requirements:
- numpy
- scipy
- pandas
For vizualization:
- matplotlib
- seaborn
How to use this package: (Copied from https://github.com/pymetrics/audit-ai )
from auditai.misc import bias_test_check
X = df.loc[:,features]
y_pred = clf.predict_proba(X)
# test for bias
bias_test_check(labels=df['gender'], results=y_pred, category='Gender')
>>> *Gender passes 4/5 test, Fisher p-value, Chi-Squared p-value, z-test p-value and Bayes Factor at 50.00*To get a plot of the different tests at different thresholds:
from auditai.viz import plot_threshold_tests
X = df.loc[:,features]
y_pred = clf.predict_proba(X)
# test for bias
plot_threshold_tests(labels=df['gender'], results=y_pred, category='Gender')
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