Open Sourced Bias Testing for Generalized Machine Learning Applications

audit-AI is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. audit-AI was developed by the Data Science team at pymetrics

audit-AI a tool to measure and mitigate the effects discriminatory patterns in training data and the predictions made by machine learning algorithms trained for the purposes of socially sensitive decision processes.

The overall goal of this research is to come up with a reasonable way to think about how to make machine learning algorithms more fair. While identifying potential bias in training datasets and by consequence the machine learning algorithms trained on them is not sufficient to solve the problem of discrimination, in a world where more and more decisions are being automated by Artifical Intelligence, our ability to understand and identify the degree to which an algorithm is fair or biased is a step in the right direction.

Here are a few of the bias testing and algorithm auditing techniques that this library implements.

• 4/5th, fisher, z-test, bayes factor, chi squared
• sim_beta_ratio, classifier_posterior_probabilities

• anova
• 4/5th, fisher, z-test, bayes factor, chi squared
• group proportions at different thresholds

The source code is currently hosted on GitHub: https://github.com/pymetrics/audit-ai

You can install the latest released version with pip.

# pip
pip install audit-AI

If 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

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')

• german-credit
• student-performance